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1996 ncaa women 's division i basketball tournament

https://en.wikipedia.org/wiki/1996_NCAA_Women%27s_Division_I_Basketball_Tournament

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16679797-4.html.csv

count

during the 1996 ncaa women 's division i basketball tournament , in the east region , two times the game was held in virginia .

{'scope': 'subset', 'criterion': 'equal', 'value': 'virginia', 'result': '2', 'col': '5', 'subset': {'col': '1', 'criterion': 'equal', 'value': 'east'}}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'region', 'east'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; region ; east }', 'tointer': 'select the rows whose region record fuzzily matches to east .'}, 'state', 'virginia'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose region record fuzzily matches to east . among these rows , select the rows whose state record fuzzily matches to virginia .', 'tostr': 'filter_eq { filter_eq { all_rows ; region ; east } ; state ; virginia }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; region ; east } ; state ; virginia } }', 'tointer': 'select the rows whose region record fuzzily matches to east . among these rows , select the rows whose state record fuzzily matches to virginia . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; region ; east } ; state ; virginia } } ; 2 } = true', 'tointer': 'select the rows whose region record fuzzily matches to east . among these rows , select the rows whose state record fuzzily matches to virginia . the number of such rows is 2 .'}

eq { count { filter_eq { filter_eq { all_rows ; region ; east } ; state ; virginia } } ; 2 } = true

select the rows whose region record fuzzily matches to east . among these rows , select the rows whose state record fuzzily matches to virginia . the number of such rows is 2 .

4

4

{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'region_6': 6, 'east_7': 7, 'state_8': 8, 'virginia_9': 9, '2_10': 10}

{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'region_6': 'region', 'east_7': 'east', 'state_8': 'state', 'virginia_9': 'virginia', '2_10': '2'}

{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'region_6': [0], 'east_7': [0], 'state_8': [1], 'virginia_9': [1], '2_10': [3]}

['region', 'host', 'venue', 'city', 'state']

[['east', 'old dominion university', 'old dominion university fieldhouse', 'norfolk', 'virginia'], ['east', 'university of virginia', 'university hall ( university of virginia )', 'charlottesville', 'virginia'], ['east', 'university of tennessee', 'thompson - boling arena', 'knoxville', 'tennessee'], ['east', 'university of kansas', 'allen field house', 'lawrence', 'kansas'], ['mideast', 'university of iowa', 'carverhawkeye arena', 'iowa city', 'indiana'], ['mideast', 'university of connecticut', 'harry a gampel pavilion', 'storrs', 'connecticut'], ['mideast', 'vanderbilt university', 'memorial gymnasium ( vanderbilt university )', 'nashville', 'tennessee'], ['mideast', 'duke university', 'cameron indoor stadium', 'durham', 'north carolina'], ['midwest', 'university of georgia', 'georgia coliseum ( stegeman coliseum )', 'athens', 'georgia'], ['midwest', 'louisiana tech university', 'thomas assembly center', 'ruston', 'louisiana'], ['midwest', 'clemson university', 'littlejohn coliseum', 'clemson', 'south carolina'], ['midwest', 'texas tech university', 'lubbock municipal coliseum', 'lubbock', 'texas'], ['west', 'pennsylvania state university', 'recreation building ( rec hall )', 'university park', 'pennsylvania'], ['west', 'university of colorado', 'cu events center ( coors events center )', 'boulder', 'colorado'], ['west', 'stanford university', 'maples pavilion', 'stanford', 'california'], ['west', 'university of alabama', 'coleman coliseum', 'tuscaloosa', 'alabama']]

1992 - 93 toronto maple leafs season

https://en.wikipedia.org/wiki/1992%E2%80%9393_Toronto_Maple_Leafs_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13913477-5.html.csv

count

in the 1992 - 93 toronto maple leafs season , among the games where toronto was a home team , 2 of them had total points of 28 .

{'scope': 'subset', 'criterion': 'equal', 'value': '28', 'result': '2', 'col': '7', 'subset': {'col': '3', 'criterion': 'not_equal', 'value': 'toronto'}}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_str_not_eq', 'args': ['all_rows', 'visitor', 'toronto'], 'result': None, 'ind': 0, 'tostr': 'filter_not_eq { all_rows ; visitor ; toronto }', 'tointer': 'select the rows whose visitor record does not match to toronto .'}, 'points', '28'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose visitor record does not match to toronto . among these rows , select the rows whose points record is equal to 28 .', 'tostr': 'filter_eq { filter_not_eq { all_rows ; visitor ; toronto } ; points ; 28 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_not_eq { all_rows ; visitor ; toronto } ; points ; 28 } }', 'tointer': 'select the rows whose visitor record does not match to toronto . among these rows , select the rows whose points record is equal to 28 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_not_eq { all_rows ; visitor ; toronto } ; points ; 28 } } ; 2 } = true', 'tointer': 'select the rows whose visitor record does not match to toronto . among these rows , select the rows whose points record is equal to 28 . the number of such rows is 2 .'}

eq { count { filter_eq { filter_not_eq { all_rows ; visitor ; toronto } ; points ; 28 } } ; 2 } = true

select the rows whose visitor record does not match to toronto . among these rows , select the rows whose points record is equal to 28 . the number of such rows is 2 .

4

4

{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_eq_1': 1, 'filter_str_not_eq_0': 0, 'all_rows_5': 5, 'visitor_6': 6, 'toronto_7': 7, 'points_8': 8, '28_9': 9, '2_10': 10}

{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_eq_1': 'filter_eq', 'filter_str_not_eq_0': 'filter_str_not_eq', 'all_rows_5': 'all_rows', 'visitor_6': 'visitor', 'toronto_7': 'toronto', 'points_8': 'points', '28_9': '28', '2_10': '2'}

{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_eq_1': [2], 'filter_str_not_eq_0': [1], 'all_rows_5': [0], 'visitor_6': [0], 'toronto_7': [0], 'points_8': [1], '28_9': [1], '2_10': [3]}

['game', 'date', 'visitor', 'score', 'home', 'record', 'points']

[['24', 'december 1', 'toronto', '3 - 8', 'new jersey', '11 - 10 - 3', '25'], ['25', 'december 3', 'toronto', '3 - 4', 'chicago', '11 - 11 - 3', '25'], ['26', 'december 5', 'chicago', '2 - 2', 'toronto', '11 - 11 - 4', '26'], ['27', 'december 6', 'toronto', '0 - 6', 'ny rangers', '11 - 12 - 4', '26'], ['28', 'december 9', 'detroit', '5 - 3', 'toronto', '12 - 12 - 4', '28'], ['29', 'december 11', 'calgary', '3 - 6', 'toronto', '12 - 13 - 4', '28'], ['30', 'december 15', 'toronto', '5 - 6', 'minnesota', '12 - 14 - 4', '28'], ['31', 'december 19', 'ottawa', '5 - 1', 'toronto', '13 - 14 - 4', '30'], ['32', 'december 20', 'toronto', '4 - 5', 'buffalo', '13 - 15 - 4', '30'], ['33', 'december 22', 'toronto', '4 - 4', 'detroit', '13 - 15 - 5', '31'], ['34', 'december 26', 'detroit', '1 - 5', 'toronto', '13 - 16 - 5', '31'], ['35', 'december 27', 'toronto', '6 - 3', 'st louis', '14 - 16 - 5', '33'], ['36', 'december 29', 'toronto', '3 - 2', 'ny islanders', '15 - 16 - 5', '35'], ['37', 'december 31', 'toronto', '3 - 3', 'pittsburgh', '15 - 16 - 6', '36']]

1962 oakland raiders season

https://en.wikipedia.org/wiki/1962_Oakland_Raiders_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12676700-1.html.csv

unique

the oakland raiders game the took place on september 9 , 1962 is the onlt game during the 1962 season that resulted in a score of 28 - 17 .

{'scope': 'all', 'row': '1', 'col': '4', 'col_other': '2', 'criterion': 'equal', 'value': '28 - 17', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', '28 - 17'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to 28 - 17 .', 'tostr': 'filter_eq { all_rows ; result ; 28 - 17 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; 28 - 17 } }', 'tointer': 'select the rows whose result record fuzzily matches to 28 - 17 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', '28 - 17'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to 28 - 17 .', 'tostr': 'filter_eq { all_rows ; result ; 28 - 17 }'}, 'date'], 'result': 'september 9 , 1962', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; 28 - 17 } ; date }'}, 'september 9 , 1962'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; 28 - 17 } ; date } ; september 9 , 1962 }', 'tointer': 'the date record of this unqiue row is september 9 , 1962 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; result ; 28 - 17 } } ; eq { hop { filter_eq { all_rows ; result ; 28 - 17 } ; date } ; september 9 , 1962 } } = true', 'tointer': 'select the rows whose result record fuzzily matches to 28 - 17 . there is only one such row in the table . the date record of this unqiue row is september 9 , 1962 .'}

and { only { filter_eq { all_rows ; result ; 28 - 17 } } ; eq { hop { filter_eq { all_rows ; result ; 28 - 17 } ; date } ; september 9 , 1962 } } = true

select the rows whose result record fuzzily matches to 28 - 17 . there is only one such row in the table . the date record of this unqiue row is september 9 , 1962 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'result_7': 7, '28 - 17_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, 'september 9 , 1962_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'result_7': 'result', '28 - 17_8': '28 - 17', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', 'september 9 , 1962_10': 'september 9 , 1962'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'result_7': [0], '28 - 17_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], 'september 9 , 1962_10': [3]}

['week', 'date', 'opponent', 'result', 'attendance']

[['1', 'september 9 , 1962', 'new york titans', 'l 28 - 17', '12893'], ['2', 'september 23 , 1962', 'dallas texans', 'l 26 - 16', '12500'], ['3', 'september 30 , 1962', 'san diego chargers', 'l 42 - 33', '13000'], ['4', 'october 5 , 1962', 'denver broncos', 'l 44 - 7', '22452'], ['5', 'october 14 , 1962', 'denver broncos', 'l 23 - 6', '7000'], ['6', 'october 20 , 1962', 'buffalo bills', 'l 14 - 6', '21037'], ['7', 'october 26 , 1962', 'boston patriots', 'l 26 - 16', '12514'], ['8', 'november 4 , 1962', 'new york titans', 'l 31 - 21', '18247'], ['9', 'november 11 , 1962', 'houston oilers', 'l 28 - 20', '11000'], ['10', 'november 18 , 1962', 'buffalo bills', 'l 10 - 6', '12500'], ['11', 'november 25 , 1962', 'dallas texans', 'l 35 - 7', '13557'], ['12', 'december 2 , 1962', 'san diego chargers', 'l 31 - 21', '17874'], ['13', 'december 9 , 1962', 'houston oilers', 'l 32 - 17', '27400'], ['14', 'december 16 , 1962', 'boston patriots', 'w 20 - 0', '8000']]

badminton at the pan american games

https://en.wikipedia.org/wiki/Badminton_at_the_Pan_American_Games

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10371133-1.html.csv

superlative

canada won more gold medals in badminton at the pan american games than any other nation .

{'scope': 'all', 'col_superlative': '3', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'gold'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; gold }'}, 'nation'], 'result': 'canada ( can )', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; gold } ; nation }'}, 'canada ( can )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; gold } ; nation } ; canada ( can ) } = true', 'tointer': 'select the row whose gold record of all rows is maximum . the nation record of this row is canada ( can ) .'}

eq { hop { argmax { all_rows ; gold } ; nation } ; canada ( can ) } = true

select the row whose gold record of all rows is maximum . the nation record of this row is canada ( can ) .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'gold_5': 5, 'nation_6': 6, 'canada (can)_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'gold_5': 'gold', 'nation_6': 'nation', 'canada (can)_7': 'canada ( can )'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'gold_5': [0], 'nation_6': [1], 'canada (can)_7': [2]}

['rank', 'nation', 'gold', 'silver', 'bronze', 'total']

[['1', 'canada ( can )', '16', '16', '11', '43'], ['2', 'united states ( usa )', '7', '6', '12', '25'], ['3', 'guatemala ( gua )', '1', '2', '3', '6'], ['4', 'jamaica ( jam )', '1', '0', '5', '6'], ['5', 'cuba ( cub )', '0', '1', '0', '1'], ['6', 'peru ( per )', '0', '0', '14', '14'], ['7', 'mexico ( mex )', '0', '0', '3', '3'], ['8', 'brazil ( bra )', '0', '0', '2', '2'], ['total', 'total', '25', '25', '50', '100']]

lard

https://en.wikipedia.org/wiki/Lard

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18621997-1.html.csv

superlative

suet has the highest level of saturated fat , at 55 % .

{'scope': 'all', 'col_superlative': '3', 'row_superlative': '10', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '1', 'subset': None}

{'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'saturated fat'], 'result': '52 g ( 55 % )', 'ind': 0, 'tostr': 'max { all_rows ; saturated fat }', 'tointer': 'the maximum saturated fat record of all rows is 52 g ( 55 % ) .'}, '52 g ( 55 % )'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; saturated fat } ; 52 g ( 55 % ) }', 'tointer': 'the maximum saturated fat record of all rows is 52 g ( 55 % ) .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'saturated fat'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; saturated fat }'}, ''], 'result': 'suet', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; saturated fat } ; }'}, 'suet'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; saturated fat } ; } ; suet }', 'tointer': 'the record of the row with superlative saturated fat record is suet .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { max { all_rows ; saturated fat } ; 52 g ( 55 % ) } ; eq { hop { argmax { all_rows ; saturated fat } ; } ; suet } } = true', 'tointer': 'the maximum saturated fat record of all rows is 52 g ( 55 % ) . the record of the row with superlative saturated fat record is suet .'}

and { eq { max { all_rows ; saturated fat } ; 52 g ( 55 % ) } ; eq { hop { argmax { all_rows ; saturated fat } ; } ; suet } } = true

the maximum saturated fat record of all rows is 52 g ( 55 % ) . the record of the row with superlative saturated fat record is suet .

6

6

{'and_5': 5, 'result_6': 6, 'eq_1': 1, 'max_0': 0, 'all_rows_7': 7, 'saturated fat_8': 8, '52 g (55%)_9': 9, 'str_eq_4': 4, 'str_hop_3': 3, 'argmax_2': 2, 'all_rows_10': 10, 'saturated fat_11': 11, '_12': 12, 'suet_13': 13}

{'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'max_0': 'max', 'all_rows_7': 'all_rows', 'saturated fat_8': 'saturated fat', '52 g (55%)_9': '52 g ( 55 % )', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'argmax_2': 'argmax', 'all_rows_10': 'all_rows', 'saturated fat_11': 'saturated fat', '_12': '', 'suet_13': 'suet'}

{'and_5': [6], 'result_6': [], 'eq_1': [5], 'max_0': [1], 'all_rows_7': [0], 'saturated fat_8': [0], '52 g (55%)_9': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'argmax_2': [3], 'all_rows_10': [2], 'saturated fat_11': [2], '_12': [3], 'suet_13': [4]}

['', 'total fat', 'saturated fat', 'monounsaturated fat', 'polyunsaturated fat', 'smoke point']

[['sunflower oil', '100 g', '11 g', '20 g ( 84 g in high oleic variety )', '69 g ( 4 g in high oleic variety )', 'degree'], ['soybean oil', '100 g', '16 g', '23 g', '58 g', 'degree'], ['canola oil', '100 g', '7 g', '63 g', '28 g', 'degree'], ['olive oil', '100 g', '14 g', '73 g', '11 g', 'degree'], ['corn oil', '100 g', '15 g', '30 g', '55 g', 'degree'], ['peanut oil', '100 g', '17 g', '46 g', '32 g', 'degree'], ['rice bran oil', '100 g', '25 g', '38 g', '37 g', 'degree'], ['vegetable shortening ( hydrogenated )', '71 g', '23 g ( 34 % )', '8 g ( 11 % )', '37 g ( 52 % )', 'degree'], ['lard', '100 g', '39 g', '45 g', '11 g', 'degree'], ['suet', '94 g', '52 g ( 55 % )', '32 g ( 34 % )', '3 g ( 3 % )', '200degree ( 400degree )']]

galicia , spain

https://en.wikipedia.org/wiki/Galicia%2C_Spain

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12837-1.html.csv

aggregation

the average number of days with frost in galicia , spain , is 15.67 .

{'scope': 'all', 'col': '5', 'type': 'average', 'result': '15.67', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'days with frost'], 'result': '15.67', 'ind': 0, 'tostr': 'avg { all_rows ; days with frost }'}, '15.67'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; days with frost } ; 15.67 } = true', 'tointer': 'the average of the days with frost record of all rows is 15.67 .'}

round_eq { avg { all_rows ; days with frost } ; 15.67 } = true

the average of the days with frost record of all rows is 15.67 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'days with frost_4': 4, '15.67_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'days with frost_4': 'days with frost', '15.67_5': '15.67'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'days with frost_4': [0], '15.67_5': [1]}

['city / town', 'july av t', 'rain', 'days with rain ( year / summer )', 'days with frost', 'sunlight hours']

[['santiago de compostela', 'degree', 'mm ( in )', '141 / 19', '15', '1998'], ['a coruña', 'degree', 'mm ( in )', '131 / 19', '0', '1966'], ['lugo', 'degree', 'mm ( in )', '131 / 18', '42', '1821'], ['vigo', 'degree', 'mm ( in )', '130 / 18', '5', '2212'], ['ourense', 'degree', 'mm ( in )', '97 / 12', '30', '2043'], ['pontevedra', 'degree', 'mm ( in )', '133 / 18', '2', '2223']]

2000 u.s. open ( golf )

https://en.wikipedia.org/wiki/2000_U.S._Open_%28golf%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17128242-7.html.csv

count

11 players participated in the 2000 u.s. open ( golf ) tournament .

{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '11', 'col': '1', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'place'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose place record is arbitrary .', 'tostr': 'filter_all { all_rows ; place }'}], 'result': '11', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; place } }', 'tointer': 'select the rows whose place record is arbitrary . the number of such rows is 11 .'}, '11'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; place } } ; 11 } = true', 'tointer': 'select the rows whose place record is arbitrary . the number of such rows is 11 .'}

eq { count { filter_all { all_rows ; place } } ; 11 } = true

select the rows whose place record is arbitrary . the number of such rows is 11 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'place_5': 5, '11_6': 6}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'place_5': 'place', '11_6': '11'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'place_5': [0], '11_6': [2]}

['place', 'player', 'country', 'score', 'to par', 'money']

[['1', 'tiger woods', 'united states', '65 + 69 + 71 + 67 = 272', '- 12', '800000'], ['t2', 'miguel ángel jiménez', 'spain', '66 + 74 + 76 + 71 = 287', '+ 3', '391150'], ['t2', 'ernie els', 'south africa', '74 + 73 + 68 + 72 = 287', '+ 3', '391150'], ['4', 'john huston', 'united states', '67 + 75 + 76 + 70 = 288', '+ 4', '212779'], ['t5', 'lee westwood', 'england', '71 + 71 + 76 + 71 = 289', '+ 5', '162526'], ['t5', 'pádraig harrington', 'ireland', '73 + 71 + 72 + 73 = 289', '+ 5', '162526'], ['7', 'nick faldo', 'england', '69 + 74 + 76 + 71 = 290', '+ 6', '137203'], ['t8', 'vijay singh', 'fiji', '70 + 73 + 80 + 68 = 291', '+ 7', '112766'], ['t8', 'stewart cink', 'united states', '77 + 72 + 72 + 70 = 291', '+ 7', '112766'], ['t8', 'david duval', 'united states', '75 + 71 + 74 + 71 = 291', '+ 7', '112766'], ['t8', 'loren roberts', 'united states', '68 + 78 + 73 + 72 = 291', '+ 7', '112766']]

catch me if you can ( musical )

https://en.wikipedia.org/wiki/Catch_Me_If_You_Can_%28musical%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14779860-1.html.csv

majority

most of the awards catch me if you can was nominated for were drama desk awards .

{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'drama desk award', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'award ceremony', 'drama desk award'], 'result': True, 'ind': 0, 'tointer': 'for the award ceremony records of all rows , most of them fuzzily match to drama desk award .', 'tostr': 'most_eq { all_rows ; award ceremony ; drama desk award } = true'}

most_eq { all_rows ; award ceremony ; drama desk award } = true

for the award ceremony records of all rows , most of them fuzzily match to drama desk award .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'award ceremony_3': 3, 'drama desk award_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'award ceremony_3': 'award ceremony', 'drama desk award_4': 'drama desk award'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'award ceremony_3': [0], 'drama desk award_4': [0]}

['year', 'award ceremony', 'category', 'nominee', 'result']

[['2011', 'tony award', 'best musical', 'best musical', 'nominated'], ['2011', 'tony award', 'best performance by a leading actor in a musical', 'norbert leo butz', 'won'], ['2011', 'tony award', 'best sound design', 'steve canyon kennedy', 'nominated'], ['2011', 'tony award', 'best orchestrations', 'marc shaiman and larry blank', 'nominated'], ['2011', 'drama desk award', 'outstanding actor in a musical', 'norbert leo butz', 'won'], ['2011', 'drama desk award', 'outstanding featured actor in a musical', 'tom wopat', 'nominated'], ['2011', 'drama desk award', 'outstanding featured actress in a musical', 'kerry butler', 'nominated'], ['2011', 'drama desk award', 'outstanding music', 'marc shaiman', 'nominated'], ['2011', 'drama desk award', 'outstanding lyrics', 'scott wittman and marc shaiman', 'nominated'], ['2011', 'drama desk award', 'outstanding orchestrations', 'marc shaiman and larry blank', 'nominated']]

largest gold companies

https://en.wikipedia.org/wiki/Largest_gold_companies

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24307126-3.html.csv

count

the united states has two of the largest gold companies .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'united states', 'result': '2', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'base', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose base record fuzzily matches to united states .', 'tostr': 'filter_eq { all_rows ; base ; united states }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; base ; united states } }', 'tointer': 'select the rows whose base record fuzzily matches to united states . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; base ; united states } } ; 2 } = true', 'tointer': 'select the rows whose base record fuzzily matches to united states . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; base ; united states } } ; 2 } = true

select the rows whose base record fuzzily matches to united states . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'base_5': 5, 'united states_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'base_5': 'base', 'united states_6': 'united states', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'base_5': [0], 'united states_6': [0], '2_7': [2]}

['april 2013 cum rank', 'name', 'rank 2012', 'rank 2013', 'base', '2013 rev ( bil usd )', '2013 profit ( mil usd )', 'assets 2013 ( bil )', 'market cap march 15 ( mil )']

[['1', 'freeport - mcmoran', '235', '273', 'united states', '18.0', '3000', '35.4', '23100'], ['2', 'newmont mining', '639', '448', 'united states', '9.9', '1800', '29.6', '19700'], ['3', 'goldcorp', '507', '559', 'canada', '5.4', '1700', '31.2', '26400'], ['4', 'barrick gold', '225', '659', 'canada', '14.5', '( 700 )', '47.3', '28700'], ['5', 'newcrest mining', '735', '744', 'australia', '4.5', '1100', '20.8', '17500'], ['6', 'anglogold ashanti', '794', '936', 'south africa', '6.1', '800', '12.6', '9500'], ['7', 'yamana gold', '1219', '1279', 'canada', '2.3', '400', '11.8', '11600'], ['8', 'polyus gold', '1810', '1293', 'russia', '2.8', '900', '5.6', '9800'], ['9', 'gold fields', '968', '1435', 'south africa', '3.4', '700', '11.2', '5900'], ['10', 'kinross gold', '1384', '1551', 'canada', '4.3', '( 2500 )', '14.9', '9100'], ['11', 'buenaventura', '1276', '1601', 'peru', '1.5', '700', '4.5', '6300'], ['12', 'shandong gold - mining', '1980', '1613', 'china', '6.3', '300', '2.0', '7600']]

1984 washington redskins season

https://en.wikipedia.org/wiki/1984_Washington_Redskins_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15085579-1.html.csv

ordinal

the washington redskins game played on december 9 , 1984 had the second highest attendance .

{'row': '15', 'col': '5', 'order': '2', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'attendance', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; attendance ; 2 }'}, 'date'], 'result': 'december 9 , 1984', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; attendance ; 2 } ; date }'}, 'december 9 , 1984'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; december 9 , 1984 } = true', 'tointer': 'select the row whose attendance record of all rows is 2nd maximum . the date record of this row is december 9 , 1984 .'}

eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; december 9 , 1984 } = true

select the row whose attendance record of all rows is 2nd maximum . the date record of this row is december 9 , 1984 .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '2_6': 6, 'date_7': 7, 'december 9 , 1984_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', '2_6': '2', 'date_7': 'date', 'december 9 , 1984_8': 'december 9 , 1984'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '2_6': [0], 'date_7': [1], 'december 9 , 1984_8': [2]}

['week', 'date', 'opponent', 'result', 'attendance']

[['1', 'september 2 , 1984', 'miami dolphins', 'l , 17 - 35', '52683'], ['2', 'september 10 , 1984', 'san francisco 49ers', 'l , 31 - 37', '59707'], ['3', 'september 16 , 1984', 'new york giants', 'w , 30 - 14', '52997'], ['4', 'september 23 , 1984', 'new england patriots', 'w , 26 - 10', '60503'], ['5', 'september 30 , 1984', 'philadelphia eagles', 'w , 20 - 0', '53064'], ['6', 'october 7 , 1984', 'indianapolis colts', 'w , 35 - 7', '60012'], ['7', 'october 14 , 1984', 'dallas cowboys', 'w , 34 - 14', '55431'], ['8', 'october 21 , 1984', 'st louis cardinals', 'l , 24 - 26', '50262'], ['9', 'october 28 , 1984', 'new york giants', 'l , 13 - 37', '76192'], ['10', 'november 5 , 1984', 'atlanta falcons', 'w , 14 - 27', '51301'], ['11', 'november 11 , 1984', 'detroit lions', 'w , 28 - 14', '50212'], ['12', 'november 18 , 1984', 'philadelphia eagles', 'l , 10 - 16', '63117'], ['13', 'november 25 , 1984', 'buffalo bills', 'w , 41 - 14', '51513'], ['14', 'november 29 , 1984', 'minnesota vikings', 'w , 31 - 17', '55017'], ['15', 'december 9 , 1984', 'dallas cowboys', 'w , 30 - 28', '64286'], ['16', 'december 16 , 1984', 'st louis cardinals', 'w , 29 - 27', '54299']]

rustavi 2

https://en.wikipedia.org/wiki/Rustavi_2

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1544974-1.html.csv

majority

majority of rustavi 2 series that have present finale can be watched monday to friday .

{'scope': 'subset', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'monday to friday', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'present'}}

{'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'series finale', 'present'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; series finale ; present }', 'tointer': 'select the rows whose series finale record fuzzily matches to present .'}, 'weekly schedule', 'monday to friday'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose series finale record fuzzily matches to present . for the weekly schedule records of these rows , most of them fuzzily match to monday to friday .', 'tostr': 'most_eq { filter_eq { all_rows ; series finale ; present } ; weekly schedule ; monday to friday } = true'}

most_eq { filter_eq { all_rows ; series finale ; present } ; weekly schedule ; monday to friday } = true

select the rows whose series finale record fuzzily matches to present . for the weekly schedule records of these rows , most of them fuzzily match to monday to friday .

2

2

{'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'series finale_4': 4, 'present_5': 5, 'weekly schedule_6': 6, 'monday to friday_7': 7}

{'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'series finale_4': 'series finale', 'present_5': 'present', 'weekly schedule_6': 'weekly schedule', 'monday to friday_7': 'monday to friday'}

{'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'series finale_4': [0], 'present_5': [0], 'weekly schedule_6': [1], 'monday to friday_7': [1]}

['country', 'telenovela', 'translation', 'series premiere', 'series finale', 'weekly schedule', 'timeslot']

[['mexico', 'mentir para vivir', 'ოჰ ეს ცრემლები / პარალელური საიდუმლო', 'september 2 , 2013', 'present', 'monday to friday', '10:10'], ['mexico', 'corazon indomable', 'კატური სულის საიდუმლო', 'june 24 , 2013', 'present', 'monday to friday', '10:55'], ['mexico', 'cachito de cielo', 'მცირე ნაწილი', 'october 7 , 2013', 'present', 'monday to sunday', '16:45'], ['eeuu', 'la patrona', 'მკაწრავი უკვდავება', 'september 22 , 2013', 'present', 'monday to sunday', '19:45'], ['mexico', 'amores verdaderos', 'უყვარს კალოჩე', 'july 17 , 2013', 'cancelled since october 6 , 2013', 'monday to friday', '17:45'], ['mexico', 'la tempestad', 'შტორმის მოტანილს შტორმი წაიყვანს', 'october 7 , 2013', 'present', 'monday to friday', '17:45'], ['brazil', 'avenida brasil', 'ბრაზილიის უბანი', 'october 14 , 2013', 'present', 'monday to friday', '11:00'], ['turkey', 'lale devri', 'ტიტების ტყვეობაში', 'july 25 , 2013', 'present', 'monday to sunday', '18:45 - 21:00']]

eurovision song contest 1970

https://en.wikipedia.org/wiki/Eurovision_Song_Contest_1970

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-184813-1.html.csv

count

two of the songs had lyrics written in the english language .

{'scope': 'all', 'criterion': 'equal', 'value': 'english', 'result': '2', 'col': '1', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'language', 'english'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose language record fuzzily matches to english .', 'tostr': 'filter_eq { all_rows ; language ; english }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; language ; english } }', 'tointer': 'select the rows whose language record fuzzily matches to english . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; language ; english } } ; 2 } = true', 'tointer': 'select the rows whose language record fuzzily matches to english . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; language ; english } } ; 2 } = true

select the rows whose language record fuzzily matches to english . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'language_5': 5, 'english_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'language_5': 'language', 'english_6': 'english', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'language_5': [0], 'english_6': [0], '2_7': [2]}

['language', 'artist', 'song', 'place', 'points']

[['dutch', 'hearts of soul', 'waterman', '7', '7'], ['french', 'henri dès', 'retour', '4', '8'], ['italian', 'gianni morandi', 'occhi di ragazza', '8', '5'], ['slovene', 'eva sršen', 'pridi , dala ti bom cvet', '11', '4'], ['french', 'jean vallée', "viens l'oublier", '8', '5'], ['french', 'guy bonnet', 'marie - blanche', '4', '8'], ['english', 'mary hopkin', "knock , knock who 's there", '2', '26'], ['french', 'david alexandre winter', 'je suis tombé du ciel', '12', '0'], ['spanish', 'julio iglesias', 'gwendolyne', '4', '8'], ['french', 'dominique dussault', 'marlène', '8', '5'], ['german', 'katja ebstein', 'wunder gibt es immer wieder', '3', '12'], ['english', 'dana', 'all kinds of everything', '1', '32']]

list of festivals at donington park

https://en.wikipedia.org/wiki/List_of_Festivals_at_Donington_Park

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10311801-2.html.csv

unique

there was only one metallica concert that was held at donlington park .

{'scope': 'all', 'row': '6', 'col': '3', 'col_other': 'n/a', 'criterion': 'fuzzily_match', 'value': 'metallica', 'subset': None}

{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'event', 'metallica'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose event record fuzzily matches to metallica .', 'tostr': 'filter_eq { all_rows ; event ; metallica }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; event ; metallica } } = true', 'tointer': 'select the rows whose event record fuzzily matches to metallica . there is only one such row in the table .'}

only { filter_eq { all_rows ; event ; metallica } } = true

select the rows whose event record fuzzily matches to metallica . there is only one such row in the table .

2

2

{'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'event_4': 4, 'metallica_5': 5}

{'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'event_4': 'event', 'metallica_5': 'metallica'}

{'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'event_4': [0], 'metallica_5': [0]}

['year', 'date', 'event', 'days', 'stages', 'acts']

[['1990', '18 august', 'monsters of rock', '1 day', '1 stage', '5 bands'], ['1991', '17 august', 'monsters of rock', '1 day', '1 stage', '5 bands'], ['1992', '2526 july', 'one step beyond', '24 hours', '1 stage', "60 + dj 's"], ['1992', '22 august', 'monsters of rock', '1 day', '1 stage', '6 bands'], ['1994', '4 june', 'monsters of rock', '1 day', '2 stages', '12 bands'], ['1995', '26 august', 'metallica : escape from the studio', '1 day', '1 stage', '9 bands'], ['1996', '17 august', 'monsters of rock', '1 day', '2 stages', '13 bands']]

2008 north west 200 races

https://en.wikipedia.org/wiki/2008_North_West_200_Races

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17477518-2.html.csv

aggregation

the average speed among competitors in the 2008 north west 200 races was around 121.208 mph .

{'scope': 'all', 'col': '5', 'type': 'average', 'result': '121.208', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'speed'], 'result': '121.208', 'ind': 0, 'tostr': 'avg { all_rows ; speed }'}, '121.208'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; speed } ; 121.208 } = true', 'tointer': 'the average of the speed record of all rows is 121.208 .'}

round_eq { avg { all_rows ; speed } ; 121.208 } = true

the average of the speed record of all rows is 121.208 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'speed_4': 4, '121.208_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'speed_4': 'speed', '121.208_5': '121.208'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'speed_4': [0], '121.208_5': [1]}

['rank', 'rider', 'team', 'time', 'speed']

[['1', 'michael rutter', 'ducati', "21 ' 52.169", '122.609 mph'], ['2', 'guy martin', 'honda', '+ 0.810', '122.534 mph'], ['3', 'john mcguinness', 'honda', '+ 0.956', '122.510 mph'], ['4', 'steve plater', 'yamaha yzf - r', '+ 1.192', '121.658 mph'], ['5', 'gary johnson', 'honda', '+ 10.257', '120.979 mph'], ['6', 'bruce anstey', 'suzuki gsx - r1000', '+ 17.682', '120.979 mph'], ['7', 'ian hutchinson', 'yamaha', '+ 17.970', '120.953 mph'], ['8', 'ryan farquhar', 'kawasaki zx - 10r', '+ 18.386', '120.915 mph'], ['9', 'denver robb', 'suzuki', '+ 27.118', '120.127 mph'], ['10', 'keith amor', 'honda', '+ 41.841', '118.820 mph']]

football records in spain

https://en.wikipedia.org/wiki/Football_records_in_Spain

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17937080-2.html.csv

superlative

the team that had the most goals in a season was real madrid .

{'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'goals'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; goals }'}, 'club'], 'result': 'real madrid', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; goals } ; club }'}, 'real madrid'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; goals } ; club } ; real madrid } = true', 'tointer': 'select the row whose goals record of all rows is maximum . the club record of this row is real madrid .'}

eq { hop { argmax { all_rows ; goals } ; club } ; real madrid } = true

select the row whose goals record of all rows is maximum . the club record of this row is real madrid .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'goals_5': 5, 'club_6': 6, 'real madrid_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'goals_5': 'goals', 'club_6': 'club', 'real madrid_7': 'real madrid'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'goals_5': [0], 'club_6': [1], 'real madrid_7': [2]}

['rank', 'club', 'season', 'goals', 'apps']

[['1', 'real madrid', '2011 / 12', '121', '38'], ['2', 'barcelona', '2012 / 13', '115', '38'], ['3', 'barcelona', '2011 / 12', '114', '38'], ['4', 'real madrid', '1989 / 90', '107', '38'], ['5', 'barcelona', '2008 / 09', '105', '38'], ['6', 'real madrid', '2012 / 13', '103', '38'], ['7', 'real madrid', '2009 / 10', '102', '38'], ['7', 'real madrid', '2010 / 11', '102', '38'], ['7', 'barcelona', '1996 / 97', '102', '42']]

mighty ships

https://en.wikipedia.org/wiki/Mighty_Ships

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26168687-3.html.csv

aggregation

the average original air date for mighty ships is 2009 .

{'scope': 'all', 'col': '7', 'type': 'average', 'result': '2009', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'original air date'], 'result': '2009', 'ind': 0, 'tostr': 'avg { all_rows ; original air date }'}, '2009'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; original air date } ; 2009 } = true', 'tointer': 'the average of the original air date record of all rows is 2009 .'}

round_eq { avg { all_rows ; original air date } ; 2009 } = true

the average of the original air date record of all rows is 2009 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'original air date_4': 4, '2009_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'original air date_4': 'original air date', '2009_5': '2009'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'original air date_4': [0], '2009_5': [1]}

['no in series', 'no in season', 'title', 'vessel type', 'vessel operator', 'narrated by', 'original air date']

[['5', '1', 'mv resolution', 'turbine installation vessel', 'mpi offshore ltd', 'barbara budd', '2009'], ['6', '2', 'mv peace in africa', 'dredger', 'de beers', 'barbara budd', '2009'], ['7', '3', 'akamalik', 'fishing trawler', 'royal greenland', 'barbara budd', '2009'], ['8', '4', 'ccgs henry larsen', 'icebreaker', 'canadian coast guard', 'barbara budd', '2009'], ['9', '5', 'uss nimitz', 'supercarrier', 'us navy', 'barbara budd', '2009'], ['10', '6', 'hdms absalon', 'flexible support ship', 'royal danish navy', 'barbara budd', '2009'], ['11', '7', 'mv fairplayer', 'heavy lift vessel', 'jumbo shipping', 'barbara budd', '2009'], ['12', '8', 'tyco resolute', 'cable layer', 'tyco telecommunications', 'barbara budd', '2009']]

2009 open championship

https://en.wikipedia.org/wiki/2009_Open_Championship

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18811509-5.html.csv

unique

in the 2009 open championship , the only player from australia was mathew goggin .

{'scope': 'all', 'row': '11', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'australia', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'australia'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to australia .', 'tostr': 'filter_eq { all_rows ; country ; australia }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; country ; australia } }', 'tointer': 'select the rows whose country record fuzzily matches to australia . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'australia'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to australia .', 'tostr': 'filter_eq { all_rows ; country ; australia }'}, 'player'], 'result': 'mathew goggin', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; country ; australia } ; player }'}, 'mathew goggin'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; country ; australia } ; player } ; mathew goggin }', 'tointer': 'the player record of this unqiue row is mathew goggin .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; country ; australia } } ; eq { hop { filter_eq { all_rows ; country ; australia } ; player } ; mathew goggin } } = true', 'tointer': 'select the rows whose country record fuzzily matches to australia . there is only one such row in the table . the player record of this unqiue row is mathew goggin .'}

and { only { filter_eq { all_rows ; country ; australia } } ; eq { hop { filter_eq { all_rows ; country ; australia } ; player } ; mathew goggin } } = true

select the rows whose country record fuzzily matches to australia . there is only one such row in the table . the player record of this unqiue row is mathew goggin .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'country_7': 7, 'australia_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'mathew goggin_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'country_7': 'country', 'australia_8': 'australia', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'mathew goggin_10': 'mathew goggin'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'country_7': [0], 'australia_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'mathew goggin_10': [3]}

['place', 'player', 'country', 'score', 'to par']

[['t1', 'steve marino', 'united states', '67 + 68 = 135', '5'], ['t1', 'tom watson', 'united states', '65 + 70 = 135', '5'], ['3', 'mark calcavecchia', 'united states', '67 + 69 = 136', '4'], ['t4', 'ross fisher', 'england', '69 + 68 = 137', '3'], ['t4', 'retief goosen', 'south africa', '67 + 70 = 137', '3'], ['t4', 'miguel ángel jiménez', 'spain', '64 + 73 = 137', '3'], ['t4', 'kenichi kuboya', 'japan', '65 + 72 = 137', '3'], ['t4', 'vijay singh', 'fiji', '67 + 70 = 137', '3'], ['t9', 'stewart cink', 'united states', '66 + 72 = 138', '2'], ['t9', 'j b holmes', 'united states', '68 + 70 = 138', '2'], ['t9', 'mathew goggin', 'australia', '66 + 72 = 138', '2'], ['t9', 'james kingston', 'south africa', '67 + 71 = 138', '2'], ['t9', 'lee westwood', 'england', '68 + 70 = 138', '2']]

1948 ashes series

https://en.wikipedia.org/wiki/1948_Ashes_series

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16570286-4.html.csv

comparative

ray lindwall and bill johnston both got the same number of wickets in the 1948 ashes series .

{'row_1': '1', 'row_2': '4', 'col': '4', 'col_other': '1', 'relation': 'equal', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'ray lindwall'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to ray lindwall .', 'tostr': 'filter_eq { all_rows ; player ; ray lindwall }'}, 'wickets'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; ray lindwall } ; wickets }', 'tointer': 'select the rows whose player record fuzzily matches to ray lindwall . take the wickets record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'bill johnston'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to bill johnston .', 'tostr': 'filter_eq { all_rows ; player ; bill johnston }'}, 'wickets'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; bill johnston } ; wickets }', 'tointer': 'select the rows whose player record fuzzily matches to bill johnston . take the wickets record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { all_rows ; player ; ray lindwall } ; wickets } ; hop { filter_eq { all_rows ; player ; bill johnston } ; wickets } } = true', 'tointer': 'select the rows whose player record fuzzily matches to ray lindwall . take the wickets record of this row . select the rows whose player record fuzzily matches to bill johnston . take the wickets record of this row . the first record is equal to the second record .'}

eq { hop { filter_eq { all_rows ; player ; ray lindwall } ; wickets } ; hop { filter_eq { all_rows ; player ; bill johnston } ; wickets } } = true

select the rows whose player record fuzzily matches to ray lindwall . take the wickets record of this row . select the rows whose player record fuzzily matches to bill johnston . take the wickets record of this row . the first record is equal to the second record .

5

5

{'eq_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'player_7': 7, 'ray lindwall_8': 8, 'wickets_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'bill johnston_12': 12, 'wickets_13': 13}

{'eq_4': 'eq', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'player_7': 'player', 'ray lindwall_8': 'ray lindwall', 'wickets_9': 'wickets', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'bill johnston_12': 'bill johnston', 'wickets_13': 'wickets'}

{'eq_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'ray lindwall_8': [0], 'wickets_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'bill johnston_12': [1], 'wickets_13': [3]}

['player', 'team', 'matches', 'wickets', 'average', 'best bowling']

[['ray lindwall', 'australia', '5', '27', '19.62', '6 / 20'], ['norman yardley', 'england', '5', '9', '22.66', '2 / 32'], ['keith miller', 'australia', '5', '13', '23.15', '4 / 125'], ['bill johnston', 'australia', '5', '27', '23.33', '5 / 36'], ['ernie toshack', 'australia', '4', '11', '33.09', '5 / 40'], ['alec bedser', 'england', '5', '18', '38.22', '4 / 81']]

mystery of mamo

https://en.wikipedia.org/wiki/Mystery_of_Mamo

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2160215-1.html.csv

count

for the mystery of mamo , when the english ( streamline ) is unknown , there were two instances where the english ( pioneer / geneon ) is richard cansino .

{'scope': 'subset', 'criterion': 'equal', 'value': 'richard cansino', 'result': '2', 'col': '5', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'unknown'}}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'english ( streamline )', 'unknown'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; english ( streamline ) ; unknown }', 'tointer': 'select the rows whose english ( streamline ) record fuzzily matches to unknown .'}, 'english ( pioneer / geneon )', 'richard cansino'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose english ( streamline ) record fuzzily matches to unknown . among these rows , select the rows whose english ( pioneer / geneon ) record fuzzily matches to richard cansino .', 'tostr': 'filter_eq { filter_eq { all_rows ; english ( streamline ) ; unknown } ; english ( pioneer / geneon ) ; richard cansino }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; english ( streamline ) ; unknown } ; english ( pioneer / geneon ) ; richard cansino } }', 'tointer': 'select the rows whose english ( streamline ) record fuzzily matches to unknown . among these rows , select the rows whose english ( pioneer / geneon ) record fuzzily matches to richard cansino . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; english ( streamline ) ; unknown } ; english ( pioneer / geneon ) ; richard cansino } } ; 2 } = true', 'tointer': 'select the rows whose english ( streamline ) record fuzzily matches to unknown . among these rows , select the rows whose english ( pioneer / geneon ) record fuzzily matches to richard cansino . the number of such rows is 2 .'}

eq { count { filter_eq { filter_eq { all_rows ; english ( streamline ) ; unknown } ; english ( pioneer / geneon ) ; richard cansino } } ; 2 } = true

select the rows whose english ( streamline ) record fuzzily matches to unknown . among these rows , select the rows whose english ( pioneer / geneon ) record fuzzily matches to richard cansino . the number of such rows is 2 .

4

4

{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'english ( streamline )_6': 6, 'unknown_7': 7, 'english ( pioneer / geneon )_8': 8, 'richard cansino_9': 9, '2_10': 10}

{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'english ( streamline )_6': 'english ( streamline )', 'unknown_7': 'unknown', 'english ( pioneer / geneon )_8': 'english ( pioneer / geneon )', 'richard cansino_9': 'richard cansino', '2_10': '2'}

{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'english ( streamline )_6': [0], 'unknown_7': [0], 'english ( pioneer / geneon )_8': [1], 'richard cansino_9': [1], '2_10': [3]}

['character', 'original japanese', 'english ( streamline )', 'english ( manga uk )', 'english ( pioneer / geneon )']

[['arsène lupin iii / wolf iii', 'yasuo yamada', 'bob bergen', 'bill dufris', 'tony oliver'], ['fujiko mine / margot', 'eiko masuyama', 'edie mirman', 'toni barry', 'michelle ruff'], ['howard lockewood / foward fughes ( mamo / mameaux )', 'kō nishimura', 'robert axelrod', 'allan wenger', 'george c cole'], ['daisuke jigen / dan dunn', 'kiyoshi kobayashi', 'steve bulen', 'eric meyers', 'richard epcar'], ['goemon ishikawa xiii / samurai', 'makio inoue', 'kirk thornton', 'garrick hagon', 'lex lang'], ['inspector koichi zenigata / detective ed scott', 'gorō naya', 'david povall', 'sean barrett', 'dan martin'], ['heinrich starky / stuckey gissinger', 'tōru ōhira', 'steve kramer', 'unknown', 'osgood w glick'], ['special agent gordon', 'hidekatsu shibata', 'michael forest', 'unknown', 'michael mcconnohie'], ['police commissioner', 'kōsei tomita', 'jeff winkless', 'unknown', 'richard cansino'], ['flinch / frenchy', 'shōzō iizuka', 'unknown', 'unknown', 'bob papenbrook'], ['scientist', 'ichirō murakoshi', 'unknown', 'unknown', 'richard cansino'], ['dietman / premier', 'shunsuke shima', 'unknown', 'unknown', 'richard cansino'], ['officer', 'yūji mikimoto', 'unknown', 'unknown', 'unknown'], ['egyptian police chief', 'haruo minami ( special guest voice )', 'steve kramer', 'unknown', 'richard cansino'], ['president', 'fujio akatsuka ( special guest voice )', 'steve kramer', 'unknown', 'richard cansino']]

skal vi danse ? ( season 6 )

https://en.wikipedia.org/wiki/Skal_vi_danse%3F_%28season_6%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28677723-9.html.csv

aggregation

in the sixth season of the show " skal vi danse ? " the scores from tango dances totaled 54 .

{'scope': 'subset', 'col': '8', 'type': 'sum', 'result': '54', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'tango'}}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'style', 'tango'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; style ; tango }', 'tointer': 'select the rows whose style record fuzzily matches to tango .'}, 'total'], 'result': '54', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; style ; tango } ; total }'}, '54'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; style ; tango } ; total } ; 54 } = true', 'tointer': 'select the rows whose style record fuzzily matches to tango . the sum of the total record of these rows is 54 .'}

round_eq { sum { filter_eq { all_rows ; style ; tango } ; total } ; 54 } = true

select the rows whose style record fuzzily matches to tango . the sum of the total record of these rows is 54 .

3

3

{'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'style_5': 5, 'tango_6': 6, 'total_7': 7, '54_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'style_5': 'style', 'tango_6': 'tango', 'total_7': 'total', '54_8': '54'}

{'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'style_5': [0], 'tango_6': [0], 'total_7': [1], '54_8': [2]}

['couple', 'style', 'music', 'trine dehli cleve', 'tor fløysvik', 'karianne gulliksen', 'christer tornell', 'total']

[['åsleik & nadia', 'cha - cha - cha', 'ymca - village people', '8', '8', '8', '8', '32'], ['stig & alexandra', 'pasodoble', 'eye of the tiger - survivor', '6', '5', '6', '7', '24'], ['stine & tom - erik', 'rumba', 'la isla bonita - madonna', '6', '6', '7', '6', '25'], ['cecilie & tobias', 'tango', 'twist in my sobriety - tanita tikaram', '5', '4', '6', '6', '21'], ['håvard & elena', 'cha - cha - cha', 'never gon na give you up - rick astley', '8', '7', '8', '7', '30'], ['maria & asmund', 'english waltz', 'i have nothing - whitney houston', '7', '5', '7', '6', '25'], ['aylar & egor', 'tango', "that do n't impress me much - shania twain", '8', '9', '8', '8', '33']]

list of how it 's made episodes

https://en.wikipedia.org/wiki/List_of_How_It%27s_Made_episodes

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15187735-13.html.csv

count

there are 13 episodes in series titled ' how it 's made ' .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': '13', 'result': '13', 'col': '1', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'series ep', '13'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose series ep record fuzzily matches to 13 .', 'tostr': 'filter_eq { all_rows ; series ep ; 13 }'}], 'result': '13', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; series ep ; 13 } }', 'tointer': 'select the rows whose series ep record fuzzily matches to 13 . the number of such rows is 13 .'}, '13'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; series ep ; 13 } } ; 13 } = true', 'tointer': 'select the rows whose series ep record fuzzily matches to 13 . the number of such rows is 13 .'}

eq { count { filter_eq { all_rows ; series ep ; 13 } } ; 13 } = true

select the rows whose series ep record fuzzily matches to 13 . the number of such rows is 13 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'series ep_5': 5, '13_6': 6, '13_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'series ep_5': 'series ep', '13_6': '13', '13_7': '13'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'series ep_5': [0], '13_6': [0], '13_7': [2]}

['series ep', 'episode', 'segment a', 'segment b', 'segment c', 'segment d']

[['13 - 01', '157', 'hammers', 'swiss cheese', 'roller skates', 'coloured pencils'], ['13 - 02', '158', 'carbon fiber bicycles', 'blood products', 'forged chandeliers', 'ballpoint pens'], ['13 - 03', '159', 'swiss army knives', 'player piano rolls', 'oil tankers', 'racing wheels'], ['13 - 04', '160', 'bowling balls', 'barber poles', 'felt', 'radar guns'], ['13 - 05', '161', 'copper pipe fittings', 'cylinder music boxes', 'pepper mills', 'hot rod steering columns'], ['13 - 06', '162', 'gears', 'leather watchbands', 'vitrelle dishes', 'kitchen shears'], ['13 - 07', '163', 'pressure cookers', 'mechanical singing birds', 'oceanographic buoys', 'stainless - steel tank trailers'], ['13 - 08', '164', 'aluminium boats', 'alpine horns', 'es luxury watch ( part 1 )', 'es luxury watch ( part 2 )'], ['13 - 09', '165', 'all - terrain vehicles', 'alpine skis', 'laser cutters', 'marble sculptures'], ['13 - 10', '166', 'socket sets', 'leather shoes', 'aluminium water bottles', 'bike chains'], ['13 - 11', '167', 'carved wood sculptures', 'flatware', 'cow bells', 'fountain pens'], ['13 - 12', '168', 'olive oil', 'lift s truck', 'seamless rolled rings', 'ski boots'], ['13 - 13', '169', 'professional cookware', 'luxury inlaid boxes', 'high - efficiency water heaters', 'scooters']]

2nd amateurliga bayern

https://en.wikipedia.org/wiki/2nd_Amateurliga_Bayern

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23224961-1.html.csv

count

fc passau won the niederbayern region a total of four times .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'fc passau', 'result': '4', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'niederbayern', 'fc passau'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose niederbayern record fuzzily matches to fc passau .', 'tostr': 'filter_eq { all_rows ; niederbayern ; fc passau }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; niederbayern ; fc passau } }', 'tointer': 'select the rows whose niederbayern record fuzzily matches to fc passau . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; niederbayern ; fc passau } } ; 4 } = true', 'tointer': 'select the rows whose niederbayern record fuzzily matches to fc passau . the number of such rows is 4 .'}

eq { count { filter_eq { all_rows ; niederbayern ; fc passau } } ; 4 } = true

select the rows whose niederbayern record fuzzily matches to fc passau . the number of such rows is 4 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'niederbayern_5': 5, 'fc passau_6': 6, '4_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'niederbayern_5': 'niederbayern', 'fc passau_6': 'fc passau', '4_7': '4'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'niederbayern_5': [0], 'fc passau_6': [0], '4_7': [2]}

['season', 'oberbayern a', 'oberbayern b', 'niederbayern', 'schwaben', 'oberpfalz']

[['1951 - 52', 'sc münchen - süd', 'spvgg helios münchen', 'spvgg plattling', 'fc kempten', 'sv mitterteich'], ['1952 - 53', 'mtv ingolstadt', 'fc penzberg', 'spvgg deggendorf', 'tsv kottern', 'sv mitterteich'], ['1953 - 54', 'bsc sendling', 'tsv raubling', 'sv saal', 'spvgg kaufbeuren', 'tv sulzbach - rosenberg'], ['1954 - 55', 'tsg pasing', 'asv dachau', 'sv saal', 'fc memmingen', 'fc maxhütte - haidhof'], ['1955 - 56', 'fc bayern munich ii', 'sv aubing', '1 . fc passau', 'fc kempten', 'fc schwandorf'], ['1956 - 57', 'tsg pasing', 'sc 1906 münchen', '1 . fc passau', 'fc kempten', 'fc maxhütte - haidhof'], ['1957 - 58', 'spvgg helios münchen', 'sv aubing', '1 . fc passau', 'spvgg kaufbeuren', 'spvgg vohenstrauß'], ['1958 - 59', 'tsv 1860 munich ii', 'fsv pfaffenhofen', 'spvgg deggendorf', 'fc memmingen', 'turnerschaft regensburg'], ['1959 - 60', 'tsv 1860 rosenheim', 'tsg pasing', 'spvgg landshut', 'tsv kottern', 'tus rosenberg'], ['1960 - 61', 'fc oberau', 'esv ingolstadt', '1 . fc passau', 'tsv königsbrunn', 'tv wackersdorf'], ['1961 - 62', 'wacker burghausen', 'fc wacker münchen', 'sv saal', 'bc augsburg ii', 'jahn regensburg ii']]

vesna dolonc

https://en.wikipedia.org/wiki/Vesna_Dolonc

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15639710-7.html.csv

majority

vesna dolonc had a runner-up outcome in the majority of tournaments that she played .

{'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'runner-up', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'outcome', 'runner-up'], 'result': True, 'ind': 0, 'tointer': 'for the outcome records of all rows , most of them fuzzily match to runner-up .', 'tostr': 'most_eq { all_rows ; outcome ; runner-up } = true'}

most_eq { all_rows ; outcome ; runner-up } = true

for the outcome records of all rows , most of them fuzzily match to runner-up .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'outcome_3': 3, 'runner-up_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'outcome_3': 'outcome', 'runner-up_4': 'runner-up'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'outcome_3': [0], 'runner-up_4': [0]}

['outcome', 'date', 'tournament', 'surface', 'partner', 'opponents in the final', 'score']

[['runner - up', '2 october 2005', 'podgorica , serbia & montenegro', 'clay', 'neda kozić', 'ani mijačika dijana stojics', '6 - 1 3 - 6 4 - 6'], ['runner - up', '11 may 2007', 'monzón , spain', 'hard', 'iryna kuryanovich', 'estrella cabeza - candela maría emilia salerni', '2 - 6 1 - 6'], ['winner', '25 august 2007', 'moscow , russia', 'clay', 'maria kondratieva', 'nina bratchikova sophie lefèvre', '6 - 2 6 - 1'], ['runner - up', '10 november 2007', 'minsk , belarus', 'hard', 'ekaterina ivanova', 'alla kudryavtseva anastasia pavlyuchenkova', '0 - 6 2 - 6'], ['winner', '10 april 2009', 'monzón , spain', 'hard', 'yi chen', 'alberta brianti margalita chakhnashvili', '2 - 6 6 - 4'], ['runner - up', '11 july 2009', 'la coruña , spain', 'hard', 'ksenia milevskaya', 'maría irigoyen florencia molinero', '2 - 6 4 - 6'], ['runner - up', '14 november 2009', 'minsk , belarus', 'hard', 'evgeniya rodina', 'lyudmyla kichenok nadiya kichenok', '3 - 6 6 - 7 ( 7 )'], ['runner - up', '25 september 2010', 'shrewsbury , great britain', 'hard', 'claire feuerstein', 'vitalia diatchenko irena pavlovic', '4 - 6 6 - 4'], ['runner - up', '2 july 2011', 'cuneo , italia', 'clay', 'eva birnerová', 'mandy minella stefanie vögele', '3 - 6 2 - 6'], ['runner - up', '6 february 2012', 'midland , usa', 'hard ( i )', 'stéphanie foretz gacon', 'andrea hlaváčková lucie hradecká', '6 ( 4 ) - 7 2 - 6'], ['winner', '14 may 2012', 'saint - gaudens , france', 'clay', 'irina khromacheva', 'naomi broady julia glushko', '6 - 2 6 - 0']]

1965 vfl season

https://en.wikipedia.org/wiki/1965_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10788451-11.html.csv

count

there were 6 game venues used during the 1965 vfl season .

{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '6', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'venue'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record is arbitrary .', 'tostr': 'filter_all { all_rows ; venue }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; venue } }', 'tointer': 'select the rows whose venue record is arbitrary . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; venue } } ; 6 } = true', 'tointer': 'select the rows whose venue record is arbitrary . the number of such rows is 6 .'}

eq { count { filter_all { all_rows ; venue } } ; 6 } = true

select the rows whose venue record is arbitrary . the number of such rows is 6 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'venue_5': 5, '6_6': 6}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'venue_5': 'venue', '6_6': '6'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'venue_5': [0], '6_6': [2]}

['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']

[['hawthorn', '9.13 ( 67 )', 'melbourne', '19.11 ( 125 )', 'glenferrie oval', '14900', '3 july 1965'], ['footscray', '7.10 ( 52 )', 'north melbourne', '5.10 ( 40 )', 'western oval', '14150', '3 july 1965'], ['st kilda', '12.8 ( 80 )', 'carlton', '10.14 ( 74 )', 'moorabbin oval', '35794', '3 july 1965'], ['richmond', '23.20 ( 158 )', 'south melbourne', '12.10 ( 82 )', 'mcg', '35200', '10 july 1965'], ['essendon', '12.16 ( 88 )', 'geelong', '6.9 ( 45 )', 'windy hill', '27000', '10 july 1965'], ['collingwood', '10.14 ( 74 )', 'fitzroy', '4.6 ( 30 )', 'victoria park', '20657', '10 july 1965']]

1981 vfl season

https://en.wikipedia.org/wiki/1981_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10823950-20.html.csv

ordinal

mcg venue recorded the highest crowd participation during the 1981 vfl season .

{'row': '5', 'col': '6', 'order': '1', 'col_other': '5', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'crowd', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; crowd ; 1 }'}, 'venue'], 'result': 'mcg', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; crowd ; 1 } ; venue }'}, 'mcg'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; mcg } = true', 'tointer': 'select the row whose crowd record of all rows is 1st maximum . the venue record of this row is mcg .'}

eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; mcg } = true

select the row whose crowd record of all rows is 1st maximum . the venue record of this row is mcg .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '1_6': 6, 'venue_7': 7, 'mcg_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', '1_6': '1', 'venue_7': 'venue', 'mcg_8': 'mcg'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '1_6': [0], 'venue_7': [1], 'mcg_8': [2]}

['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']

[['carlton', '15.8 ( 98 )', 'essendon', '14.15 ( 99 )', 'princes park', '36736', '15 august 1981'], ['north melbourne', '21.19 ( 145 )', 'melbourne', '13.9 ( 87 )', 'arden street oval', '7749', '15 august 1981'], ['south melbourne', '12.14 ( 86 )', 'geelong', '21.13 ( 139 )', 'lake oval', '11489', '15 august 1981'], ['footscray', '12.14 ( 86 )', 'fitzroy', '22.15 ( 147 )', 'western oval', '11770', '15 august 1981'], ['richmond', '11.20 ( 86 )', 'collingwood', '14.7 ( 91 )', 'mcg', '69217', '15 august 1981'], ['hawthorn', '10.17 ( 77 )', 'st kilda', '9.14 ( 68 )', 'vfl park', '20863', '15 august 1981']]

yen plus

https://en.wikipedia.org/wiki/Yen_Plus

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18685750-1.html.csv

comparative

in yen plus , the title bamboo blade had its last issue released earlier than the title black butler .

{'row_1': '1', 'row_2': '2', 'col': '4', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'bamboo blade'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to bamboo blade .', 'tostr': 'filter_eq { all_rows ; title ; bamboo blade }'}, 'last issue'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; title ; bamboo blade } ; last issue }', 'tointer': 'select the rows whose title record fuzzily matches to bamboo blade . take the last issue record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'black butler'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose title record fuzzily matches to black butler .', 'tostr': 'filter_eq { all_rows ; title ; black butler }'}, 'last issue'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; title ; black butler } ; last issue }', 'tointer': 'select the rows whose title record fuzzily matches to black butler . take the last issue record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; title ; bamboo blade } ; last issue } ; hop { filter_eq { all_rows ; title ; black butler } ; last issue } } = true', 'tointer': 'select the rows whose title record fuzzily matches to bamboo blade . take the last issue record of this row . select the rows whose title record fuzzily matches to black butler . take the last issue record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; title ; bamboo blade } ; last issue } ; hop { filter_eq { all_rows ; title ; black butler } ; last issue } } = true

select the rows whose title record fuzzily matches to bamboo blade . take the last issue record of this row . select the rows whose title record fuzzily matches to black butler . take the last issue record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'title_7': 7, 'bamboo blade_8': 8, 'last issue_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'title_11': 11, 'black butler_12': 12, 'last issue_13': 13}

{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'title_7': 'title', 'bamboo blade_8': 'bamboo blade', 'last issue_9': 'last issue', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'title_11': 'title', 'black butler_12': 'black butler', 'last issue_13': 'last issue'}

{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'title_7': [0], 'bamboo blade_8': [0], 'last issue_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'title_11': [1], 'black butler_12': [1], 'last issue_13': [3]}

['title', 'author', 'first issue', 'last issue', 'completed']

[['bamboo blade', 'masahiro totsuka ( author ) , aguri igarashi ( artist )', 'august 2008', 'may 2009', 'no'], ['black butler', 'yana toboso', 'august 2009', 'july 2010', 'no'], ['higurashi when they cry', 'ryukishi07 ( author ) , karin suzuragi ( artist )', 'august 2008', 'january 2009', 'no'], ['hero tales', 'huang jin zhou ( author ) , hiromu arakawa ( artist )', 'february 2009', 'on hiatus', 'no'], ['k - on !', 'kakifly', 'september 2010', 'ongoing', 'no'], ['nabari no ou', 'yuhki kamatani', 'august 2008', 'unknown', 'no'], ['pandora hearts', 'jun mochizuki', 'june 2009', 'unknown', 'no'], ['soul eater', 'atsushi okubo', 'august 2008', 'unknown', 'no'], ['sumomomo momomo', 'shinobu ohtaka', 'august 2008', 'october 2009', 'no'], ['yotsuba & !', 'kiyohiko azuma', 'september 2010', 'ongoing', 'no']]

new year 's revolution ( 2006 )

https://en.wikipedia.org/wiki/New_Year%27s_Revolution_%282006%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14656943-2.html.csv

ordinal

chris masters had the second highest elimination time in the ring at the 2006 new year 's revolution .

{'row': '4', 'col': '5', 'order': '2', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'time', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; time ; 2 }'}, 'wrestler'], 'result': 'chris masters', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; time ; 2 } ; wrestler }'}, 'chris masters'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; time ; 2 } ; wrestler } ; chris masters } = true', 'tointer': 'select the row whose time record of all rows is 2nd maximum . the wrestler record of this row is chris masters .'}

eq { hop { nth_argmax { all_rows ; time ; 2 } ; wrestler } ; chris masters } = true

select the row whose time record of all rows is 2nd maximum . the wrestler record of this row is chris masters .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'time_5': 5, '2_6': 6, 'wrestler_7': 7, 'chris masters_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'time_5': 'time', '2_6': '2', 'wrestler_7': 'wrestler', 'chris masters_8': 'chris masters'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'time_5': [0], '2_6': [0], 'wrestler_7': [1], 'chris masters_8': [2]}

['elimination no', 'wrestler', 'entered', 'eliminated by', 'time']

[['1', 'kurt angle', '4', 'michaels', '13:58'], ['2', 'kane', '6', 'carlito and masters', '19:24'], ['3', 'shawn michaels', '1', 'carlito', '23:35'], ['4', 'chris masters', '5', 'carlito', '28:15'], ['5', 'carlito', '3', 'cena', '28:22'], ['winner', 'john cena', '2', 'n / a', 'n / a']]

list of songs in rock band

https://en.wikipedia.org/wiki/List_of_songs_in_Rock_Band

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14160327-3.html.csv

majority

most of the songs are considered to be family friendly .

{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'yes', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'family friendly', 'yes'], 'result': True, 'ind': 0, 'tointer': 'for the family friendly records of all rows , most of them fuzzily match to yes .', 'tostr': 'most_eq { all_rows ; family friendly ; yes } = true'}

most_eq { all_rows ; family friendly ; yes } = true

for the family friendly records of all rows , most of them fuzzily match to yes .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'family friendly_3': 3, 'yes_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'family friendly_3': 'family friendly', 'yes_4': 'yes'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'family friendly_3': [0], 'yes_4': [0]}

['song title', 'artist', 'decade', 'genre', 'family friendly']

[['dirty little secret', 'all american rejects the all american rejects', '2000s', 'emo', 'yes'], ["do n't look back in anger", 'oasis', '1990s', 'rock', 'yes'], ['roam', "b - 52 's the b - 52 's", '1980s', 'pop / rock', 'yes'], ['rockaway beach', 'ramones', '1970s', 'punk', 'yes'], ['roxanne', 'police the police', '1970s', 'pop / rock', 'no']]

1971 - 72 cleveland cavaliers season

https://en.wikipedia.org/wiki/1971%E2%80%9372_Cleveland_Cavaliers_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16946097-3.html.csv

comparative

the caveliers scored more points on october 31 than they did on the 29th .

{'row_1': '10', 'row_2': '9', 'col': '4', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'october 31'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to october 31 .', 'tostr': 'filter_eq { all_rows ; date ; october 31 }'}, 'score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; october 31 } ; score }', 'tointer': 'select the rows whose date record fuzzily matches to october 31 . take the score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'october 29'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to october 29 .', 'tostr': 'filter_eq { all_rows ; date ; october 29 }'}, 'score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; october 29 } ; score }', 'tointer': 'select the rows whose date record fuzzily matches to october 29 . take the score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; date ; october 31 } ; score } ; hop { filter_eq { all_rows ; date ; october 29 } ; score } } = true', 'tointer': 'select the rows whose date record fuzzily matches to october 31 . take the score record of this row . select the rows whose date record fuzzily matches to october 29 . take the score record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; date ; october 31 } ; score } ; hop { filter_eq { all_rows ; date ; october 29 } ; score } } = true

select the rows whose date record fuzzily matches to october 31 . take the score record of this row . select the rows whose date record fuzzily matches to october 29 . take the score record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'date_7': 7, 'october 31_8': 8, 'score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'date_11': 11, 'october 29_12': 12, 'score_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'date_7': 'date', 'october 31_8': 'october 31', 'score_9': 'score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', 'october 29_12': 'october 29', 'score_13': 'score'}

{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'date_7': [0], 'october 31_8': [0], 'score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'date_11': [1], 'october 29_12': [1], 'score_13': [3]}

['date', 'h / a / n', 'opponent', 'score', 'record']

[['october 15', 'h', 'buffalo braves', '109 - 111 ( ot )', '0 - 1'], ['october 16', 'a', 'buffalo braves', '93 - 89', '1 - 1'], ['october 17', 'h', 'new york knicks', '120 - 121 ( ot )', '1 - 2'], ['october 19', 'a', 'milwaukee bucks', '82 - 116', '1 - 3'], ['october 20', 'h', 'san francisco warriors', '98 - 115', '1 - 4'], ['october 23', 'n', 'baltimore bullets', '109 - 101', '2 - 4'], ['october 24', 'h', 'philadelphia 76ers', '93 - 111', '2 - 5'], ['october 27', 'a', 'philadelphia 76ers', '106 - 120', '2 - 6'], ['october 29', 'h', 'atlanta hawks', '97 - 98', '2 - 7'], ['october 31', 'h', 'milwaukee bucks', '102 - 118', '2 - 8']]

boroughs of sherbrooke

https://en.wikipedia.org/wiki/Boroughs_of_Sherbrooke

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14927794-1.html.csv

aggregation

the average number of borough councilors is about 3.8 .

{'scope': 'all', 'col': '4', 'type': 'average', 'result': '3.8', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'number of borough councilors'], 'result': '3.8', 'ind': 0, 'tostr': 'avg { all_rows ; number of borough councilors }'}, '3.8'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; number of borough councilors } ; 3.8 } = true', 'tointer': 'the average of the number of borough councilors record of all rows is 3.8 .'}

round_eq { avg { all_rows ; number of borough councilors } ; 3.8 } = true

the average of the number of borough councilors record of all rows is 3.8 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'number of borough councilors_4': 4, '3.8_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'number of borough councilors_4': 'number of borough councilors', '3.8_5': '3.8'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'number of borough councilors_4': [0], '3.8_5': [1]}

['borough', 'components', 'population', 'number of borough councilors', 'number of municipal councilors']

[['brompton', 'bromptonville', '5771', '3', '1'], ['fleurimont', 'eastern sherbrooke , fleurimont', '41289', '5', '5'], ['lennoxville', 'lennoxville', '4947', '3', '1'], ['mont - bellevue', 'western sherbrooke , ascot', '31373', '4', '4'], ['rock forest - saint - élie - deauville', "rock forest , saint - élie - d'orford , deauville", '26757', '4', '4'], ['jacques - cartier', 'northern sherbrooke', '29311', '4', '4']]

list of ship launches in 1878

https://en.wikipedia.org/wiki/List_of_ship_launches_in_1878

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18548768-1.html.csv

unique

john roach and son built the only passenger ship that launched in 1878 .

{'scope': 'all', 'row': '1', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': 'passenger ship', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'class / type', 'passenger ship'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose class / type record fuzzily matches to passenger ship .', 'tostr': 'filter_eq { all_rows ; class / type ; passenger ship }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; class / type ; passenger ship } }', 'tointer': 'select the rows whose class / type record fuzzily matches to passenger ship . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'class / type', 'passenger ship'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose class / type record fuzzily matches to passenger ship .', 'tostr': 'filter_eq { all_rows ; class / type ; passenger ship }'}, 'builder'], 'result': 'john roach and son', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; class / type ; passenger ship } ; builder }'}, 'john roach and son'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; class / type ; passenger ship } ; builder } ; john roach and son }', 'tointer': 'the builder record of this unqiue row is john roach and son .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; class / type ; passenger ship } } ; eq { hop { filter_eq { all_rows ; class / type ; passenger ship } ; builder } ; john roach and son } } = true', 'tointer': 'select the rows whose class / type record fuzzily matches to passenger ship . there is only one such row in the table . the builder record of this unqiue row is john roach and son .'}

and { only { filter_eq { all_rows ; class / type ; passenger ship } } ; eq { hop { filter_eq { all_rows ; class / type ; passenger ship } ; builder } ; john roach and son } } = true

select the rows whose class / type record fuzzily matches to passenger ship . there is only one such row in the table . the builder record of this unqiue row is john roach and son .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'class / type_7': 7, 'passenger ship_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'builder_9': 9, 'john roach and son_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'class / type_7': 'class / type', 'passenger ship_8': 'passenger ship', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'builder_9': 'builder', 'john roach and son_10': 'john roach and son'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'class / type_7': [0], 'passenger ship_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'builder_9': [2], 'john roach and son_10': [3]}

['country', 'builder', 'location', 'ship', 'class / type']

[['united states', 'john roach and son', 'chester , pennsylvania', 'city of rio de janeiro', 'passenger ship'], ['germany', 'kaiserliche werft wilhelmshaven', 'wilhelmshaven', 'bayern', 'sachsen - class ironclad'], ['united kingdom', 'royal dockyard', 'devonport , devon', 'pegasus', 'doterel - class sloop'], ['united kingdom', 'royal dockyard', 'sheerness', 'gannet', 'doterel - class sloop'], ['norway', 'karljohansverns verft', 'horten', 'nor', 'vale - class gunboat'], ['norway', 'karljohansverns verft', 'horten', 'brage', 'vale - class gunboat'], ['germany', 'a g vulcan', 'stettin', 'wã ¼ rttemberg', 'sachsen - class ironclad']]

mont \ xc3 \ xa9r \ xc3 \ xa9gie

https://en.wikipedia.org/wiki/Mont%C3%A9r%C3%A9gie

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1011906-1.html.csv

count

there are 6 regional county municipalities ( rcm ) in the montérégie region .

{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '6', 'col': '1', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'regional county municipality ( rcm )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose regional county municipality ( rcm ) record is arbitrary .', 'tostr': 'filter_all { all_rows ; regional county municipality ( rcm ) }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; regional county municipality ( rcm ) } }', 'tointer': 'select the rows whose regional county municipality ( rcm ) record is arbitrary . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; regional county municipality ( rcm ) } } ; 6 } = true', 'tointer': 'select the rows whose regional county municipality ( rcm ) record is arbitrary . the number of such rows is 6 .'}

eq { count { filter_all { all_rows ; regional county municipality ( rcm ) } } ; 6 } = true

select the rows whose regional county municipality ( rcm ) record is arbitrary . the number of such rows is 6 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'regional county municipality (rcm)_5': 5, '6_6': 6}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'regional county municipality (rcm)_5': 'regional county municipality ( rcm )', '6_6': '6'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'regional county municipality (rcm)_5': [0], '6_6': [2]}

['regional county municipality ( rcm )', 'population canada 2011 census', 'land area', 'density ( pop per km2 )', 'seat of rcm']

[['acton', '15381', 'km2 ( sqmi )', '26.5', 'acton vale'], ['brome - missisquoi', '55621', 'km2 ( sqmi )', '33.7', 'cowansville'], ['la haute - yamaska', '85042', 'km2 ( sqmi )', '133.6', 'granby'], ['la vallãe - du - richelieu', '116773', 'km2 ( sqmi )', '198.3', 'mcmasterville'], ['le haut - richelieu', '114344', 'km2 ( sqmi )', '122.1', 'saint - jean - sur - richelieu'], ['les maskoutains', '84248', 'km2 ( sqmi )', '64.7', 'saint - hyacinthe']]

1990 - 91 atlanta hawks season

https://en.wikipedia.org/wiki/1990%E2%80%9391_Atlanta_Hawks_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27882867-6.html.csv

ordinal

in the 1990-91 atlanta hawks season , the game with the 2nd highest attendance was on january 12th .

{'row': '6', 'col': '8', 'order': '2', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'location attendance', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; location attendance ; 2 }'}, 'date'], 'result': 'january 12', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; location attendance ; 2 } ; date }'}, 'january 12'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; location attendance ; 2 } ; date } ; january 12 } = true', 'tointer': 'select the row whose location attendance record of all rows is 2nd maximum . the date record of this row is january 12 .'}

eq { hop { nth_argmax { all_rows ; location attendance ; 2 } ; date } ; january 12 } = true

select the row whose location attendance record of all rows is 2nd maximum . the date record of this row is january 12 .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'location attendance_5': 5, '2_6': 6, 'date_7': 7, 'january 12_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'location attendance_5': 'location attendance', '2_6': '2', 'date_7': 'date', 'january 12_8': 'january 12'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'location attendance_5': [0], '2_6': [0], 'date_7': [1], 'january 12_8': [2]}

['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']

[['29', 'january 2', 'la clippers', 'w 120 - 107', 'd wilkins ( 35 )', 'd wilkins ( 16 )', 'g rivers ( 11 )', 'omni coliseum 8733', '16 - 13'], ['30', 'january 4', 'indiana', 'w 111 - 96', 'd wilkins ( 36 )', 'm malone ( 11 )', 'g rivers , r robinson ( 5 )', 'omni coliseum 10124', '17 - 13'], ['31', 'january 5', 'minnesota', 'w 117 - 112 ( ot )', 'j battle ( 27 )', 'k willis ( 19 )', 'j koncak ( 7 )', 'omni coliseum 10988', '18 - 13'], ['32', 'january 8', 'san antonio', 'w 109 - 98', 'd wilkins ( 40 )', 'k willis ( 9 )', 'g rivers ( 10 )', 'omni coliseum 12608', '19 - 13'], ['33', 'january 11', 'chicago', 'l 96 - 99', 'd wilkins ( 23 )', 'd wilkins ( 12 )', 'j battle ( 7 )', 'chicago stadium 18676', '19 - 14'], ['34', 'january 12', 'new york', 'l 92 - 99', 'd wilkins ( 22 )', 'g rivers ( 8 )', 'g rivers , r robinson ( 6 )', 'madison square garden 17457', '19 - 15'], ['35', 'january 14', 'new york', 'w 96 - 82', 'd wilkins ( 26 )', 'd wilkins ( 16 )', 'j battle , s moncrief ( 4 )', 'omni coliseum 12612', '20 - 15'], ['36', 'january 15', 'indiana', 'w 117 - 106', 'd wilkins ( 28 )', 'd wilkins ( 12 )', 'j battle ( 8 )', 'market square arena 9531', '21 - 15'], ['37', 'january 18', 'chicago', 'w 114 - 105', 'd wilkins ( 34 )', 'm malone ( 12 )', 'g rivers ( 5 )', 'omni coliseum 16390', '22 - 15'], ['38', 'january 19', 'new jersey', 'w 114 - 84', 'k willis ( 24 )', 'k willis ( 17 )', 'a webb ( 6 )', 'omni coliseum 15758', '23 - 15'], ['39', 'january 22', 'miami', 'w 118 - 107', 'k willis ( 29 )', 'k willis ( 10 )', 'g rivers , a webb , d wilkins ( 7 )', 'omni coliseum 10440', '24 - 15'], ['40', 'january 23', 'washington', 'l 99 - 104', 'd wilkins ( 27 )', 'd wilkins ( 13 )', 'g rivers ( 7 )', 'capital centre 9830', '24 - 16'], ['41', 'january 26', 'seattle', 'l 102 - 103', 'd wilkins ( 43 )', 'd wilkins ( 10 )', 'a webb ( 9 )', 'seattle center coliseum 12792', '24 - 17'], ['42', 'january 28', 'portland', 'l 111 - 116', 'd wilkins ( 34 )', 'k willis ( 10 )', 'a webb ( 11 )', 'memorial coliseum 12884', '24 - 18'], ['43', 'january 29', 'utah', 'l 105 - 116', 'd wilkins ( 24 )', 'd wilkins ( 14 )', 'a webb ( 4 )', 'salt palace 12616', '24 - 19']]

united states house of representatives elections , 1946

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1946

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342233-3.html.csv

count

two of the incumbents in the election of 1946 for united states house of representatives , were first elected in 1944 .

{'scope': 'all', 'criterion': 'equal', 'value': '1944', 'result': '2', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'first elected', '1944'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose first elected record is equal to 1944 .', 'tostr': 'filter_eq { all_rows ; first elected ; 1944 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; first elected ; 1944 } }', 'tointer': 'select the rows whose first elected record is equal to 1944 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; first elected ; 1944 } } ; 2 } = true', 'tointer': 'select the rows whose first elected record is equal to 1944 . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; first elected ; 1944 } } ; 2 } = true

select the rows whose first elected record is equal to 1944 . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'first elected_5': 5, '1944_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'first elected_5': 'first elected', '1944_6': '1944', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'first elected_5': [0], '1944_6': [0], '2_7': [2]}

['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']

[['alabama 1', 'frank w boykin', 'democratic', '1935', 're - elected', 'frank w boykin ( d ) unopposed'], ['alabama 2', 'george m grant', 'democratic', '1938', 're - elected', 'george m grant ( d ) unopposed'], ['alabama 3', 'george w andrews', 'democratic', '1944', 're - elected', 'george w andrews ( d ) unopposed'], ['alabama 4', 'sam hobbs', 'democratic', '1934', 're - elected', 'sam hobbs ( d ) 88.1 % roger s bingham ( r ) 11.9 %'], ['alabama 5', 'albert rains', 'democratic', '1944', 're - elected', 'albert rains ( d ) unopposed'], ['alabama 6', 'pete jarman', 'democratic', '1936', 're - elected', 'pete jarman ( d ) unopposed'], ['alabama 7', 'carter manasco', 'democratic', '1941', 're - elected', 'carter manasco ( d ) 72.7 % m h woodward ( r ) 27.3 %'], ['alabama 8', 'john sparkman', 'democratic', '1936', 're - elected elected simultaneously to u s senate', 'john sparkman ( d ) 92.4 % arthur south ( r ) 7.6 %']]

alona bondarenko

https://en.wikipedia.org/wiki/Alona_Bondarenko

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1498593-3.html.csv

unique

hard ( i ) is the only surface used once by alona bondarenko .

{'scope': 'all', 'row': '3', 'col': '4', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'hard ( i )', 'subset': None}

{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'hard ( i )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to hard ( i ) .', 'tostr': 'filter_eq { all_rows ; surface ; hard ( i ) }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; surface ; hard ( i ) } } = true', 'tointer': 'select the rows whose surface record fuzzily matches to hard ( i ) . there is only one such row in the table .'}

only { filter_eq { all_rows ; surface ; hard ( i ) } } = true

select the rows whose surface record fuzzily matches to hard ( i ) . there is only one such row in the table .

2

2

{'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'surface_4': 4, 'hard (i)_5': 5}

{'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'surface_4': 'surface', 'hard (i)_5': 'hard ( i )'}

{'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'surface_4': [0], 'hard (i)_5': [0]}

['outcome', 'date', 'championship', 'surface', 'partner', 'opponent in the final', 'score in the final']

[['winner', '27 may 2006', 'istanbul , turkey', 'clay', 'anastasiya yakimova', 'sania mirza alicia molik', '6 - 2 , 6 - 4'], ['winner', '26 january 2008', 'melbourne , australia', 'hard', 'kateryna bondarenko', "victoria azarenka shahar pe'er", '2 - 6 , 6 - 1 , 6 - 4'], ['winner', '10 february 2008', 'paris , france', 'hard ( i )', 'kateryna bondarenko', 'vladimíra uhlířová eva hrdinová', '6 - 1 , 6 - 4'], ['runner - up', '16 january 2009', 'hobart , australia', 'hard', 'kateryna bondarenko', 'gisela dulko flavia pennetta', '2 - 6 , 6 - 7 ( 4 - 7 )'], ['runner - up', '6 july 2009', 'budapest , hungary', 'clay', 'kateryna bondarenko', 'alisa kleybanova monica niculescu', '4 - 6 , 6 - 7 ( 5 - 7 )'], ['winner', '13 july 2009', 'prague , czech republick', 'clay', 'kateryna bondarenko', 'iveta benešová barbora záhlavová - strýcová', '6 - 1 , 6 - 2']]

ugly betty ( season 4 )

https://en.wikipedia.org/wiki/Ugly_Betty_%28season_4%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22570439-1.html.csv

unique

in ugly betty season 4 , for the episodes that originally aired in march , the only one directed by andy wolk was the episode titled all the world 's a stage .

{'scope': 'subset', 'row': '16', 'col': '5', 'col_other': '3', 'criterion': 'equal', 'value': 'andy wolk', 'subset': {'col': '7', 'criterion': 'fuzzily_match', 'value': 'march'}}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'original air date', 'march'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; original air date ; march }', 'tointer': 'select the rows whose original air date record fuzzily matches to march .'}, 'directed by', 'andy wolk'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose original air date record fuzzily matches to march . among these rows , select the rows whose directed by record fuzzily matches to andy wolk .', 'tostr': 'filter_eq { filter_eq { all_rows ; original air date ; march } ; directed by ; andy wolk }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; original air date ; march } ; directed by ; andy wolk } }', 'tointer': 'select the rows whose original air date record fuzzily matches to march . among these rows , select the rows whose directed by record fuzzily matches to andy wolk . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'original air date', 'march'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; original air date ; march }', 'tointer': 'select the rows whose original air date record fuzzily matches to march .'}, 'directed by', 'andy wolk'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose original air date record fuzzily matches to march . among these rows , select the rows whose directed by record fuzzily matches to andy wolk .', 'tostr': 'filter_eq { filter_eq { all_rows ; original air date ; march } ; directed by ; andy wolk }'}, 'episode title'], 'result': "all the world 's a stage", 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; original air date ; march } ; directed by ; andy wolk } ; episode title }'}, "all the world 's a stage"], 'result': True, 'ind': 4, 'tostr': "eq { hop { filter_eq { filter_eq { all_rows ; original air date ; march } ; directed by ; andy wolk } ; episode title } ; all the world 's a stage }", 'tointer': "the episode title record of this unqiue row is all the world 's a stage ."}], 'result': True, 'ind': 5, 'tostr': "and { only { filter_eq { filter_eq { all_rows ; original air date ; march } ; directed by ; andy wolk } } ; eq { hop { filter_eq { filter_eq { all_rows ; original air date ; march } ; directed by ; andy wolk } ; episode title } ; all the world 's a stage } } = true", 'tointer': "select the rows whose original air date record fuzzily matches to march . among these rows , select the rows whose directed by record fuzzily matches to andy wolk . there is only one such row in the table . the episode title record of this unqiue row is all the world 's a stage ."}

and { only { filter_eq { filter_eq { all_rows ; original air date ; march } ; directed by ; andy wolk } } ; eq { hop { filter_eq { filter_eq { all_rows ; original air date ; march } ; directed by ; andy wolk } ; episode title } ; all the world 's a stage } } = true

select the rows whose original air date record fuzzily matches to march . among these rows , select the rows whose directed by record fuzzily matches to andy wolk . there is only one such row in the table . the episode title record of this unqiue row is all the world 's a stage .

8

6

{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'original air date_8': 8, 'march_9': 9, 'directed by_10': 10, 'andy wolk_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'episode title_12': 12, "all the world 's a stage_13": 13}

{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'original air date_8': 'original air date', 'march_9': 'march', 'directed by_10': 'directed by', 'andy wolk_11': 'andy wolk', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'episode title_12': 'episode title', "all the world 's a stage_13": "all the world 's a stage"}

{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'original air date_8': [0], 'march_9': [0], 'directed by_10': [1], 'andy wolk_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'episode title_12': [3], "all the world 's a stage_13": [4]}

['series', 'season', 'episode title', 'written by', 'directed by', 'us viewers ( millions )', 'original air date']

[['66', '1', 'the butterfly effect ( part 1 )', 'sheila lawrence & henry alonso myers', 'john terlesky', '5.01', 'october 16 , 2009'], ['67', '2', 'the butterfly effect ( part 2 )', 'sheila lawrence & henry alonso myers', 'victor nelli , jr', '5.18', 'october 16 , 2009'], ['68', '3', 'blue on blue', 'abraham higginbotham', 'victor nelli , jr', '4.55', 'october 23 , 2009'], ['69', '4', 'the weiner , the bun , and the boob', 'brian tanen', 'wendey stanzler', '4.50', 'october 30 , 2009'], ['70', '5', 'plus none', 'cara dipaolo', 'paul holahan', '4.76', 'november 6 , 2009'], ['71', '6', 'backseat betty', 'tracy poust & jon kinnally', 'john putch', '4.46', 'november 13 , 2009'], ['72', '7', 'level ( 7 ) with me', 'chris black', 'john fortenberry', '3.39', 'november 27 , 2009'], ['73', '8', 'the bahamas triangle', 'sheila lawrence', 'victor neili , jr', '4.23', 'december 4 , 2009'], ['74', '9', 'be - shure', 'gail lerner', 'david dworetzky', '4.80', 'december 11 , 2009'], ['75', '10', 'the passion of the betty', 'david grubstick & chris black', 'sj clarkson', '5.13', 'january 6 , 2010'], ['76', '11', 'back in her place', 'abraham higginbotham', 'richard heus', '4.67', 'january 13 , 2010'], ['77', '12', 'blackout !', 'cara dipoulo', 'john putch', '4.59', 'january 20 , 2010'], ['78', '13', 'chica and the man', 'gail lerner', 'victor nelli , jr', '4.34', 'february 3 , 2010'], ['79', '14', "smokin ' hot", 'brian tanen', 'john scott', '4.68', 'february 10 , 2010'], ['80', '15', 'fire and nice', 'erika johnson', 'john terlesky', '4.10', 'march 10 , 2010'], ['81', '16', "all the world 's a stage", 'abraham higginbotham & david grubstick', 'andy wolk', '3.33', 'march 17 , 2010'], ['82', '17', 'million dollar smile', 'henry alonso myers & chris black', 'paul holahan', '4.56', 'march 24 , 2010'], ['83', '18', 'london calling', 'david grubstick & sheila lawrence', 'mark worthington', '4.01', 'march 31 , 2010'], ['84', '19', 'the past presents the future', 'jon kinnaly & tracy poust', 'paul holahan', '4.03', 'april 7 , 2010']]

1935 masters tournament

https://en.wikipedia.org/wiki/1935_Masters_Tournament

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12586224-1.html.csv

aggregation

the average scores of the top 10 finishes in the 1935 masters tournament was 70 .

{'scope': 'all', 'col': '4', 'type': 'average', 'result': '70', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'score'], 'result': '70', 'ind': 0, 'tostr': 'avg { all_rows ; score }'}, '70'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; score } ; 70 } = true', 'tointer': 'the average of the score record of all rows is 70 .'}

round_eq { avg { all_rows ; score } ; 70 } = true

the average of the score record of all rows is 70 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'score_4': 4, '70_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'score_4': 'score', '70_5': '70'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'score_4': [0], '70_5': [1]}

['place', 'player', 'country', 'score', 'to par']

[['1', 'henry picard', 'united states', '67', '- 5'], ['t2', 'gene sarazen', 'united states', '68', '- 4'], ['t2', 'ray mangrum', 'united states', '68', '- 4'], ['t2', 'willie goggin', 'united states', '68', '- 4'], ['5', 'craig wood', 'united states', '69', '- 3'], ['t6', 'olin dutra', 'united states', '70', '- 2'], ['t6', 'jimmy hines', 'united states', '70', '- 2'], ['t6', 'johnny revolta', 'united states', '70', '- 2'], ['t6', 'paul runyan', 'united states', '70', '- 2'], ['t10', 'tony manero', 'united states', '71', '- 1'], ['t10', 'leo diegel', 'united states', '71', '- 1'], ['t10', 'willie klein', 'united states', '71', '- 1'], ['t10', 'mike turnesa', 'united states', '71', '- 1']]

list of greek episodes

https://en.wikipedia.org/wiki/List_of_Greek_episodes

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-12419515-4.html.csv

superlative

our fathers episode in the greek series has the most total viewers ( in millions ) .

{'scope': 'all', 'col_superlative': '7', 'row_superlative': '2', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '3', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'total viewers ( in millions )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; total viewers ( in millions ) }'}, 'title'], 'result': 'our fathers', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; total viewers ( in millions ) } ; title }'}, 'our fathers'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; total viewers ( in millions ) } ; title } ; our fathers } = true', 'tointer': 'select the row whose total viewers ( in millions ) record of all rows is maximum . the title record of this row is our fathers .'}

eq { hop { argmax { all_rows ; total viewers ( in millions ) } ; title } ; our fathers } = true

select the row whose total viewers ( in millions ) record of all rows is maximum . the title record of this row is our fathers .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'total viewers (in millions)_5': 5, 'title_6': 6, 'our fathers_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'total viewers (in millions)_5': 'total viewers ( in millions )', 'title_6': 'title', 'our fathers_7': 'our fathers'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'total viewers (in millions)_5': [0], 'title_6': [1], 'our fathers_7': [2]}

['series', 'season', 'title', 'directed by', 'written by', 'original air date', 'total viewers ( in millions )']

[['45', '1', 'the day after', 'michael lange', 'patrick sean smith', 'august 31 , 2009', '1.211'], ['46', '2', 'our fathers', 'patrick norris', 'jessica otoole & amy rardin', 'september 7 , 2009', '1.313'], ['47', '3', 'the half - naked gun', 'michael lange', 'roger grant', 'september 14 , 2009', 'n / a'], ['48', '4', 'high and dry', 'shawn piller', 'casey johnson', 'september 21 , 2009', 'n / a'], ['49', '5', 'down on your luck', 'michael lange', 'matt whitney', 'september 28 , 2009', 'n / a'], ['50', '6', 'lost and founders', 'fred gerber', 'michael berns', 'october 5 , 2009', 'n / a'], ['51', '7', 'the dork knight', 'rick rosenthal', 'adam milch', 'october 12 , 2009', 'n / a'], ['52', '8', 'fight the power', 'michael lange', 'jessica otoole & amy rardin', 'october 19 , 2009', 'n / a'], ['53', '9', 'the wish - pretzel', 'melanie mayron', 'lana cho & matt whitney', 'october 26 , 2009', 'n / a'], ['54', '10', 'friend or foe', 'michael lange', 'roger grant', 'november 2 , 2009', 'n / a'], ['55', '11', 'i know what you did last semester', 'michael lange', 'casey johnson & david windsor', 'january 25 , 2010', 'n / a'], ['56', '12', 'pride & punishment', 'john t kretchmer', 'jessica otoole & amy rardin', 'february 1 , 2010', 'n / a'], ['57', '13', 'take me out', 'lee rose', 'matt whitney', 'february 8 , 2010', 'n / a'], ['58', '14', 'the tortoise and the hair', 'michael lange', 'rob bragin', 'february 15 , 2010', 'n / a'], ['59', '15', 'love , actually , possibly , maybe or not', 'mark rosman', 'roger grant', 'february 22 , 2010', '0.872'], ['60', '16', 'your friends and neighbors', 'michael lange', 'dana greenblatt', 'march 1 , 2010', '0.937'], ['61', '17', 'the big easy does it', 'fred savage', 'casey johnson & david windsor', 'march 8 , 2010', '1.031'], ['62', '18', 'camp buy me love', 'michael lange', 'jessica otoole & amy rardin', 'march 15 , 2010', '0.820'], ['63', '19', 'the first last', 'patrick norris', 'roger grant & matt whitney', 'march 22 , 2010', 'n / a']]

swatch fivb world tour 2006

https://en.wikipedia.org/wiki/Swatch_FIVB_World_Tour_2006

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18395409-3.html.csv

aggregation

the total number of medals won by all the nations in the 2006 swatch fivb world tour was 87 .

{'scope': 'all', 'col': '6', 'type': 'sum', 'result': '87', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'total'], 'result': '87', 'ind': 0, 'tostr': 'sum { all_rows ; total }'}, '87'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; total } ; 87 } = true', 'tointer': 'the sum of the total record of all rows is 87 .'}

round_eq { sum { all_rows ; total } ; 87 } = true

the sum of the total record of all rows is 87 .

2

2

{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'total_4': 4, '87_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'total_4': 'total', '87_5': '87'}

{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'total_4': [0], '87_5': [1]}

['rank', 'nation', 'gold', 'silver', 'bronze', 'total']

[['1', 'brazil', '17', '18', '15', '50'], ['2', 'united states', '5', '5', '4', '14'], ['3', 'china', '4', '5', '5', '14'], ['4', 'germany', '2', '1', '3', '6'], ['5', 'switzerland', '1', '0', '0', '1'], ['6', 'netherlands', '0', '0', '1', '1'], ['6', 'norway', '0', '0', '1', '1']]

nature of america

https://en.wikipedia.org/wiki/Nature_of_America

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15635768-1.html.csv

ordinal

the alpine tundra ecosystem series of nature of america stamps has the fourth highest face value .

{'row': '9', 'col': '5', 'order': '4', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'face value', '4'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; face value ; 4 }'}, 'ecosystem'], 'result': 'alpine tundra', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; face value ; 4 } ; ecosystem }'}, 'alpine tundra'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; face value ; 4 } ; ecosystem } ; alpine tundra } = true', 'tointer': 'select the row whose face value record of all rows is 4th maximum . the ecosystem record of this row is alpine tundra .'}

eq { hop { nth_argmax { all_rows ; face value ; 4 } ; ecosystem } ; alpine tundra } = true

select the row whose face value record of all rows is 4th maximum . the ecosystem record of this row is alpine tundra .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'face value_5': 5, '4_6': 6, 'ecosystem_7': 7, 'alpine tundra_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'face value_5': 'face value', '4_6': '4', 'ecosystem_7': 'ecosystem', 'alpine tundra_8': 'alpine tundra'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'face value_5': [0], '4_6': [0], 'ecosystem_7': [1], 'alpine tundra_8': [2]}

['ecosystem', 'date of issue', 'place of issue', 'no stamps in sheet', 'face value', 'printer']

[['sonoran desert', 'april 6 , 1999', 'tucson , arizona', '10', '33', 'banknote corporation of america'], ['pacific coast rain forest', 'march 28 , 2000', 'seattle , washington', '10', '33', 'banknote corporation of america'], ['great plains prairie', 'march 29 , 2001', 'lincoln , nebraska', '10', '34', 'ashton - potter ( usa ) ltd'], ['longleaf pine forest', 'april 26 , 2002', 'tallahassee , florida', '10', '34', 'american packaging corp for sennet security'], ['arctic tundra', 'july 1 , 2003', 'fairbanks , alaska', '10', '37', 'banknote corporation of america'], ['pacific coral reef', 'jan 2 , 2004', 'honolulu , hawaii', '10', '37', 'avery dennison'], ['northeast deciduous forest', 'march 3 , 2005', 'new york , new york', '10', '37', 'avery dennison'], ['southern florida wetland', 'october 5 , 2006', 'naples , florida', '10', '39', 'avery dennison'], ['alpine tundra', 'august 28 , 2007', 'estes park , colorado', '10', '41', 'sennett security products'], ['great lakes dunes', 'october 2 , 2008', 'empire , michigan', '10', '42', 'avery dennison'], ['kelp forest', 'october 1 , 2009', 'monterey , california', '10', '44', 'avery dennison'], ['hawaiian rain forest', 'september 1 , 2010', 'hawaii national park', '10', '44', 'banknote corporation of america , inc']]

24th united states congress

https://en.wikipedia.org/wiki/24th_United_States_Congress

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-225200-4.html.csv

comparative

hopkins holsey was seated as a successor earlier than john young in the 24th united states congress .

{'row_1': '3', 'row_2': '12', 'col': '5', 'col_other': '4', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'successor', 'hopkins holsey ( j )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose successor record fuzzily matches to hopkins holsey ( j ) .', 'tostr': 'filter_eq { all_rows ; successor ; hopkins holsey ( j ) }'}, 'date successor seated'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; successor ; hopkins holsey ( j ) } ; date successor seated }', 'tointer': 'select the rows whose successor record fuzzily matches to hopkins holsey ( j ) . take the date successor seated record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'successor', 'john young ( aj )'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose successor record fuzzily matches to john young ( aj ) .', 'tostr': 'filter_eq { all_rows ; successor ; john young ( aj ) }'}, 'date successor seated'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; successor ; john young ( aj ) } ; date successor seated }', 'tointer': 'select the rows whose successor record fuzzily matches to john young ( aj ) . take the date successor seated record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; successor ; hopkins holsey ( j ) } ; date successor seated } ; hop { filter_eq { all_rows ; successor ; john young ( aj ) } ; date successor seated } } = true', 'tointer': 'select the rows whose successor record fuzzily matches to hopkins holsey ( j ) . take the date successor seated record of this row . select the rows whose successor record fuzzily matches to john young ( aj ) . take the date successor seated record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; successor ; hopkins holsey ( j ) } ; date successor seated } ; hop { filter_eq { all_rows ; successor ; john young ( aj ) } ; date successor seated } } = true

select the rows whose successor record fuzzily matches to hopkins holsey ( j ) . take the date successor seated record of this row . select the rows whose successor record fuzzily matches to john young ( aj ) . take the date successor seated record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'successor_7': 7, 'hopkins holsey ( j )_8': 8, 'date successor seated_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'successor_11': 11, 'john young ( aj )_12': 12, 'date successor seated_13': 13}

{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'successor_7': 'successor', 'hopkins holsey ( j )_8': 'hopkins holsey ( j )', 'date successor seated_9': 'date successor seated', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'successor_11': 'successor', 'john young ( aj )_12': 'john young ( aj )', 'date successor seated_13': 'date successor seated'}

{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'successor_7': [0], 'hopkins holsey ( j )_8': [0], 'date successor seated_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'successor_11': [1], 'john young ( aj )_12': [1], 'date successor seated_13': [3]}

['district', 'vacator', 'reason for change', 'successor', 'date successor seated']

[['south carolina 6th', 'vacant', 'rep warren r davis died during previous congress', 'waddy thompson , jr ( aj )', 'seated september 10 , 1835'], ['georgia at - large', 'vacant', 'rep james m wayne resigned in previous congress', 'jabez y jackson ( j )', 'seated october 5 , 1835'], ['georgia at - large', 'james c terrell ( j )', 'resigned july 8 , 1835 due to ill health', 'hopkins holsey ( j )', 'seated october 5 , 1835'], ['connecticut at - large', 'zalmon wildman ( j )', 'died december 10 , 1835', 'thomas t whittlesey ( j )', 'seated april 29 , 1836'], ['pennsylvania 24th', 'john banks ( am )', 'resigned sometime in 1836', 'john j pearson ( aj )', 'seated december 5 , 1836'], ['south carolina 4th', 'james h hammond ( n )', 'resigned february 26 , 1836 because of ill health', 'franklin h elmore ( n )', 'seated december 10 , 1836'], ['new york 17th', 'samuel beardsley ( j )', 'resigned march 29 , 1836', 'rutger b miller ( j )', 'seated november 9 , 1836'], ['north carolina 12th', 'james graham ( aj )', 'seat declared vacant march 29 , 1836', 'james graham ( aj', 'seated december 5 , 1836'], ['south carolina 8th', 'richard i manning ( j )', 'died may 1 , 1836', 'john p richardson ( j )', 'seated december 19 , 1836'], ['mississippi at - large', 'david dickson ( aj )', 'died july 31 , 1836', 'samuel j gholson ( j )', 'seated december 1 , 1836'], ['georgia at - large', 'george w towns ( j )', 'resigned september 1 , 1836', 'julius c alford ( aj )', 'seated january 2 , 1837'], ['new york 30th', 'philo c fuller ( aj )', 'resigned september 2 , 1836', 'john young ( aj )', 'seated november 9 , 1836'], ['georgia at - large', 'john e coffee ( j )', 'died september 25 , 1836', 'william c dawson ( aj )', 'seated november 7 , 1836'], ['pennsylvania 13th', 'jesse miller ( j )', 'resigned october 30 , 1836', 'james black ( j )', 'seated december 5 , 1836'], ['indiana 6th', 'george l kinnard ( j )', 'died november 26 , 1836', 'william herod ( aj )', 'seated january 25 , 1837'], ['virginia 2nd', 'john y mason ( j )', 'resigned january 11 , 1837', 'vacant', 'not filled this congress']]

japanese house of councillors election , 2001

https://en.wikipedia.org/wiki/Japanese_House_of_Councillors_election%2C_2001

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10124546-1.html.csv

ordinal

the democratic party won the second most seats in 2001 .

{'row': '2', 'col': '4', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'total elected 2001', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; total elected 2001 ; 2 }'}, 'party'], 'result': 'democratic party', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; total elected 2001 ; 2 } ; party }'}, 'democratic party'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; total elected 2001 ; 2 } ; party } ; democratic party } = true', 'tointer': 'select the row whose total elected 2001 record of all rows is 2nd maximum . the party record of this row is democratic party .'}

eq { hop { nth_argmax { all_rows ; total elected 2001 ; 2 } ; party } ; democratic party } = true

select the row whose total elected 2001 record of all rows is 2nd maximum . the party record of this row is democratic party .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'total elected 2001_5': 5, '2_6': 6, 'party_7': 7, 'democratic party_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'total elected 2001_5': 'total elected 2001', '2_6': '2', 'party_7': 'party', 'democratic party_8': 'democratic party'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'total elected 2001_5': [0], '2_6': [0], 'party_7': [1], 'democratic party_8': [2]}

['party', 'pr seats', 'district seats', 'total elected 2001', 'total seats']

[['liberal democratic party', '20', '45', '65', '111'], ['democratic party', '8', '18', '26', '59'], ['new komeito party', '8', '5', '13', '23'], ['liberal party', '4', '2', '6', '8'], ['communist party', '4', '1', '5', '20'], ['social democratic party', '3', '0', '3', '8'], ['new conservative party', '1', '0', '1', '5'], ['others', '0', '2', '2', '2'], ['independents', '0', '0', '0', '4'], ['total', '48', '73', '121', '247']]

1965 belgian grand prix

https://en.wikipedia.org/wiki/1965_Belgian_Grand_Prix

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1122334-1.html.csv

count

of the teams in the 1965 belgian grand prix that completed fewer than 25 laps , two had ignition problems .

{'scope': 'subset', 'criterion': 'equal', 'value': 'ignition', 'result': '2', 'col': '4', 'subset': {'col': '3', 'criterion': 'less_than', 'value': '25'}}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'laps', '25'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; laps ; 25 }', 'tointer': 'select the rows whose laps record is less than 25 .'}, 'time / retired', 'ignition'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose laps record is less than 25 . among these rows , select the rows whose time / retired record fuzzily matches to ignition .', 'tostr': 'filter_eq { filter_less { all_rows ; laps ; 25 } ; time / retired ; ignition }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_less { all_rows ; laps ; 25 } ; time / retired ; ignition } }', 'tointer': 'select the rows whose laps record is less than 25 . among these rows , select the rows whose time / retired record fuzzily matches to ignition . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_less { all_rows ; laps ; 25 } ; time / retired ; ignition } } ; 2 } = true', 'tointer': 'select the rows whose laps record is less than 25 . among these rows , select the rows whose time / retired record fuzzily matches to ignition . the number of such rows is 2 .'}

eq { count { filter_eq { filter_less { all_rows ; laps ; 25 } ; time / retired ; ignition } } ; 2 } = true

select the rows whose laps record is less than 25 . among these rows , select the rows whose time / retired record fuzzily matches to ignition . the number of such rows is 2 .

4

4

{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_less_0': 0, 'all_rows_5': 5, 'laps_6': 6, '25_7': 7, 'time / retired_8': 8, 'ignition_9': 9, '2_10': 10}

{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_less_0': 'filter_less', 'all_rows_5': 'all_rows', 'laps_6': 'laps', '25_7': '25', 'time / retired_8': 'time / retired', 'ignition_9': 'ignition', '2_10': '2'}

{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_less_0': [1], 'all_rows_5': [0], 'laps_6': [0], '25_7': [0], 'time / retired_8': [1], 'ignition_9': [1], '2_10': [3]}

['driver', 'constructor', 'laps', 'time / retired', 'grid']

[['jim clark', 'lotus - climax', '32', '2:23:34.8', '2'], ['jackie stewart', 'brm', '32', '+ 44.8 secs', '3'], ['bruce mclaren', 'cooper - climax', '31', '+ 1 lap', '9'], ['jack brabham', 'brabham - climax', '31', '+ 1 lap', '10'], ['graham hill', 'brm', '31', '+ 1 lap', '1'], ['richie ginther', 'honda', '31', '+ 1 lap', '4'], ['mike spence', 'lotus - climax', '31', '+ 1 lap', '12'], ['jo siffert', 'brabham - brm', '31', '+ 1 lap', '8'], ['lorenzo bandini', 'ferrari', '30', '+ 2 laps', '15'], ['dan gurney', 'brabham - climax', '30', '+ 2 laps', '5'], ['jochen rindt', 'cooper - climax', '29', '+ 3 laps', '14'], ['lucien bianchi', 'brm', '29', '+ 3 laps', '17'], ['innes ireland', 'lotus - brm', '27', '+ 5 laps', '16'], ['richard attwood', 'lotus - brm', '26', 'accident', '13'], ['masten gregory', 'brm', '12', 'fuel pump', '20'], ['jo bonnier', 'brabham - climax', '9', 'ignition', '7'], ['ronnie bucknum', 'honda', '9', 'gearbox', '11'], ['john surtees', 'ferrari', '5', 'engine', '6'], ['frank gardner', 'brabham - brm', '3', 'ignition', '19']]

1996 - 97 philadelphia flyers season

https://en.wikipedia.org/wiki/1996%E2%80%9397_Philadelphia_Flyers_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14208850-4.html.csv

count

the philadelphia flyers played against the hartford whalers twice .

{'scope': 'all', 'criterion': 'equal', 'value': 'hartford whalers', 'result': '2', 'col': '3', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'hartford whalers'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to hartford whalers .', 'tostr': 'filter_eq { all_rows ; opponent ; hartford whalers }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; opponent ; hartford whalers } }', 'tointer': 'select the rows whose opponent record fuzzily matches to hartford whalers . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; opponent ; hartford whalers } } ; 2 } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to hartford whalers . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; opponent ; hartford whalers } } ; 2 } = true

select the rows whose opponent record fuzzily matches to hartford whalers . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'opponent_5': 5, 'hartford whalers_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'opponent_5': 'opponent', 'hartford whalers_6': 'hartford whalers', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'opponent_5': [0], 'hartford whalers_6': [0], '2_7': [2]}

['game', 'december', 'opponent', 'score', 'record', 'points']

[['27', '1', 'vancouver canucks', '4 - 3', '14 - 12 - 1', '29'], ['28', '4', 'new york rangers', '1 - 1 ot', '14 - 12 - 2', '30'], ['29', '6', 'dallas stars', '6 - 3', '15 - 12 - 2', '32'], ['30', '10', 'florida panthers', '5 - 4', '16 - 12 - 2', '34'], ['31', '12', 'hartford whalers', '3 - 2', '17 - 12 - 2', '36'], ['32', '14', 'hartford whalers', '4 - 0', '18 - 12 - 2', '38'], ['33', '15', 'boston bruins', '6 - 0', '19 - 12 - 2', '40'], ['34', '19', 'new york islanders', '5 - 0', '20 - 12 - 2', '42'], ['35', '21', 'st louis blues', '4 - 0', '21 - 12 - 2', '44'], ['36', '22', 'chicago blackhawks', '2 - 2 ot', '21 - 12 - 3', '45'], ['37', '27', 'edmonton oilers', '6 - 4', '22 - 12 - 3', '47'], ['38', '29', 'calgary flames', '4 - 2', '23 - 12 - 3', '49'], ['39', '31', 'vancouver canucks', '5 - 3', '24 - 12 - 3', '51']]

list of cities , towns and villages in vojvodina

https://en.wikipedia.org/wiki/List_of_cities%2C_towns_and_villages_in_Vojvodina

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2562572-19.html.csv

comparative

in the list of cities , towns and villages in vojvodina , gardinovci has a larger population than lok .

{'row_1': '2', 'row_2': '3', 'col': '4', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'settlement', 'gardinovci'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose settlement record fuzzily matches to gardinovci .', 'tostr': 'filter_eq { all_rows ; settlement ; gardinovci }'}, 'population ( 2011 )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; settlement ; gardinovci } ; population ( 2011 ) }', 'tointer': 'select the rows whose settlement record fuzzily matches to gardinovci . take the population ( 2011 ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'settlement', 'lok'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose settlement record fuzzily matches to lok .', 'tostr': 'filter_eq { all_rows ; settlement ; lok }'}, 'population ( 2011 )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; settlement ; lok } ; population ( 2011 ) }', 'tointer': 'select the rows whose settlement record fuzzily matches to lok . take the population ( 2011 ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; settlement ; gardinovci } ; population ( 2011 ) } ; hop { filter_eq { all_rows ; settlement ; lok } ; population ( 2011 ) } } = true', 'tointer': 'select the rows whose settlement record fuzzily matches to gardinovci . take the population ( 2011 ) record of this row . select the rows whose settlement record fuzzily matches to lok . take the population ( 2011 ) record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; settlement ; gardinovci } ; population ( 2011 ) } ; hop { filter_eq { all_rows ; settlement ; lok } ; population ( 2011 ) } } = true

select the rows whose settlement record fuzzily matches to gardinovci . take the population ( 2011 ) record of this row . select the rows whose settlement record fuzzily matches to lok . take the population ( 2011 ) record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'settlement_7': 7, 'gardinovci_8': 8, 'population (2011)_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'settlement_11': 11, 'lok_12': 12, 'population (2011)_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'settlement_7': 'settlement', 'gardinovci_8': 'gardinovci', 'population (2011)_9': 'population ( 2011 )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'settlement_11': 'settlement', 'lok_12': 'lok', 'population (2011)_13': 'population ( 2011 )'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'settlement_7': [0], 'gardinovci_8': [0], 'population (2011)_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'settlement_11': [1], 'lok_12': [1], 'population (2011)_13': [3]}

['settlement', 'cyrillic name', 'type', 'population ( 2011 )', 'largest ethnic group ( 2002 )', 'dominant religion ( 2002 )']

[['titel', 'тител', 'town', '5294', 'serbs', 'orthodox christianity'], ['gardinovci', 'гардиновци', 'village', '1297', 'serbs', 'orthodox christianity'], ['lok', 'лок', 'village', '1114', 'serbs', 'orthodox christianity'], ['mošorin', 'мошорин', 'village', '2569', 'serbs', 'orthodox christianity'], ['šajkaš', 'шајкаш', 'village', '4374', 'serbs', 'orthodox christianity']]

united states house of representatives elections , 1962

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1962

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341884-12.html.csv

majority

most of the incumbents in the 1962 united states elections of the house of representatives for georgia ran unopposed .

{'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'unopposed', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'candidates', 'unopposed'], 'result': True, 'ind': 0, 'tointer': 'for the candidates records of all rows , most of them fuzzily match to unopposed .', 'tostr': 'most_eq { all_rows ; candidates ; unopposed } = true'}

most_eq { all_rows ; candidates ; unopposed } = true

for the candidates records of all rows , most of them fuzzily match to unopposed .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'candidates_3': 3, 'unopposed_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'candidates_3': 'candidates', 'unopposed_4': 'unopposed'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'candidates_3': [0], 'unopposed_4': [0]}

['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']

[['georgia 1', 'george elliott hagan', 'democratic', '1960', 're - elected', 'george elliott hagan ( d ) unopposed'], ['georgia 2', 'j l pilcher', 'democratic', '1953', 're - elected', 'j l pilcher ( d ) unopposed'], ['georgia 3', 'tic forrester', 'democratic', '1950', 're - elected', 'tic forrester ( d ) unopposed'], ['georgia 4', 'john james flynt , jr', 'democratic', '1954', 're - elected', 'john james flynt , jr ( d ) unopposed'], ['georgia 6', 'carl vinson', 'democratic', '1914', 're - elected', 'carl vinson ( d ) unopposed'], ['georgia 7', 'john w davis', 'democratic', '1960', 're - elected', 'john w davis ( d ) 72.4 % e ralph ivey ( r ) 27.6 %'], ['georgia 8', 'iris faircloth blitch', 'democratic', '1954', 'retired democratic hold', 'j russell tuten ( d ) unopposed'], ['georgia 9', 'phillip m landrum', 'democratic', '1952', 're - elected', 'phillip m landrum ( d ) unopposed']]

automobiles gonfaronnaises sportives

https://en.wikipedia.org/wiki/Automobiles_Gonfaronnaises_Sportives

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226665-1.html.csv

majority

the majority of the chasis in automobiles gonfaronnaises sportives between 1986 and 1991 uses g tyres .

{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'g', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'tyres', 'g'], 'result': True, 'ind': 0, 'tointer': 'for the tyres records of all rows , most of them fuzzily match to g .', 'tostr': 'most_eq { all_rows ; tyres ; g } = true'}

most_eq { all_rows ; tyres ; g } = true

for the tyres records of all rows , most of them fuzzily match to g .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'tyres_3': 3, 'g_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'tyres_3': 'tyres', 'g_4': 'g'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'tyres_3': [0], 'g_4': [0]}

['year', 'chassis', 'engine', 'tyres', 'points']

[['1986', 'ags jh21c', 'motori moderni 615 - 90 v6 ( t / c )', 'p', '0'], ['1987', 'ags jh22', 'ford dfz v8', 'g', '1'], ['1988', 'ags jh23', 'ford dfz v8', 'g', '0'], ['1989', 'ags jh23b ags jh24', 'ford dfr v8', 'g', '1'], ['1990', 'ags jh24 ags jh25', 'ford dfr v8', 'g', '0'], ['1991', 'ags jh25b ags jh27', 'ford dfr v8', 'g', '0']]

b " rqw women 's championship "

https://en.wikipedia.org/wiki/RQW_Women%27s_Championship

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18963089-2.html.csv

count

four of the rqw women 's championship events were held in norfolk .

{'scope': 'all', 'criterion': 'equal', 'value': 'norfolk', 'result': '4', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'norfolk'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to norfolk .', 'tostr': 'filter_eq { all_rows ; location ; norfolk }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location ; norfolk } }', 'tointer': 'select the rows whose location record fuzzily matches to norfolk . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location ; norfolk } } ; 4 } = true', 'tointer': 'select the rows whose location record fuzzily matches to norfolk . the number of such rows is 4 .'}

eq { count { filter_eq { all_rows ; location ; norfolk } } ; 4 } = true

select the rows whose location record fuzzily matches to norfolk . the number of such rows is 4 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'location_5': 5, 'norfolk_6': 6, '4_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'location_5': 'location', 'norfolk_6': 'norfolk', '4_7': '4'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location_5': [0], 'norfolk_6': [0], '4_7': [2]}

['wrestlers', 'reign', 'days held', 'location', 'event']

[['erin angel', '1', '111', 'eastleigh , hampshire', 'a night of champions'], ['vacated', '-', '-', '-', '-'], ['eden black', '1', '302', 'horndean , portsmouth', 'summer brawl 2006'], ['wesna', '1', '392', 'live event', 'a night of champions'], ['sweet saraya', '1', '225', 'vienna , austria', 'wrestling weltmeisterschaft'], ['jetta', '1', '300', 'great yarmouth , norfolk', 'waw 15th anniversary'], ['britani knight', '1', '700', 'takeley , essex', 'hew final fight : the christmas spectacular'], ['queen maya', '1', '491', 'costessey , norfolk', 'bellatrix 2'], ['liberty', '1', '196', 'norwich , norfolk', 'bellatrix 5'], ['sammi baynz', '1', '118', 'norwich , norfolk', 'bellatrix 7 - bellatrix vs shimmer']]

1980 tampa bay buccaneers season

https://en.wikipedia.org/wiki/1980_Tampa_Bay_Buccaneers_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11406866-2.html.csv

ordinal

the attendance at the third game the tampa bay buccaneers won was 51925 .

{'scope': 'subset', 'row': '9', 'col': '2', 'order': '3', 'col_other': '7', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '4', 'criterion': 'fuzzily_match', 'value': 'w'}}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'w'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; result ; w }', 'tointer': 'select the rows whose result record fuzzily matches to w .'}, 'date', '3'], 'result': None, 'ind': 1, 'tostr': 'nth_argmin { filter_eq { all_rows ; result ; w } ; date ; 3 }'}, 'attendance'], 'result': '51925', 'ind': 2, 'tostr': 'hop { nth_argmin { filter_eq { all_rows ; result ; w } ; date ; 3 } ; attendance }'}, '51925'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmin { filter_eq { all_rows ; result ; w } ; date ; 3 } ; attendance } ; 51925 } = true', 'tointer': 'select the rows whose result record fuzzily matches to w . select the row whose date record of these rows is 3rd minimum . the attendance record of this row is 51925 .'}

eq { hop { nth_argmin { filter_eq { all_rows ; result ; w } ; date ; 3 } ; attendance } ; 51925 } = true

select the rows whose result record fuzzily matches to w . select the row whose date record of these rows is 3rd minimum . the attendance record of this row is 51925 .

4

4

{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'result_6': 6, 'w_7': 7, 'date_8': 8, '3_9': 9, 'attendance_10': 10, '51925_11': 11}

{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmin_1': 'nth_argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'result_6': 'result', 'w_7': 'w', 'date_8': 'date', '3_9': '3', 'attendance_10': 'attendance', '51925_11': '51925'}

{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'result_6': [0], 'w_7': [0], 'date_8': [1], '3_9': [1], 'attendance_10': [2], '51925_11': [3]}

['week', 'date', 'opponent', 'result', 'kickoff', 'game site', 'attendance', 'record']

[['week', 'date', 'opponent', 'result', 'kickoff', 'game site', 'attendance', 'record'], ['1', 'september 7 , 1980', 'cincinnati bengals', 'w 17 - 12', '1:00', 'riverfront stadium', '35551', '1 - 0'], ['2', 'september 11 , 1980', 'los angeles rams', 'w 10 - 9', '9:00', 'tampa stadium', '66576', '2 - 0'], ['3', 'september 21 , 1980', 'dallas cowboys', 'l 28 - 17', '4:00', 'texas stadium', '62750', '2 - 1'], ['4', 'september 28 , 1980', 'cleveland browns', 'l 34 - 27', '1:00', 'tampa stadium', '65540', '2 - 2'], ['5', 'october 6 , 1980', 'chicago bears', 'l 23 - 0', '9:00', 'soldier field', '61350', '2 - 3'], ['6', 'october 12 , 1980', 'green bay packers', 't 14 - 14 ot', '1:00', 'tampa stadium', '64854', '2 - 3 - 1'], ['7', 'october 19 , 1980', 'houston oilers', 'l 20 - 14', '4:00', 'houston astrodome', '48167', '2 - 4 - 1'], ['8', 'october 26 , 1980', 'san francisco 49ers', 'w 24 - 23', '4:00', 'candlestick park', '51925', '3 - 4 - 1'], ['9', 'november 2 , 1980', 'new york giants', 'w 30 - 13', '1:00', 'tampa stadium', '68256', '4 - 4 - 1'], ['10', 'november 9 , 1980', 'pittsburgh steelers', 'l 24 - 21', '1:00', 'tampa stadium', '71636', '4 - 5 - 1'], ['11', 'november 16 , 1980', 'minnesota vikings', 'l 38 - 30', '2:00', 'metropolitan stadium', '46032', '4 - 6 - 1'], ['12', 'november 23 , 1980', 'detroit lions', 'l 24 - 10', '1:00', 'tampa stadium', '64976', '4 - 7 - 1'], ['13', 'november 30 , 1980', 'green bay packers', 'w 20 - 17', '2:00', 'milwaukee county stadium', '54225', '5 - 7 - 1'], ['14', 'december 7 , 1980', 'minnesota vikings', 'l 21 - 10', '1:00', 'tampa stadium', '65649', '5 - 8 - 1'], ['15', 'december 14 , 1980', 'detroit lions', 'l 27 - 14', '4:00', 'pontiac silverdome', '77098', '5 - 9 - 1'], ['16', 'december 20 , 1980', 'chicago bears', 'l 14 - 13', '4:00', 'tampa stadium', '55298', '5 - 10 - 1']]

2010 red bull motogp rookies cup season

https://en.wikipedia.org/wiki/2010_Red_Bull_MotoGP_Rookies_Cup_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28925058-1.html.csv

unique

the italian grand prix is the only one to have only one round on one day .

{'scope': 'all', 'row': '3', 'col': '3', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'italian grand prix', 'subset': None}

{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'grand prix', 'italian grand prix'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose grand prix record fuzzily matches to italian grand prix .', 'tostr': 'filter_eq { all_rows ; grand prix ; italian grand prix }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; grand prix ; italian grand prix } } = true', 'tointer': 'select the rows whose grand prix record fuzzily matches to italian grand prix . there is only one such row in the table .'}

only { filter_eq { all_rows ; grand prix ; italian grand prix } } = true

select the rows whose grand prix record fuzzily matches to italian grand prix . there is only one such row in the table .

2

2

{'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'grand prix_4': 4, 'italian grand prix_5': 5}

{'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'grand prix_4': 'grand prix', 'italian grand prix_5': 'italian grand prix'}

{'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'grand prix_4': [0], 'italian grand prix_5': [0]}

['round', 'date', 'grand prix', 'circuit', 'pole position', 'fastest lap', 'race winner']

[['1', 'may 1', 'spanish grand prix', 'jerez', 'daijiro hiura', 'daniel ruiz', 'danny kent'], ['1', 'may 2', 'spanish grand prix', 'jerez', 'daijiro hiura', 'daniel ruiz', 'daniel ruiz'], ['2', 'june 5', 'italian grand prix', 'mugello circuit', 'daniel ruiz', 'kevin calia', 'daijiro hiura'], ['3', 'june 25', 'dutch tt', 'tt circuit assen', 'daijiro hiura', 'kevin calia', 'jacob gagne'], ['3', 'june 26', 'dutch tt', 'tt circuit assen', 'daijiro hiura', 'kevin calia', 'daniel ruiz'], ['4', 'july 17', 'german grand prix', 'sachsenring', 'jacob gagne', 'alexander kristiansson', 'jacob gagne'], ['4', 'july 18', 'german grand prix', 'sachsenring', 'jacob gagne', 'danny kent', 'jacob gagne'], ['5', 'august 14', 'czech republic grand prix', 'brno', 'alejandro pardo', 'daniel ruiz', 'kevin calia'], ['5', 'august 15', 'czech republic grand prix', 'brno', 'alejandro pardo', 'harry stafford', 'jacob gagne']]

blue ridge hockey conference

https://en.wikipedia.org/wiki/Blue_Ridge_Hockey_Conference

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16404837-3.html.csv

majority

most of the schools in the blue ridge hockey conference were founded prior to 1900 .

{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '1900', 'subset': None}

{'func': 'most_less', 'args': ['all_rows', 'founded', '1900'], 'result': True, 'ind': 0, 'tointer': 'for the founded records of all rows , most of them are less than 1900 .', 'tostr': 'most_less { all_rows ; founded ; 1900 } = true'}

most_less { all_rows ; founded ; 1900 } = true

for the founded records of all rows , most of them are less than 1900 .

1

1

{'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'founded_3': 3, '1900_4': 4}

{'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'founded_3': 'founded', '1900_4': '1900'}

{'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'founded_3': [0], '1900_4': [0]}

['school', 'location', 'founded', 'affiliation', 'nickname']

[['american university', 'washington dc', '1893', 'private / methodist', 'eagles'], ['catholic university', 'washington dc', '1887', 'private / roman catholic', 'cardinals'], ['george mason university', 'fairfax , va', '1957', 'public', 'patriots'], ['university of maryland', 'college park , md', '1856', 'public flagship ( university system of maryland )', 'terrapins'], ['northern virginia community college', 'annandale , va', '1964', 'community college', 'raiders'], ['college of william & mary', 'williamsburg , va', '1693', 'public', 'tribe']]

pirveli liga

https://en.wikipedia.org/wiki/Pirveli_Liga

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18009885-2.html.csv

comparative

the mikheil meskhi stadium has a higher seating capacity than the sasha kvaratskhelia stadium .

{'row_1': '15', 'row_2': '13', 'col': '5', 'col_other': '4', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'stadium', 'mikheil meskhi stadium'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose stadium record fuzzily matches to mikheil meskhi stadium .', 'tostr': 'filter_eq { all_rows ; stadium ; mikheil meskhi stadium }'}, 'capacity'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; stadium ; mikheil meskhi stadium } ; capacity }', 'tointer': 'select the rows whose stadium record fuzzily matches to mikheil meskhi stadium . take the capacity record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'stadium', 'sasha kvaratskhelia stadium'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose stadium record fuzzily matches to sasha kvaratskhelia stadium .', 'tostr': 'filter_eq { all_rows ; stadium ; sasha kvaratskhelia stadium }'}, 'capacity'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; stadium ; sasha kvaratskhelia stadium } ; capacity }', 'tointer': 'select the rows whose stadium record fuzzily matches to sasha kvaratskhelia stadium . take the capacity record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; stadium ; mikheil meskhi stadium } ; capacity } ; hop { filter_eq { all_rows ; stadium ; sasha kvaratskhelia stadium } ; capacity } } = true', 'tointer': 'select the rows whose stadium record fuzzily matches to mikheil meskhi stadium . take the capacity record of this row . select the rows whose stadium record fuzzily matches to sasha kvaratskhelia stadium . take the capacity record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; stadium ; mikheil meskhi stadium } ; capacity } ; hop { filter_eq { all_rows ; stadium ; sasha kvaratskhelia stadium } ; capacity } } = true

select the rows whose stadium record fuzzily matches to mikheil meskhi stadium . take the capacity record of this row . select the rows whose stadium record fuzzily matches to sasha kvaratskhelia stadium . take the capacity record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'stadium_7': 7, 'mikheil meskhi stadium_8': 8, 'capacity_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'stadium_11': 11, 'sasha kvaratskhelia stadium_12': 12, 'capacity_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'stadium_7': 'stadium', 'mikheil meskhi stadium_8': 'mikheil meskhi stadium', 'capacity_9': 'capacity', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'stadium_11': 'stadium', 'sasha kvaratskhelia stadium_12': 'sasha kvaratskhelia stadium', 'capacity_13': 'capacity'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'stadium_7': [0], 'mikheil meskhi stadium_8': [0], 'capacity_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'stadium_11': [1], 'sasha kvaratskhelia stadium_12': [1], 'capacity_13': [3]}

['clubs', 'position 2010 - 11', 'region', 'stadium', 'capacity']

[['samtredia', 'umaglesi liga', 'imereti', 'erosi manjgaladze stadium', '15000'], ['chikhura sachkhere', '4', 'imereti', 'tsentral stadium ( sachkhere )', '2000'], ['dinamo batumi', '5', 'adjara', 'batumi stadium', '30000'], ['guria lanchkhuti', '6', 'guria', 'evgrapi shevardnadze stadium', '22000'], ['kolkheti khobi', '7', 'samegrelo', 'tsentral stadium ( khobi )', '12000'], ['imereti khoni', '8', 'imereti', 'tsentral stadium ( khoni )', '2000'], ['meshakhte tkibuli', '9', 'imereti', 'vladimer bochorishvili stadium', '11700'], ['norchi dinamoeli tbilisi', '10', 'tbilisi', 'sport - kompleksi shatili', '2000'], ['chkherimela kharagauli', '11', 'imereti', 'kharagauli stadium', '6000'], ['adeli batumi', '12', 'adjara', 'tsentral stadium ( batumi )', '15000'], ['mertskhali ozurgeti', '13', 'guria', 'megobroba stadium', '3500'], ['samgurali tskhaltubo', '14', 'imereti', '26 may stadium', '12000'], ['skuri tsalenjikha', '15', 'samegrelo', 'sasha kvaratskhelia stadium', '4000'], ['chiatura sachkhere', '16', 'imereti', 'temur maghradze stadium', '11700'], ['lokomotivi tbilisi', '17', 'tbilisi', 'mikheil meskhi stadium', '24680'], ['sulori vani', 'meore liga', 'imereti', 'grigol nikoleishvili stadium', '2500'], ['stu tbilisi', 'meore liga', 'tbilisi', 'sport - kompleksi shatili', '2000'], ['meskheri akhaltsikhe', 'meore liga', 'samtskhe - javakheti', 'tsentral stadium ( akhaltsikhe )', '4000'], ['aeti sokhumi', 'meore liga', 'abkhazia', 'sport - kompleksi shatili', '2000'], ['zooveti tbilisi', 'meore liga', 'tbilisi', 'sportis akademiis stadioni', '1000']]

coppa italia

https://en.wikipedia.org/wiki/Coppa_Italia

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1281200-1.html.csv

count

a total of four rounds had no new entries in the round .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'none', 'result': '4', 'col': '6', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'new entries this round', 'none'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose new entries this round record fuzzily matches to none .', 'tostr': 'filter_eq { all_rows ; new entries this round ; none }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; new entries this round ; none } }', 'tointer': 'select the rows whose new entries this round record fuzzily matches to none . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; new entries this round ; none } } ; 4 } = true', 'tointer': 'select the rows whose new entries this round record fuzzily matches to none . the number of such rows is 4 .'}

eq { count { filter_eq { all_rows ; new entries this round ; none } } ; 4 } = true

select the rows whose new entries this round record fuzzily matches to none . the number of such rows is 4 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'new entries this round_5': 5, 'none_6': 6, '4_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'new entries this round_5': 'new entries this round', 'none_6': 'none', '4_7': '4'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'new entries this round_5': [0], 'none_6': [0], '4_7': [2]}

['phase', 'round', 'clubs remaining', 'clubs involved', 'winners from previous round', 'new entries this round', 'leagues entering at this round']

[['first phase', 'first round', '78', '36', 'none', '36', 'teams from lega pro and serie d'], ['first phase', 'second round', '60', '40', '18', '22', 'serie b'], ['first phase', 'third round', '40', '32', '20', '12', 'lowest - ranked serie a teams'], ['first phase', 'fourth round', '24', '16', '16', 'none', 'none'], ['second phase', 'round of 16', '16', '16', '8', '8', 'highest - ranked serie a teams'], ['second phase', 'quarter - finals', '8', '8', '8', 'none', 'none'], ['second phase', 'semi - finals', '4', '4', '4', 'none', 'none'], ['second phase', 'final', '2', '2', '2', 'none', 'none']]

1992 - 93 toronto maple leafs season

https://en.wikipedia.org/wiki/1992%E2%80%9393_Toronto_Maple_Leafs_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13913477-9.html.csv

ordinal

the toronto maple leafs game against new jersey was the earliest in the 1992 - 93 season .

{'row': '1', 'col': '2', 'order': '1', 'col_other': '3', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'date', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; date ; 1 }'}, 'visitor'], 'result': 'new jersey', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; date ; 1 } ; visitor }'}, 'new jersey'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; date ; 1 } ; visitor } ; new jersey } = true', 'tointer': 'select the row whose date record of all rows is 1st minimum . the visitor record of this row is new jersey .'}

eq { hop { nth_argmin { all_rows ; date ; 1 } ; visitor } ; new jersey } = true

select the row whose date record of all rows is 1st minimum . the visitor record of this row is new jersey .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, '1_6': 6, 'visitor_7': 7, 'new jersey_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'date_5': 'date', '1_6': '1', 'visitor_7': 'visitor', 'new jersey_8': 'new jersey'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], '1_6': [0], 'visitor_7': [1], 'new jersey_8': [2]}

['game', 'date', 'visitor', 'score', 'home', 'record', 'points']

[['78', 'april 3', 'new jersey', '1 - 0', 'toronto', '42 - 25 - 11', '95'], ['79', 'april 4', 'toronto', '0 - 4', 'philadelphia', '42 - 26 - 11', '95'], ['80', 'april 8', 'toronto', '3 - 5', 'winnipeg', '42 - 27 - 11', '95'], ['81', 'april 10', 'philadelphia', '0 - 4', 'toronto', '42 - 28 - 11', '95'], ['82', 'april 11', 'toronto', '4 - 2', 'hartford', '43 - 28 - 11', '97'], ['83', 'april 13', 'st louis', '2 - 1', 'toronto', '44 - 28 - 11', '99'], ['84', 'april 15', 'toronto', '2 - 3', 'chicago', '44 - 29 - 11', '99']]

wru division four south east

https://en.wikipedia.org/wiki/WRU_Division_Four_South_East

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13940275-4.html.csv

unique

cefn coed rfc is the only club with a single try bonus in the wru division four south east .

{'scope': 'all', 'row': '13', 'col': '9', 'col_other': '1', 'criterion': 'equal', 'value': '1', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'try bonus', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose try bonus record is equal to 1 .', 'tostr': 'filter_eq { all_rows ; try bonus ; 1 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; try bonus ; 1 } }', 'tointer': 'select the rows whose try bonus record is equal to 1 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'try bonus', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose try bonus record is equal to 1 .', 'tostr': 'filter_eq { all_rows ; try bonus ; 1 }'}, 'club'], 'result': 'cefn coed rfc', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; try bonus ; 1 } ; club }'}, 'cefn coed rfc'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; try bonus ; 1 } ; club } ; cefn coed rfc }', 'tointer': 'the club record of this unqiue row is cefn coed rfc .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; try bonus ; 1 } } ; eq { hop { filter_eq { all_rows ; try bonus ; 1 } ; club } ; cefn coed rfc } } = true', 'tointer': 'select the rows whose try bonus record is equal to 1 . there is only one such row in the table . the club record of this unqiue row is cefn coed rfc .'}

and { only { filter_eq { all_rows ; try bonus ; 1 } } ; eq { hop { filter_eq { all_rows ; try bonus ; 1 } ; club } ; cefn coed rfc } } = true

select the rows whose try bonus record is equal to 1 . there is only one such row in the table . the club record of this unqiue row is cefn coed rfc .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'try bonus_7': 7, '1_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'club_9': 9, 'cefn coed rfc_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'try bonus_7': 'try bonus', '1_8': '1', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'club_9': 'club', 'cefn coed rfc_10': 'cefn coed rfc'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'try bonus_7': [0], '1_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'club_9': [2], 'cefn coed rfc_10': [3]}

['club', 'played', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points']

[['club', 'played', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points'], ['pentyrch rfc', '22', '0', '3', '542', '210', '78', '25', '11', '1', '88'], ['heol y cyw rfc', '22', '2', '3', '513', '219', '70', '24', '9', '3', '84'], ['porth harlequins rfc', '22', '1', '5', '443', '300', '64', '36', '9', '2', '77'], ['cardiff hsob rfc', '22', '0', '9', '554', '442', '73', '63', '8', '4', '64'], ['llantwit major rfc', '22', '0', '10', '465', '397', '58', '57', '8', '6', '62'], ['dowlais rfc', '22', '0', '9', '463', '334', '59', '45', '6', '2', '60'], ['abercwmboi rfc', '22', '0', '10', '398', '358', '50', '44', '5', '5', '54'], ['taffs well rfc', '22', '0', '12', '336', '533', '35', '74', '2', '3', '45'], ['ferndale rfc', '22', '2', '13', '397', '451', '53', '57', '3', '4', '39'], ['tonyrefail rfc', '22', '2', '15', '341', '564', '49', '72', '5', '5', '34'], ['senghenydd rfc', '22', '1', '19', '303', '542', '35', '75', '3', '6', '19'], ['cefn coed rfc', '22', '0', '20', '200', '605', '26', '78', '1', '1', '10']]

ray sefo

https://en.wikipedia.org/wiki/Ray_Sefo

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1533651-2.html.csv

count

ray sefo had 4 fights that only went 1 round .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': '1', 'result': '4', 'col': '6', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'round', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose round record fuzzily matches to 1 .', 'tostr': 'filter_eq { all_rows ; round ; 1 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; round ; 1 } }', 'tointer': 'select the rows whose round record fuzzily matches to 1 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; round ; 1 } } ; 4 } = true', 'tointer': 'select the rows whose round record fuzzily matches to 1 . the number of such rows is 4 .'}

eq { count { filter_eq { all_rows ; round ; 1 } } ; 4 } = true

select the rows whose round record fuzzily matches to 1 . the number of such rows is 4 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'round_5': 5, '1_6': 6, '4_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'round_5': 'round', '1_6': '1', '4_7': '4'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'round_5': [0], '1_6': [0], '4_7': [2]}

['date', 'result', 'opponent', 'location', 'method', 'round', 'record']

[['2001 - 09 - 02', 'loss', 'chester hughes', 'elgin , illinois , usa', 'ko', '1', '5 - 1 - 0'], ['2001 - 06 - 03', 'win', 'joe lenhart', 'elgin , illinois , usa', 'tko', '1', '5 - 0 - 0'], ['2001 - 02 - 11', 'win', 'steve griffin', 'elgin , illinois , usa', 'tko', '1', '4 - 0 - 0'], ['1996 - 10 - 05', 'win', 'nicky faamata', 'auckland , new zealand', 'tko', '3', '3 - 0 - 0'], ['1995 - 03 - 16', 'win', 'paul baker', 'auckland , new zealand', 'decision', '4', '2 - 0 - 0'], ['1994 - 11 - 24', 'win', 'alex katu', 'auckland , new zealand', 'tko', '1', '1 - 0 - 0']]

regions of iceland

https://en.wikipedia.org/wiki/Regions_of_Iceland

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2252745-1.html.csv

count

four of the regions of iceland have an area smaller than 10000 square kilometers .

{'scope': 'all', 'criterion': 'less_than', 'value': '10000', 'result': '4', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'area ( km square )', '10000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose area ( km square ) record is less than 10000 .', 'tostr': 'filter_less { all_rows ; area ( km square ) ; 10000 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_less { all_rows ; area ( km square ) ; 10000 } }', 'tointer': 'select the rows whose area ( km square ) record is less than 10000 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_less { all_rows ; area ( km square ) ; 10000 } } ; 4 } = true', 'tointer': 'select the rows whose area ( km square ) record is less than 10000 . the number of such rows is 4 .'}

eq { count { filter_less { all_rows ; area ( km square ) ; 10000 } } ; 4 } = true

select the rows whose area ( km square ) record is less than 10000 . the number of such rows is 4 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_less_0': 0, 'all_rows_4': 4, 'area (km square)_5': 5, '10000_6': 6, '4_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_less_0': 'filter_less', 'all_rows_4': 'all_rows', 'area (km square)_5': 'area ( km square )', '10000_6': '10000', '4_7': '4'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_less_0': [1], 'all_rows_4': [0], 'area (km square)_5': [0], '10000_6': [0], '4_7': [2]}

['', 'name', 'name ( english )', 'population 2008 - 07 - 01', 'area ( km square )', 'pop / km square', 'iso 3166 - 2', 'administrative centre']

[['1', 'höfuðborgarsvæði', 'capital region', '200969', '1062', '167.61', 'is - 1', 'reykjavík'], ['2', 'suðurnes', 'southern peninsula', '21431', '829', '20.18', 'is - 2', 'keflavík'], ['3', 'vesturland', 'western region', '15601', '9554', '1.51', 'is - 3', 'akranes'], ['4', 'vestfirðir', 'westfjords', '7279', '9409', '0.85', 'is - 4', 'ísafjörður'], ['5', 'norðurland vestra', 'northwestern region', '7392', '12737', '0.73', 'is - 5', 'sauðárkrókur'], ['6', 'norðurland eystra', 'northeastern region', '28925', '21968', '1.21', 'is - 6', 'akureyri'], ['7', 'austurland', 'eastern region', '13786', '22721', '0.52', 'is - 7', 'egilsstaðir'], ['8', 'suðurland', 'southern region', '23972', '24526', '0.87', 'is - 8', 'selfoss']]

2008 - 09 süper lig

https://en.wikipedia.org/wiki/2008%E2%80%9309_S%C3%BCper_Lig

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17356873-2.html.csv

comparative

raşit cetiner left his position eight days before engin ipekoğlu left his position .

{'row_1': '1', 'row_2': '2', 'col': '4', 'col_other': '2', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '8 days', 'bigger': 'row2'}}

{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'outgoing manager', 'raşit çetiner'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose outgoing manager record fuzzily matches to raşit çetiner .', 'tostr': 'filter_eq { all_rows ; outgoing manager ; raşit çetiner }'}, 'date of vacancy'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; outgoing manager ; raşit çetiner } ; date of vacancy }', 'tointer': 'select the rows whose outgoing manager record fuzzily matches to raşit çetiner . take the date of vacancy record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'outgoing manager', 'engin ipekoğlu'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose outgoing manager record fuzzily matches to engin ipekoğlu .', 'tostr': 'filter_eq { all_rows ; outgoing manager ; engin ipekoğlu }'}, 'date of vacancy'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; outgoing manager ; engin ipekoğlu } ; date of vacancy }', 'tointer': 'select the rows whose outgoing manager record fuzzily matches to engin ipekoğlu . take the date of vacancy record of this row .'}], 'result': '-8 days', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; outgoing manager ; raşit çetiner } ; date of vacancy } ; hop { filter_eq { all_rows ; outgoing manager ; engin ipekoğlu } ; date of vacancy } }'}, '-8 days'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; outgoing manager ; raşit çetiner } ; date of vacancy } ; hop { filter_eq { all_rows ; outgoing manager ; engin ipekoğlu } ; date of vacancy } } ; -8 days } = true', 'tointer': 'select the rows whose outgoing manager record fuzzily matches to raşit çetiner . take the date of vacancy record of this row . select the rows whose outgoing manager record fuzzily matches to engin ipekoğlu . take the date of vacancy record of this row . the second record is 8 days larger than the first record .'}

eq { diff { hop { filter_eq { all_rows ; outgoing manager ; raşit çetiner } ; date of vacancy } ; hop { filter_eq { all_rows ; outgoing manager ; engin ipekoğlu } ; date of vacancy } } ; -8 days } = true

select the rows whose outgoing manager record fuzzily matches to raşit çetiner . take the date of vacancy record of this row . select the rows whose outgoing manager record fuzzily matches to engin ipekoğlu . take the date of vacancy record of this row . the second record is 8 days larger than the first record .

6

6

{'str_eq_5': 5, 'result_6': 6, 'diff_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'outgoing manager_8': 8, 'raşit çetiner_9': 9, 'date of vacancy_10': 10, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'outgoing manager_12': 12, 'engin ipekoğlu_13': 13, 'date of vacancy_14': 14, '-8 days_15': 15}

{'str_eq_5': 'str_eq', 'result_6': 'true', 'diff_4': 'diff', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'outgoing manager_8': 'outgoing manager', 'raşit çetiner_9': 'raşit çetiner', 'date of vacancy_10': 'date of vacancy', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'outgoing manager_12': 'outgoing manager', 'engin ipekoğlu_13': 'engin ipekoğlu', 'date of vacancy_14': 'date of vacancy', '-8 days_15': '-8 days'}

{'str_eq_5': [6], 'result_6': [], 'diff_4': [5], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'outgoing manager_8': [0], 'raşit çetiner_9': [0], 'date of vacancy_10': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'outgoing manager_12': [1], 'engin ipekoğlu_13': [1], 'date of vacancy_14': [3], '-8 days_15': [5]}

['team', 'outgoing manager', 'manner of departure', 'date of vacancy', 'replaced by', 'date of appointment']

[['konyaspor', 'raşit çetiner', 'sacked', '17 september 2008', 'giray bulak', '24 september 2008'], ['kocaelispor', 'engin ipekoğlu', 'sacked', '25 september 2008', 'yılmaz vural', '28 september 2008'], ['beşiktaş', 'ertuğrul sağlam', 'resigned', '7 october 2008', 'mustafa denizli', '9 october 2008'], ['ankaragücü', 'hakan kutlu', 'sacked', '20 october 2008', 'ünal karaman', '24 october 2008'], ['antalyaspor', 'jozef jarabinský', 'sacked', '28 october 2008', 'mehmet özdilek', '28 october 2008'], ['hacettepe', 'osman özdemir', 'resigned', '2 november 2008', 'erdoğan arıca', '3 november 2008'], ['denizlispor', 'ali yalçın', 'resigned', '2 november 2008', 'ümit kayıhan', '10 november 2008'], ['gençlerbirliği', 'mesut bakkal', 'resigned', '3 november 2008', 'samet aybaba', '5 november 2008'], ['bursaspor', 'samet aybaba', 'resigned', '4 november 2008', 'güvenç kurtar', '4 november 2008'], ['ankaragücü', 'ünal karaman', 'resigned', '8 december 2008', 'hakan kutlu', '2 january 2009'], ['bursaspor', 'güvenç kurtar', 'resigned', '23 december 2008', 'ertuğrul sağlam', '2 january 2009'], ['kocaelispor', 'yılmaz vural', 'resigned', '29 december 2008', 'erhan altın', '17 january 2009'], ['denizlispor', 'ümit kayıhan', 'sacked', '5 february 2009', 'mesut bakkal', '6 february 2009'], ['galatasaray', 'michael skibbe', 'sacked', '23 february 2009', 'bülent korkmaz', '23 february 2009'], ['hacettepe', 'erdoğan arıca', 'resigned', '2 march 2009', 'ergün penbe', '2 march 2009'], ['gaziantepspor', 'nurullah sağlam', 'resigned', '9 march 2009', 'josé couceiro', '6 april 2009']]

1995 - 96 colorado avalanche season

https://en.wikipedia.org/wiki/1995%E2%80%9396_Colorado_Avalanche_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11945691-4.html.csv

aggregation

the average number of points scored by the colorado avalanche in each game was 4.21 .

{'scope': 'all', 'col': '3', 'type': 'average', 'result': '4.21', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'score'], 'result': '4.21', 'ind': 0, 'tostr': 'avg { all_rows ; score }'}, '4.21'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; score } ; 4.21 } = true', 'tointer': 'the average of the score record of all rows is 4.21 .'}

round_eq { avg { all_rows ; score } ; 4.21 } = true

the average of the score record of all rows is 4.21 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'score_4': 4, '4.21_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'score_4': 'score', '4.21_5': '4.21'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'score_4': [0], '4.21_5': [1]}

['date', 'visitor', 'score', 'home', 'record']

[['december 1', 'colorado', '3 - 5', 'ny rangers', '15 - 6 - 4'], ['december 3', 'dallas', '7 - 6', 'colorado', '15 - 7 - 4'], ['december 5', 'san jose', '2 - 12', 'colorado', '16 - 7 - 4'], ['december 7', 'edmonton', '5 - 3', 'colorado', '16 - 8 - 4'], ['december 9', 'colorado', '7 - 3', 'ottawa', '17 - 8 - 4'], ['december 11', 'colorado', '5 - 1', 'toronto', '18 - 8 - 4'], ['december 13', 'colorado', '3 - 4', 'buffalo', '18 - 9 - 4'], ['december 15', 'colorado', '2 - 4', 'hartford', '18 - 10 - 4'], ['december 18', 'vancouver', '4 - 2', 'colorado', '18 - 11 - 4'], ['december 20', 'colorado', '4 - 1', 'edmonton', '19 - 11 - 4'], ['december 22', 'st louis', '1 - 2', 'colorado', '20 - 11 - 4'], ['december 23', 'colorado', '2 - 2', 'los angeles', '20 - 11 - 5'], ['december 26', 'colorado', '5 - 1', 'san jose', '21 - 11 - 5'], ['december 29', 'toronto', '2 - 3', 'colorado', '22 - 11 - 5']]

danny sullivan

https://en.wikipedia.org/wiki/Danny_Sullivan

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226454-1.html.csv

unique

the only time that danny sullivan finished in fourteenth place was 1982 .

{'scope': 'all', 'row': '1', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': '14', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'finish', '14'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose finish record is equal to 14 .', 'tostr': 'filter_eq { all_rows ; finish ; 14 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; finish ; 14 } }', 'tointer': 'select the rows whose finish record is equal to 14 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'finish', '14'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose finish record is equal to 14 .', 'tostr': 'filter_eq { all_rows ; finish ; 14 }'}, 'year'], 'result': '1982', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; finish ; 14 } ; year }'}, '1982'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; finish ; 14 } ; year } ; 1982 }', 'tointer': 'the year record of this unqiue row is 1982 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; finish ; 14 } } ; eq { hop { filter_eq { all_rows ; finish ; 14 } ; year } ; 1982 } } = true', 'tointer': 'select the rows whose finish record is equal to 14 . there is only one such row in the table . the year record of this unqiue row is 1982 .'}

and { only { filter_eq { all_rows ; finish ; 14 } } ; eq { hop { filter_eq { all_rows ; finish ; 14 } ; year } ; 1982 } } = true

select the rows whose finish record is equal to 14 . there is only one such row in the table . the year record of this unqiue row is 1982 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'finish_7': 7, '14_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1982_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'finish_7': 'finish', '14_8': '14', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1982_10': '1982'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'finish_7': [0], '14_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1982_10': [3]}

['year', 'start', 'qual', 'rank', 'finish', 'laps']

[['1982', '13', '196.292', '17', '14', '148'], ['1984', '28', '203.567', '17', '29', '57'], ['1985', '8', '210.298', '8', '1', '200'], ['1986', '2', '215.382', '2', '9', '197'], ['1987', '16', '210.271', '6', '13', '160'], ['1988', '2', '216.214', '2', '23', '101'], ['1989', '26', '216.027', '15', '28', '41'], ['1990', '9', '220.310', '9', '32', '19'], ['1991', '9', '218.343', '17', '10', '173'], ['1992', '8', '224.838', '9', '5', '199'], ['1993', '12', '219.428', '19', '33', '29'], ['1995', '18', '225.496', '29', '9', '199']]

1995 - 96 colorado avalanche season

https://en.wikipedia.org/wiki/1995%E2%80%9396_Colorado_Avalanche_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11945691-4.html.csv

majority

all of the games took place in the month of december .

{'scope': 'all', 'col': '1', 'most_or_all': 'all', 'criterion': 'fuzzily_match', 'value': 'december', 'subset': None}

{'func': 'all_str_eq', 'args': ['all_rows', 'date', 'december'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to december .', 'tostr': 'all_eq { all_rows ; date ; december } = true'}

all_eq { all_rows ; date ; december } = true

for the date records of all rows , all of them fuzzily match to december .

1

1

{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, 'december_4': 4}

{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', 'december_4': 'december'}

{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], 'december_4': [0]}

['date', 'visitor', 'score', 'home', 'record']

[['december 1', 'colorado', '3 - 5', 'ny rangers', '15 - 6 - 4'], ['december 3', 'dallas', '7 - 6', 'colorado', '15 - 7 - 4'], ['december 5', 'san jose', '2 - 12', 'colorado', '16 - 7 - 4'], ['december 7', 'edmonton', '5 - 3', 'colorado', '16 - 8 - 4'], ['december 9', 'colorado', '7 - 3', 'ottawa', '17 - 8 - 4'], ['december 11', 'colorado', '5 - 1', 'toronto', '18 - 8 - 4'], ['december 13', 'colorado', '3 - 4', 'buffalo', '18 - 9 - 4'], ['december 15', 'colorado', '2 - 4', 'hartford', '18 - 10 - 4'], ['december 18', 'vancouver', '4 - 2', 'colorado', '18 - 11 - 4'], ['december 20', 'colorado', '4 - 1', 'edmonton', '19 - 11 - 4'], ['december 22', 'st louis', '1 - 2', 'colorado', '20 - 11 - 4'], ['december 23', 'colorado', '2 - 2', 'los angeles', '20 - 11 - 5'], ['december 26', 'colorado', '5 - 1', 'san jose', '21 - 11 - 5'], ['december 29', 'toronto', '2 - 3', 'colorado', '22 - 11 - 5']]

list of memorial cup champions

https://en.wikipedia.org/wiki/List_of_Memorial_Cup_champions

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17751942-2.html.csv

superlative

of all the memorial cup champions , the toronto marlboros scored the most goals in a single championship game .

{'scope': 'all', 'col_superlative': '2', 'row_superlative': '2', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'score'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; score }'}, 'champion'], 'result': 'toronto marlboros ( oha )', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; score } ; champion }'}, 'toronto marlboros ( oha )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; score } ; champion } ; toronto marlboros ( oha ) } = true', 'tointer': 'select the row whose score record of all rows is maximum . the champion record of this row is toronto marlboros ( oha ) .'}

eq { hop { argmax { all_rows ; score } ; champion } ; toronto marlboros ( oha ) } = true

select the row whose score record of all rows is maximum . the champion record of this row is toronto marlboros ( oha ) .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'score_5': 5, 'champion_6': 6, 'toronto marlboros (oha)_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'score_5': 'score', 'champion_6': 'champion', 'toronto marlboros (oha)_7': 'toronto marlboros ( oha )'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'score_5': [0], 'champion_6': [1], 'toronto marlboros (oha)_7': [2]}

['champion', 'score', 'runner - up', 'additional participants', 'host location ( s )']

[['cornwall royals ( qmjhl )', '2 - 1', 'peterborough petes ( oha )', 'edmonton oil kings ( wchl )', 'ottawa'], ['toronto marlboros ( oha )', '9 - 1', 'quebec remparts ( qmjhl )', 'medicine hat tigers ( wchl )', 'montreal'], ['regina pats ( wchl )', '7 - 4', 'quebec remparts ( qmjhl )', 'st catharines black hawks ( oha )', 'calgary'], ['toronto marlboros ( oha )', '7 - 3', 'new westminster bruins ( wchl )', 'sherbrooke castors ( qmjhl )', 'kitchener'], ['hamilton fincups ( oha )', '5 - 2', 'new westminster bruins ( wchl )', 'quebec remparts ( qmjhl )', 'montreal'], ['new westminster bruins ( wchl )', '6 - 5', "ottawa 67 's ( oha )", 'sherbrooke castors ( qmjhl )', 'vancouver'], ['new westminster bruins ( whl )', '7 - 4', 'peterborough petes ( oha )', 'trois - rivières draveurs ( qmjhl )', 'sudbury and sault ste marie'], ['peterborough petes ( oha )', '2 - 1 ( ot )', 'brandon wheat kings ( whl )', 'trois - rivières draveurs ( qmjhl )', 'sherbrooke , trois - rivières and verdun'], ['cornwall royals ( qmjhl )', '3 - 2 ( ot )', 'peterborough petes ( oha )', 'regina pats ( whl )', 'brandon and regina'], ['cornwall royals ( qmjhl )', '5 - 2', 'kitchener rangers ( ohl )', 'victoria cougars ( whl )', 'windsor'], ['kitchener rangers ( ohl )', '7 - 4', 'sherbrooke castors ( qmjhl )', 'portland winter hawks ( whl )', 'hull']]

1977 baltimore colts season

https://en.wikipedia.org/wiki/1977_Baltimore_Colts_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14945608-1.html.csv

comparative

in the 1977 baltimore colts season , the game on october 16 had a higher attendance than the game on november 13 .

{'row_1': '5', 'row_2': '9', 'col': '7', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'october 16 , 1977'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to october 16 , 1977 .', 'tostr': 'filter_eq { all_rows ; date ; october 16 , 1977 }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; october 16 , 1977 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to october 16 , 1977 . take the attendance record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'november 13 , 1977'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to november 13 , 1977 .', 'tostr': 'filter_eq { all_rows ; date ; november 13 , 1977 }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; november 13 , 1977 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to november 13 , 1977 . take the attendance record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; date ; october 16 , 1977 } ; attendance } ; hop { filter_eq { all_rows ; date ; november 13 , 1977 } ; attendance } } = true', 'tointer': 'select the rows whose date record fuzzily matches to october 16 , 1977 . take the attendance record of this row . select the rows whose date record fuzzily matches to november 13 , 1977 . take the attendance record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; date ; october 16 , 1977 } ; attendance } ; hop { filter_eq { all_rows ; date ; november 13 , 1977 } ; attendance } } = true

select the rows whose date record fuzzily matches to october 16 , 1977 . take the attendance record of this row . select the rows whose date record fuzzily matches to november 13 , 1977 . take the attendance record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'date_7': 7, 'october 16 , 1977_8': 8, 'attendance_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'date_11': 11, 'november 13 , 1977_12': 12, 'attendance_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'date_7': 'date', 'october 16 , 1977_8': 'october 16 , 1977', 'attendance_9': 'attendance', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', 'november 13 , 1977_12': 'november 13 , 1977', 'attendance_13': 'attendance'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'date_7': [0], 'october 16 , 1977_8': [0], 'attendance_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'date_11': [1], 'november 13 , 1977_12': [1], 'attendance_13': [3]}

['week', 'date', 'opponent', 'result', 'record', 'game site', 'attendance']

[['1', 'september 18 , 1977', 'seattle seahawks', 'w 29 - 14', '1 - 0', 'kingdome', '58991'], ['2', 'september 25 , 1977', 'new york jets', 'w 20 - 12', '2 - 0', 'shea stadium', '43439'], ['3', 'october 2 , 1977', 'buffalo bills', 'w 17 - 14', '3 - 0', 'memorial stadium', '49247'], ['4', 'october 9 , 1977', 'miami dolphins', 'w 45 - 28', '4 - 0', 'memorial stadium', '57829'], ['5', 'october 16 , 1977', 'kansas city chiefs', 'w 17 - 6', '5 - 0', 'arrowhead stadium', '63076'], ['6', 'october 23 , 1977', 'new england patriots', 'l 3 - 17', '5 - 1', 'schaeffer stadium', '60958'], ['7', 'october 30 , 1977', 'pittsburgh steelers', 'w 31 - 21', '6 - 1', 'memorial stadium', '60225'], ['8', 'november 7 , 1977', 'washington redskins', 'w 10 - 3', '7 - 1', 'memorial stadium', '57740'], ['9', 'november 13 , 1977', 'buffalo bills', 'w 31 - 13', '8 - 1', 'rich stadium', '39444'], ['10', 'november 20 , 1977', 'new york jets', 'w 33 - 12', '9 - 1', 'memorial stadium', '50957'], ['11', 'november 27 , 1977', 'denver broncos', 'l 13 - 27', '9 - 2', 'mile high stadium', '74939'], ['12', 'december 5 , 1977', 'miami dolphins', 'l 6 - 17', '9 - 3', 'miami orange bowl', '68977'], ['13', 'december 11 , 1977', 'detroit lions', 'l 10 - 13', '9 - 4', 'memorial stadium', '45124']]

1920 summer olympics

https://en.wikipedia.org/wiki/1920_Summer_Olympics

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-113485-1.html.csv

ordinal

sweden received the 2nd highest amount of bronze medals in the 1920 summer olympics .

{'row': '2', 'col': '5', 'order': '2', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'bronze', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; bronze ; 2 }'}, 'nation'], 'result': 'sweden', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; bronze ; 2 } ; nation }'}, 'sweden'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; bronze ; 2 } ; nation } ; sweden } = true', 'tointer': 'select the row whose bronze record of all rows is 2nd maximum . the nation record of this row is sweden .'}

eq { hop { nth_argmax { all_rows ; bronze ; 2 } ; nation } ; sweden } = true

select the row whose bronze record of all rows is 2nd maximum . the nation record of this row is sweden .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'bronze_5': 5, '2_6': 6, 'nation_7': 7, 'sweden_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'bronze_5': 'bronze', '2_6': '2', 'nation_7': 'nation', 'sweden_8': 'sweden'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'bronze_5': [0], '2_6': [0], 'nation_7': [1], 'sweden_8': [2]}

['rank', 'nation', 'gold', 'silver', 'bronze', 'total']

[['1', 'united states', '41', '27', '27', '95'], ['2', 'sweden', '19', '20', '25', '64'], ['3', 'great britain', '15', '15', '13', '43'], ['4', 'finland', '15', '10', '9', '34'], ['5', 'belgium ( host nation )', '14', '11', '11', '36'], ['6', 'norway', '13', '9', '9', '31'], ['7', 'italy', '13', '5', '5', '23'], ['8', 'france', '9', '19', '13', '41'], ['9', 'netherlands', '4', '2', '5', '11'], ['10', 'denmark', '3', '9', '1', '13']]

1981 new york yankees season

https://en.wikipedia.org/wiki/1981_New_York_Yankees_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11487949-8.html.csv

count

in the 1981 nyy season games listed , 3 games were played at yankee stadium .

{'scope': 'all', 'criterion': 'equal', 'value': 'yankee stadium', 'result': '3', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'yankee stadium'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to yankee stadium .', 'tostr': 'filter_eq { all_rows ; location ; yankee stadium }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location ; yankee stadium } }', 'tointer': 'select the rows whose location record fuzzily matches to yankee stadium . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location ; yankee stadium } } ; 3 } = true', 'tointer': 'select the rows whose location record fuzzily matches to yankee stadium . the number of such rows is 3 .'}

eq { count { filter_eq { all_rows ; location ; yankee stadium } } ; 3 } = true

select the rows whose location record fuzzily matches to yankee stadium . the number of such rows is 3 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'location_5': 5, 'yankee stadium_6': 6, '3_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'location_5': 'location', 'yankee stadium_6': 'yankee stadium', '3_7': '3'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location_5': [0], 'yankee stadium_6': [0], '3_7': [2]}

['game', 'score', 'date', 'location', 'attendance', 'time of game']

[['1', 'dodgers - 3 , yankees - 5', 'october 20', 'yankee stadium ( new york )', '56470', '2:32'], ['2', 'dodgers - 0 , yankees - 3', 'october 21', 'yankee stadium ( new york )', '56505', '2:29'], ['3', 'yankees - 4 , dodgers - 5', 'october 23', 'dodger stadium ( los angeles )', '56236', '3:04'], ['4', 'yankees - 7 , dodgers - 8', 'october 24', 'dodger stadium ( los angeles )', '56242', '3:32'], ['5', 'yankees - 1 , dodgers - 2', 'october 25', 'dodger stadium ( los angeles )', '56115', '2:19'], ['6', 'dodgers - 9 , yankees - 2', 'october 28', 'yankee stadium ( new york )', '56513', '3:09']]

2005 buffalo bills season

https://en.wikipedia.org/wiki/2005_Buffalo_Bills_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18695319-1.html.csv

count

a total of two players from miami ( fla ) college were picked in the 2005 buffalo bills season .

{'scope': 'all', 'criterion': 'equal', 'value': 'miami ( fla )', 'result': '2', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'miami ( fla )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to miami ( fla ) .', 'tostr': 'filter_eq { all_rows ; college ; miami ( fla ) }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; college ; miami ( fla ) } }', 'tointer': 'select the rows whose college record fuzzily matches to miami ( fla ) . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; college ; miami ( fla ) } } ; 2 } = true', 'tointer': 'select the rows whose college record fuzzily matches to miami ( fla ) . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; college ; miami ( fla ) } } ; 2 } = true

select the rows whose college record fuzzily matches to miami ( fla ) . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'college_5': 5, 'miami (fla)_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'college_5': 'college', 'miami (fla)_6': 'miami ( fla )', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'college_5': [0], 'miami (fla)_6': [0], '2_7': [2]}

['round', 'pick', 'player', 'position', 'college']

[['2', '5', 'roscoe parrish', 'wide receiver', 'miami ( fla )'], ['3', '86', 'kevin everett', 'tight end', 'miami ( fla )'], ['4', '122', 'duke preston', 'center', 'illinois'], ['5', '156', 'eric king', 'cornerback', 'wake forest'], ['6', '197', 'justin geisinger', 'offensive guard', 'vanterbilt'], ['7', '236', 'lionel gates', 'running back', 'louisville']]

2007 calgary stampeders season

https://en.wikipedia.org/wiki/2007_Calgary_Stampeders_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12297537-1.html.csv

ordinal

jabari arthur was the third highest picked player for the calgary stampeders in the 2007 draft .

{'row': '3', 'col': '2', 'order': '3', 'col_other': '3', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'pick', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; pick ; 3 }'}, 'player'], 'result': 'jabari arthur', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; pick ; 3 } ; player }'}, 'jabari arthur'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; pick ; 3 } ; player } ; jabari arthur } = true', 'tointer': 'select the row whose pick record of all rows is 3rd minimum . the player record of this row is jabari arthur .'}

eq { hop { nth_argmin { all_rows ; pick ; 3 } ; player } ; jabari arthur } = true

select the row whose pick record of all rows is 3rd minimum . the player record of this row is jabari arthur .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'pick_5': 5, '3_6': 6, 'player_7': 7, 'jabari arthur_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'pick_5': 'pick', '3_6': '3', 'player_7': 'player', 'jabari arthur_8': 'jabari arthur'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'pick_5': [0], '3_6': [0], 'player_7': [1], 'jabari arthur_8': [2]}

['round', 'pick', 'player', 'position', 'school / club team']

[['1', '3', 'mike gyetvai', 'ol', 'michigan state'], ['1', '5', 'justin phillips', 'lb', 'wilfrid laurier'], ['1', '6', 'jabari arthur', 'wr', 'akron'], ['2', '14', 'kevin challenger', 'wr', 'boston college'], ['3', '21', 'patrick macdonald', 'dl', 'alberta'], ['5', '35', 'henry bekkering', 'k', 'eastern washington'], ['5', '38', 'ian hazlett', 'lb', "queen 's"], ['6', '45', 'greg hetherington', 'sb', 'mcgill']]

the rob brydon show

https://en.wikipedia.org/wiki/The_Rob_Brydon_Show

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29135051-2.html.csv

comparative

more people tuned into the rob brydon show when the musical guest was the script than when it was hurts .

{'row_1': '1', 'row_2': '6', 'col': '6', 'col_other': '4', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'singer ( s )', 'the script'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose singer ( s ) record fuzzily matches to the script .', 'tostr': 'filter_eq { all_rows ; singer ( s ) ; the script }'}, 'ratings'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; singer ( s ) ; the script } ; ratings }', 'tointer': 'select the rows whose singer ( s ) record fuzzily matches to the script . take the ratings record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'singer ( s )', 'hurts'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose singer ( s ) record fuzzily matches to hurts .', 'tostr': 'filter_eq { all_rows ; singer ( s ) ; hurts }'}, 'ratings'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; singer ( s ) ; hurts } ; ratings }', 'tointer': 'select the rows whose singer ( s ) record fuzzily matches to hurts . take the ratings record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; singer ( s ) ; the script } ; ratings } ; hop { filter_eq { all_rows ; singer ( s ) ; hurts } ; ratings } } = true', 'tointer': 'select the rows whose singer ( s ) record fuzzily matches to the script . take the ratings record of this row . select the rows whose singer ( s ) record fuzzily matches to hurts . take the ratings record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; singer ( s ) ; the script } ; ratings } ; hop { filter_eq { all_rows ; singer ( s ) ; hurts } ; ratings } } = true

select the rows whose singer ( s ) record fuzzily matches to the script . take the ratings record of this row . select the rows whose singer ( s ) record fuzzily matches to hurts . take the ratings record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'singer (s)_7': 7, 'the script_8': 8, 'ratings_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'singer (s)_11': 11, 'hurts_12': 12, 'ratings_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'singer (s)_7': 'singer ( s )', 'the script_8': 'the script', 'ratings_9': 'ratings', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'singer (s)_11': 'singer ( s )', 'hurts_12': 'hurts', 'ratings_13': 'ratings'}

{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'singer (s)_7': [0], 'the script_8': [0], 'ratings_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'singer (s)_11': [1], 'hurts_12': [1], 'ratings_13': [3]}

['episode', 'broadcast date', 'guest ( s )', 'singer ( s )', 'comedian', 'ratings']

[['1', '22 july 2011', 'matt lucas', 'the script', 'nina conti', '2.08 m'], ['2', '29 july 2011', 'bill bailey', 'beverley knight', 'celia pacquola', '1.45 m'], ['3', '5 august 2011', 'bruce forsyth', 'sophie ellis - bextor', 'elis james', 'under 1.41 m'], ['4', '12 august 2011', "chris o'dowd", 'the faces', 'josh widdicombe', 'under 1.32 m'], ['5', '19 august 2011', 'dame edna everage', 'will young', 'phil wang', '1.57 m'], ['6', '26 august 2011', 'frank skinner', 'hurts', 'joe wilkinson', '1.64 m']]

2000 masters tournament

https://en.wikipedia.org/wiki/2000_Masters_Tournament

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16514630-7.html.csv

unique

in the 2000 masters tournament , only one player from united states won less than 140,000 prize money .

{'scope': 'subset', 'row': '10', 'col': '6', 'col_other': '3', 'criterion': 'less_than', 'value': '140000', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'united states'}}

{'func': 'only', 'args': [{'func': 'filter_less', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; country ; united states }', 'tointer': 'select the rows whose country record fuzzily matches to united states .'}, 'money', '140000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose money record is less than 140000 .', 'tostr': 'filter_less { filter_eq { all_rows ; country ; united states } ; money ; 140000 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_less { filter_eq { all_rows ; country ; united states } ; money ; 140000 } } = true', 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose money record is less than 140000 . there is only one such row in the table .'}

only { filter_less { filter_eq { all_rows ; country ; united states } ; money ; 140000 } } = true

select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose money record is less than 140000 . there is only one such row in the table .

3

3

{'only_2': 2, 'result_3': 3, 'filter_less_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'country_5': 5, 'united states_6': 6, 'money_7': 7, '140000_8': 8}

{'only_2': 'only', 'result_3': 'true', 'filter_less_1': 'filter_less', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'country_5': 'country', 'united states_6': 'united states', 'money_7': 'money', '140000_8': '140000'}

{'only_2': [3], 'result_3': [], 'filter_less_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'country_5': [0], 'united states_6': [0], 'money_7': [1], '140000_8': [1]}

['place', 'player', 'country', 'score', 'to par', 'money']

[['1', 'vijay singh', 'fiji', '72 + 67 + 70 + 69 = 278', '- 10', '828000'], ['2', 'ernie els', 'south africa', '72 + 67 + 74 + 68 = 281', '- 7', '496800'], ['t3', 'david duval', 'united states', '73 + 65 + 74 + 70 = 282', '- 6', '266800'], ['t3', 'loren roberts', 'united states', '73 + 69 + 71 + 69 = 282', '- 6', '266800'], ['5', 'tiger woods', 'united states', '75 + 72 + 68 + 69 = 284', '- 4', '184000'], ['6', 'tom lehman', 'united states', '69 + 72 + 75 + 69 = 285', '- 3', '165600'], ['t7', 'carlos franco', 'paraguay', '79 + 68 + 70 + 69 = 286', '- 2', '143367'], ['t7', 'davis love iii', 'united states', '75 + 72 + 68 + 71 = 286', '- 2', '143367'], ['t7', 'phil mickelson', 'united states', '71 + 68 + 76 + 71 = 286', '- 2', '143367'], ['10', 'hal sutton', 'united states', '72 + 75 + 71 + 69 = 287', '- 1', '124200']]

concrete canoe

https://en.wikipedia.org/wiki/Concrete_canoe

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2331549-1.html.csv

comparative

for concrete canoe , the host city was buffalo , new york one year before the host city was orlando , florida .

{'row_1': '3', 'row_2': '4', 'col': '1', 'col_other': '2', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '1', 'bigger': 'row2'}}

{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'host city', 'buffalo , new york'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose host city record fuzzily matches to buffalo , new york .', 'tostr': 'filter_eq { all_rows ; host city ; buffalo , new york }'}, 'year'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; host city ; buffalo , new york } ; year }', 'tointer': 'select the rows whose host city record fuzzily matches to buffalo , new york . take the year record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'host city', 'orlando , florida'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose host city record fuzzily matches to orlando , florida .', 'tostr': 'filter_eq { all_rows ; host city ; orlando , florida }'}, 'year'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; host city ; orlando , florida } ; year }', 'tointer': 'select the rows whose host city record fuzzily matches to orlando , florida . take the year record of this row .'}], 'result': '-1', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; host city ; buffalo , new york } ; year } ; hop { filter_eq { all_rows ; host city ; orlando , florida } ; year } }'}, '-1'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; host city ; buffalo , new york } ; year } ; hop { filter_eq { all_rows ; host city ; orlando , florida } ; year } } ; -1 } = true', 'tointer': 'select the rows whose host city record fuzzily matches to buffalo , new york . take the year record of this row . select the rows whose host city record fuzzily matches to orlando , florida . take the year record of this row . the second record is 1 larger than the first record .'}

eq { diff { hop { filter_eq { all_rows ; host city ; buffalo , new york } ; year } ; hop { filter_eq { all_rows ; host city ; orlando , florida } ; year } } ; -1 } = true

select the rows whose host city record fuzzily matches to buffalo , new york . take the year record of this row . select the rows whose host city record fuzzily matches to orlando , florida . take the year record of this row . the second record is 1 larger than the first record .

6

6

{'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'host city_8': 8, 'buffalo , new york_9': 9, 'year_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'host city_12': 12, 'orlando , florida_13': 13, 'year_14': 14, '-1_15': 15}

{'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'host city_8': 'host city', 'buffalo , new york_9': 'buffalo , new york', 'year_10': 'year', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'host city_12': 'host city', 'orlando , florida_13': 'orlando , florida', 'year_14': 'year', '-1_15': '-1'}

{'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'host city_8': [0], 'buffalo , new york_9': [0], 'year_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'host city_12': [1], 'orlando , florida_13': [1], 'year_14': [3], '-1_15': [5]}

['year', 'host city', 'host school', 'champion', 'second place', 'third place']

[['1988', 'east lansing , michigan', 'michigan state university', 'university of california , berkeley', 'university of new hampshire', 'university of akron'], ['1989', 'lubbock , texas', 'texas tech university', 'university of california , berkeley', 'michigan state university', 'university of new hampshire'], ['1990', 'buffalo , new york', 'state university of new york', 'michigan state university', 'university of maryland', 'university of california , berkeley'], ['1991', 'orlando , florida', 'university of central florida', 'university of california , berkeley', 'university of maryland', 'university at buffalo'], ['1992', 'fort collins , colorado', 'colorado state university', 'university of california , berkeley', 'university of alabama , huntsville', 'university of new orleans'], ['1993', 'sacramento , california', 'california state university , sacramento', 'university of alabama , huntsville', 'michigan state university', 'university of california , berkeley'], ['1994', 'new orleans , louisiana', 'university of new orleans', 'university of alabama , huntsville', 'university of california , berkeley', 'university of new orleans'], ['1995', 'washington , dc', 'george washington university', 'south dakota school of mines & technology', 'california state university , sacramento', 'michigan state university'], ['1996', 'madison , wisconsin', 'university of wisconsin at madison', 'university of alabama , huntsville', 'michigan state university', 'university of california , berkeley'], ['1997', 'cleveland , ohio', 'cleveland state university', 'florida institute of technology', 'university of alabama , huntsville', 'university of california , berkeley'], ['1998', 'rapid city , south dakota', 'south dakota school of mines & technology', 'university of alabama , huntsville', 'california state university , sacramento', 'clemson university'], ['1999', 'melbourne , florida', 'florida institute of technology', 'clemson university', 'university of alabama , huntsville', 'oklahoma state university'], ['2000', 'golden , colorado', 'colorado school of mines', 'clemson university', 'oklahoma state university', 'florida institute of technology'], ['2001', 'san diego , california', 'san diego state university', 'university of alabama , huntsville', 'clemson university', 'oklahoma state university'], ['2002', 'madison , wisconsin', 'university of wisconsin', 'clemson university', 'université laval', 'oklahoma state university'], ['2003', 'philadelphia , pennsylvania', 'drexel university', 'university of wisconsin , madison', 'université laval', 'university of california , berkeley'], ['2004', 'washington , dc', 'the catholic university of america', 'university of wisconsin , madison', 'université laval', 'university of alabama , huntsville'], ['2005', 'clemson , south carolina', 'clemson university', 'university of wisconsin , madison', 'clemson university', 'michigan technological university'], ['2007', 'seattle , washington', 'university of washington', 'university of wisconsin , madison', 'university of florida', 'university of nevada , reno'], ['2008', 'montreal , quebec', 'école de technologie supérieure', 'university of nevada , reno', 'university of california , berkeley', 'école de technologie supérieure']]

1951 - 52 illinois fighting illini men 's basketball team

https://en.wikipedia.org/wiki/1951%E2%80%9352_Illinois_Fighting_Illini_men%27s_basketball_team

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22824312-1.html.csv

aggregation

the average weight of players on the 1951 - 52 illinois fighting illini men 's basketball team is 188 lb .

{'scope': 'all', 'col': '5', 'type': 'average', 'result': '188', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'weight'], 'result': '188', 'ind': 0, 'tostr': 'avg { all_rows ; weight }'}, '188'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; weight } ; 188 } = true', 'tointer': 'the average of the weight record of all rows is 188 .'}

round_eq { avg { all_rows ; weight } ; 188 } = true

the average of the weight record of all rows is 188 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'weight_4': 4, '188_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'weight_4': 'weight', '188_5': '188'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'weight_4': [0], '188_5': [1]}

['no', 'player', 'position', 'height', 'weight', 'class', 'hometown']

[['9', 'elmer plew', 'guard', '6 - 0', '170', 'freshman', 'paris , illinois'], ['11', 'jim dutcher', 'forward', '6 - 3', '185', 'freshman', 'downers grove , illinois'], ['16', 'jim wright', 'guard', '6 - 0', '160', 'sophomore', 'lawrenceville , illinois'], ['19', 'james bredar', 'guard', '5 - 11', '167', 'junior', 'salem , illinois'], ['22', 'johnny kerr', 'center', '6 - 9', '205', 'sophomore', 'chicago , illinois / tilden high school'], ['24', 'ed makovsky', 'forward', '6 - 5', '194', 'freshman', 'cicero , illinois / morton high school'], ['25', 'robert peterson', 'center', '6 - 8', '235', 'junior', 'wayne , illinois'], ['26', 'irving bemoras', 'guard', '6 - 3 1 / 2', '185', 'junior', 'chicago , illinois / marshall high school'], ['27', 'jack follmer', 'center', '6 - 4', '200', 'senior', 'forrest , illinois'], ['33', 'clive follmer', 'forward', '6 - 4', '195', 'junior', 'forrest , illinois'], ['34', 'seymour gantman', 'guard', '5 - 7', '165', 'senior', 'chicago , illinois / marshall high school'], ['35', 'ren alde', 'guard', '6 - 2', '180', 'senior', 'pana , illinois'], ['37', 'rod fletcher ( captain )', 'guard', '6 - 4', '194', 'senior', 'champaign , illinois'], ['38', 'herb gerecke', 'guard', '6 - 1', '180', 'senior', 'pekin , illinois'], ['41', 'max hooper', 'forward', '6 - 5', '200', 'sophomore', 'mt vernon , illinois']]

casey martin

https://en.wikipedia.org/wiki/Casey_Martin

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1697190-2.html.csv

superlative

casey martin 's best finish in a tournament on the pga tour was in 2000 when he finished tied 17th .

{'scope': 'all', 'col_superlative': '5', 'row_superlative': '2', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '1', 'subset': None}

{'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'best finish'], 'result': 't - 17', 'ind': 0, 'tostr': 'max { all_rows ; best finish }', 'tointer': 'the maximum best finish record of all rows is t - 17 .'}, 't - 17'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; best finish } ; t - 17 }', 'tointer': 'the maximum best finish record of all rows is t - 17 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'best finish'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; best finish }'}, 'year'], 'result': '2000', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; best finish } ; year }'}, '2000'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; best finish } ; year } ; 2000 }', 'tointer': 'the year record of the row with superlative best finish record is 2000 .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { max { all_rows ; best finish } ; t - 17 } ; eq { hop { argmax { all_rows ; best finish } ; year } ; 2000 } } = true', 'tointer': 'the maximum best finish record of all rows is t - 17 . the year record of the row with superlative best finish record is 2000 .'}

and { eq { max { all_rows ; best finish } ; t - 17 } ; eq { hop { argmax { all_rows ; best finish } ; year } ; 2000 } } = true

the maximum best finish record of all rows is t - 17 . the year record of the row with superlative best finish record is 2000 .

6

6

{'and_5': 5, 'result_6': 6, 'eq_1': 1, 'max_0': 0, 'all_rows_7': 7, 'best finish_8': 8, 't - 17_9': 9, 'eq_4': 4, 'num_hop_3': 3, 'argmax_2': 2, 'all_rows_10': 10, 'best finish_11': 11, 'year_12': 12, '2000_13': 13}

{'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'max_0': 'max', 'all_rows_7': 'all_rows', 'best finish_8': 'best finish', 't - 17_9': 't - 17', 'eq_4': 'eq', 'num_hop_3': 'num_hop', 'argmax_2': 'argmax', 'all_rows_10': 'all_rows', 'best finish_11': 'best finish', 'year_12': 'year', '2000_13': '2000'}

{'and_5': [6], 'result_6': [], 'eq_1': [5], 'max_0': [1], 'all_rows_7': [0], 'best finish_8': [0], 't - 17_9': [1], 'eq_4': [5], 'num_hop_3': [4], 'argmax_2': [3], 'all_rows_10': [2], 'best finish_11': [2], 'year_12': [3], '2000_13': [4]}

['year', 'tournaments played', 'cuts made', 'wins', 'best finish', 'earnings', 'money list rank']

[['1998', '3', '2', '0', 't - 23', '37221', '221'], ['2000', '29', '14', '0', 't - 17', '143248', '179'], ['2001', '2', '0', '0', 'cut', '0', 'n / a'], ['2002', '3', '0', '0', 'cut', '0', 'n / a'], ['2003', '1', '0', '0', 'cut', '0', 'n / a'], ['2004', '2', '2', '0', 't - 69', '15858', 'n / a'], ['2005', '1', '1', '0', 't - 65', '10547', 'n / a']]

list of the busiest airports in the united states

https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18047346-5.html.csv

superlative

the memphis international airport is the most busy airport in the country .

{'scope': 'all', 'col_superlative': '5', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'tonnes'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; tonnes }'}, 'airport name'], 'result': 'memphis international airport', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; tonnes } ; airport name }'}, 'memphis international airport'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; tonnes } ; airport name } ; memphis international airport } = true', 'tointer': 'select the row whose tonnes record of all rows is maximum . the airport name record of this row is memphis international airport .'}

eq { hop { argmax { all_rows ; tonnes } ; airport name } ; memphis international airport } = true

select the row whose tonnes record of all rows is maximum . the airport name record of this row is memphis international airport .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'tonnes_5': 5, 'airport name_6': 6, 'memphis international airport_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'tonnes_5': 'tonnes', 'airport name_6': 'airport name', 'memphis international airport_7': 'memphis international airport'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'tonnes_5': [0], 'airport name_6': [1], 'memphis international airport_7': [2]}

['rank', 'airport name', 'location', 'iata code', 'tonnes', '% chg 2010 / 11']

[['1', 'memphis international airport', 'memphis , tennessee', 'mem', '3916410', '0 0.0 %'], ['2', 'ted stevens anchorage international airport', 'anchorage , alaska', 'anc', '2543105', '0 3.9 %'], ['3', 'louisville international airport', 'louisville , kentucky', 'sdf', '2188422', '0 1.0 %'], ['4', 'miami international airport', 'miami , florida', 'mia', '1841929', '0 0.3 %'], ['5', 'los angeles international airport', 'los angeles , california', 'lax', '1681611', '0 3.8 %'], ['6', 'john f kennedy international airport', 'queens , new york', 'jfk', '1348992', '0 0.5 %'], ['7', "o'hare international airport", 'chicago , illinois', 'ord', '1311622', '0 4.7 %'], ['8', 'indianapolis international airport', 'indianapolis', 'ind', '0 971664', '0 4.0 %'], ['9', 'newark liberty international airport', 'newark , new jersey', 'ewr', '0 813209', '0 5.0 %']]

2004 amsterdam admirals season

https://en.wikipedia.org/wiki/2004_Amsterdam_Admirals_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24951872-2.html.csv

ordinal

the amsterdam admirals ' game against rhein fire recorded their 2nd highest attendance of the 2004 season .

{'row': '6', 'col': '8', 'order': '2', 'col_other': '4', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'attendance', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; attendance ; 2 }'}, 'opponent'], 'result': 'rhein fire', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; attendance ; 2 } ; opponent }'}, 'rhein fire'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; opponent } ; rhein fire } = true', 'tointer': 'select the row whose attendance record of all rows is 2nd maximum . the opponent record of this row is rhein fire .'}

eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; opponent } ; rhein fire } = true

select the row whose attendance record of all rows is 2nd maximum . the opponent record of this row is rhein fire .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '2_6': 6, 'opponent_7': 7, 'rhein fire_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', '2_6': '2', 'opponent_7': 'opponent', 'rhein fire_8': 'rhein fire'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '2_6': [0], 'opponent_7': [1], 'rhein fire_8': [2]}

['week', 'date', 'kickoff', 'opponent', 'final score', 'team record', 'game site', 'attendance']

[['1', 'saturday , april 3', '7:00 pm', 'frankfurt galaxy', 'l 11 - 34', '0 - 1', 'waldstadion', '21269'], ['2', 'saturday , april 10', '7:00 pm', 'berlin thunder', 'l 17 - 28', '0 - 2', 'amsterdam arena', '10763'], ['3', 'sunday , april 18', '2:00 pm', 'scottish claymores', 'w 3 - 0', '1 - 2', 'hampden park', '10971'], ['4', 'sunday , april 25', '3:00 pm', 'frankfurt galaxy', 'w 21 - 17 ot', '2 - 2', 'amsterdam arena', '10684'], ['5', 'sunday , may 2', '4:00 pm', 'berlin thunder', 'l 29 - 33', '2 - 3', 'olympic stadium', '12909'], ['6', 'sunday , may 9', '4:00 pm', 'rhein fire', 'l 13 - 20', '2 - 4', 'arena aufschalke', '18790'], ['7', 'saturday , may 15', '7:00 pm', 'cologne centurions', 'w 17 - 10', '3 - 4', 'amsterdam arena', '14437'], ['8', 'friday , may 21', '8:00 pm', 'scottish claymores', 'l 17 - 19', '3 - 5', 'amsterdam arena', '10738'], ['9', 'sunday , may 30', '4:00 pm', 'cologne centurions', 'w 23 - 18', '4 - 5', 'rheinenergiestadion', '9056']]

idaho vandals football

https://en.wikipedia.org/wiki/Idaho_Vandals_football

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15164733-4.html.csv

comparative

wayne walker was a higher overall pick than john yarno was .

{'row_1': '4', 'row_2': '9', 'col': '3', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'wayne walker'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to wayne walker .', 'tostr': 'filter_eq { all_rows ; player ; wayne walker }'}, 'overall pick'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; wayne walker } ; overall pick }', 'tointer': 'select the rows whose player record fuzzily matches to wayne walker . take the overall pick record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'john yarno'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to john yarno .', 'tostr': 'filter_eq { all_rows ; player ; john yarno }'}, 'overall pick'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; john yarno } ; overall pick }', 'tointer': 'select the rows whose player record fuzzily matches to john yarno . take the overall pick record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; player ; wayne walker } ; overall pick } ; hop { filter_eq { all_rows ; player ; john yarno } ; overall pick } } = true', 'tointer': 'select the rows whose player record fuzzily matches to wayne walker . take the overall pick record of this row . select the rows whose player record fuzzily matches to john yarno . take the overall pick record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; player ; wayne walker } ; overall pick } ; hop { filter_eq { all_rows ; player ; john yarno } ; overall pick } } = true

select the rows whose player record fuzzily matches to wayne walker . take the overall pick record of this row . select the rows whose player record fuzzily matches to john yarno . take the overall pick record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'player_7': 7, 'wayne walker_8': 8, 'overall pick_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'john yarno_12': 12, 'overall pick_13': 13}

{'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'player_7': 'player', 'wayne walker_8': 'wayne walker', 'overall pick_9': 'overall pick', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'john yarno_12': 'john yarno', 'overall pick_13': 'overall pick'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'wayne walker_8': [0], 'overall pick_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'john yarno_12': [1], 'overall pick_13': [3]}

['player', 'position', 'overall pick', 'round', 'nfl draft', 'franchise']

[['ray mcdonald', 'rb', '13', '1st', '1967', 'washington redskins'], ['mike iupati', 'g', '17', '1st', '2010', 'san francisco 49ers'], ['jerry kramer', 'g / pk', '39', '4th', '1958', 'green bay packers'], ['wayne walker', 'lb / pk', '44', '4th', '1958', 'detroit lions'], ['carl kiilsgaard', 't', '61', '5th', '1950', 'chicago cardinals'], ['ryan phillips', 'lb', '68', '3rd', '1997', 'new york giants'], ['jim prestel', 'dt', '70', '6th', '1959', 'cleveland browns'], ['jim norton', 's / p', '75', '7th', '1960', 'detroit lions'], ['john yarno', 'c', '87', '4th', '1977', 'seattle seahawks'], ['jeff robinson', 'de / te / ls', '98', '4th', '1993', 'denver broncos']]

glimt

https://en.wikipedia.org/wiki/FK_Bod%C3%B8/Glimt

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-159867-1.html.csv

unique

the 1978 - 79 season was the only season that glimt played against a team from luxembourg .

{'scope': 'all', 'row': '2', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'luxembourg', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'luxembourg'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to luxembourg .', 'tostr': 'filter_eq { all_rows ; country ; luxembourg }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; country ; luxembourg } }', 'tointer': 'select the rows whose country record fuzzily matches to luxembourg . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'luxembourg'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to luxembourg .', 'tostr': 'filter_eq { all_rows ; country ; luxembourg }'}, 'season'], 'result': '1978 - 79', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; country ; luxembourg } ; season }'}, '1978 - 79'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; country ; luxembourg } ; season } ; 1978 - 79 }', 'tointer': 'the season record of this unqiue row is 1978 - 79 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; country ; luxembourg } } ; eq { hop { filter_eq { all_rows ; country ; luxembourg } ; season } ; 1978 - 79 } } = true', 'tointer': 'select the rows whose country record fuzzily matches to luxembourg . there is only one such row in the table . the season record of this unqiue row is 1978 - 79 .'}

and { only { filter_eq { all_rows ; country ; luxembourg } } ; eq { hop { filter_eq { all_rows ; country ; luxembourg } ; season } ; 1978 - 79 } } = true

select the rows whose country record fuzzily matches to luxembourg . there is only one such row in the table . the season record of this unqiue row is 1978 - 79 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'country_7': 7, 'Luxembourg_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'season_9': 9, '1978 - 79_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'country_7': 'country', 'Luxembourg_8': 'luxembourg', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'season_9': 'season', '1978 - 79_10': '1978 - 79'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'country_7': [0], 'Luxembourg_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'season_9': [2], '1978 - 79_10': [3]}

['season', 'round', 'country', 'opponent', 'result']

[['1976 - 77', 'first round', 'italy', 'napoli', '0 - 2 , 0 - 1'], ['1978 - 79', 'first round', 'luxembourg', 'union luxembourg', '4 - 1 , 0 - 1'], ['1978 - 79', 'second round', 'italy', 'internazionale', '0 - 5 , 1 - 2'], ['1994 - 95', 'qualifying round', 'latvia', 'olimpija rīga', '6 - 0 , 0 - 0'], ['1994 - 95', 'first round', 'italy', 'sampdoria', '3 - 2 , 0 - 2'], ['1996 - 97', 'second qualifying round', 'israel', 'beitar jerusalem', '2 - 1 , 5 - 1'], ['1996 - 97', 'first round', 'turkey', 'trabzonspor', '1 - 2 , 1 - 3'], ['1999 - 2000', 'qualifying round', 'liechtenstein', 'vaduz', '1 - 0 , 2 - 1'], ['1999 - 2000', 'first round', 'germany', 'werder bremen', '0 - 5 , 1 - 1'], ['2004 - 05', 'second qualifying round', 'estonia', 'levadia tallinn', '2 - 1 , 1 - 2 ( 8 - 7 p )'], ['2004 - 05', 'first round', 'turkey', 'beşiktaş', '1 - 1 , 0 - 1']]

nemzeti bajnokság i ( men 's handball )

https://en.wikipedia.org/wiki/Nemzeti_Bajnoks%C3%A1g_I_%28men%27s_handball%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12777591-5.html.csv

superlative

the most titles in men 's handball were won by the budapest team .

{'scope': 'all', 'col_superlative': '3', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'titles'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; titles }'}, 'city'], 'result': 'budapest', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; titles } ; city }'}, 'budapest'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; titles } ; city } ; budapest } = true', 'tointer': 'select the row whose titles record of all rows is maximum . the city record of this row is budapest .'}

eq { hop { argmax { all_rows ; titles } ; city } ; budapest } = true

select the row whose titles record of all rows is maximum . the city record of this row is budapest .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'titles_5': 5, 'city_6': 6, 'budapest_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'titles_5': 'titles', 'city_6': 'city', 'budapest_7': 'budapest'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'titles_5': [0], 'city_6': [1], 'budapest_7': [2]}

['rank', 'city', 'titles', 'winning clubs', 'last victory']

[['1', 'budapest', '26', 'honvéd spartacus elektromos se vörös meteor újpest', '1991'], ['2', 'veszprém', '20', 'veszprém ( 20 )', '2012'], ['3', 'tatabánya', '4', 'tatabánya ( 4 )', '1984'], ['4', 'győr', '3', 'győr ( 3 )', '1990'], ['5', 'szeged', '2', 'szeged ( 2 )', '2007'], ['6', 'dunaújváros', '1', 'dunaferr se ( 1 )', '2000'], ['6', 'debrecen', '1', 'debrecen ( 1 )', '1975']]

95th united states congress

https://en.wikipedia.org/wiki/95th_United_States_Congress

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1013168-3.html.csv

majority

the majority of vacant seats were not filled during the term of the 95th united states congress .

{'scope': 'subset', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'not filled this term', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'vacant'}}

{'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'successor', 'vacant'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; successor ; vacant }', 'tointer': 'select the rows whose successor record fuzzily matches to vacant .'}, 'date successor seated', 'not filled this term'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose successor record fuzzily matches to vacant . for the date successor seated records of these rows , most of them fuzzily match to not filled this term .', 'tostr': 'most_eq { filter_eq { all_rows ; successor ; vacant } ; date successor seated ; not filled this term } = true'}

most_eq { filter_eq { all_rows ; successor ; vacant } ; date successor seated ; not filled this term } = true

select the rows whose successor record fuzzily matches to vacant . for the date successor seated records of these rows , most of them fuzzily match to not filled this term .

2

2

{'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'successor_4': 4, 'vacant_5': 5, 'date successor seated_6': 6, 'not filled this term_7': 7}

{'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'successor_4': 'successor', 'vacant_5': 'vacant', 'date successor seated_6': 'date successor seated', 'not filled this term_7': 'not filled this term'}

{'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'successor_4': [0], 'vacant_5': [0], 'date successor seated_6': [1], 'not filled this term_7': [1]}

['district', 'vacator', 'reason for change', 'successor', 'date successor seated']

[['louisiana 1st', 'richard a tonry ( d )', 'forced to resign may 4 , 1977', 'bob livingston ( r )', 'august 27 , 1977'], ['new york 21st', 'robert garcía ( r - l )', 'changed parties february 21 , 1978', 'robert garcía ( d )', 'february 21 , 1978'], ['tennessee 5th', 'clifford allen ( d )', 'died june 18 , 1978', 'vacant', 'not filled this term'], ['california 18th', 'william m ketchum ( r )', 'died june 24 , 1978', 'vacant', 'not filled this term'], ['illinois 1st', 'ralph metcalfe ( d )', 'died october 10 , 1978', 'vacant', 'not filled this term'], ['maryland 6th', 'goodloe byron ( d )', 'died october 11 , 1978', 'vacant', 'not filled this term'], ['wisconsin 6th', 'william a steiger ( r )', 'died december 4 , 1978', 'vacant', 'not filled this term'], ['wyoming at - large', 'teno roncalio ( d )', 'resigned december 30 , 1978', 'vacant', 'not filled this term'], ['california 3rd', 'john e moss ( d )', 'resigned december 31 , 1978', 'vacant', 'not filled this term'], ['california 14th', 'john j mcfall ( d )', 'resigned december 31 , 1978', 'vacant', 'not filled this term'], ['california 33rd', 'del m clawson ( r )', 'resigned december 31 , 1978', 'vacant', 'not filled this term'], ['kansas 5th', 'joe skubitz ( r )', 'resigned december 31 , 1978', 'vacant', 'not filled this term'], ['michigan 10th', 'elford a cederberg ( r )', 'resigned december 31 , 1978', 'vacant', 'not filled this term'], ['new jersey 14th', 'joseph a lefante ( d )', 'resigned december 31 , 1978', 'vacant', 'not filled this term'], ['new york 9th', 'james delaney ( d )', 'resigned december 31 , 1978', 'vacant', 'not filled this term'], ['texas 6th', 'olin e teague ( d )', 'resigned december 31 , 1978', 'vacant', 'not filled this term'], ['texas 11th', 'william r poage ( d )', 'resigned december 31 , 1978', 'vacant', 'not filled this term']]

1999 - 2000 philadelphia flyers season

https://en.wikipedia.org/wiki/1999%E2%80%932000_Philadelphia_Flyers_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14173105-18.html.csv

unique

in the 1999-2000 philadelphia flyers season , the only player from sweden is david nystrom .

{'scope': 'all', 'row': '6', 'col': '4', 'col_other': '2', 'criterion': 'equal', 'value': 'sweden', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'sweden'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to sweden .', 'tostr': 'filter_eq { all_rows ; nationality ; sweden }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; nationality ; sweden } }', 'tointer': 'select the rows whose nationality record fuzzily matches to sweden . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'sweden'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to sweden .', 'tostr': 'filter_eq { all_rows ; nationality ; sweden }'}, 'player'], 'result': 'david nystrom', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nationality ; sweden } ; player }'}, 'david nystrom'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; nationality ; sweden } ; player } ; david nystrom }', 'tointer': 'the player record of this unqiue row is david nystrom .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; nationality ; sweden } } ; eq { hop { filter_eq { all_rows ; nationality ; sweden } ; player } ; david nystrom } } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to sweden . there is only one such row in the table . the player record of this unqiue row is david nystrom .'}

and { only { filter_eq { all_rows ; nationality ; sweden } } ; eq { hop { filter_eq { all_rows ; nationality ; sweden } ; player } ; david nystrom } } = true

select the rows whose nationality record fuzzily matches to sweden . there is only one such row in the table . the player record of this unqiue row is david nystrom .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'nationality_7': 7, 'sweden_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'david nystrom_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'nationality_7': 'nationality', 'sweden_8': 'sweden', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'david nystrom_10': 'david nystrom'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'nationality_7': [0], 'sweden_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'david nystrom_10': [3]}

['round', 'player', 'position', 'nationality', 'college / junior / club team ( league )']

[['1', 'maxime ouellet', 'goaltender', 'canada', 'quebec remparts ( qmjhl )'], ['4', 'jeff feniak', 'defense', 'canada', 'calgary hitmen ( whl )'], ['6', 'konstantin rudenko', 'forward', 'russia', 'severstal cherepovets ( rus )'], ['7', 'pavel kasparik', 'center', 'czech republic', 'ihc pisek ( cze )'], ['7', 'vaclav pletka', 'left wing', 'czech republic', 'hc oceláři třinec ( cze )'], ['8', 'david nystrom', 'right wing', 'sweden', 'frölunda hc ( elitserien )']]

swimming at the 2000 summer olympics - women 's 200 metre individual medley

https://en.wikipedia.org/wiki/Swimming_at_the_2000_Summer_Olympics_%E2%80%93_Women%27s_200_metre_individual_medley

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12446647-4.html.csv

ordinal

marianne limpert had the third fastest swimming time at the 2000 summer olympics - women 's 200 metre individual medley .

{'row': '3', 'col': '5', 'order': '3', 'col_other': '3', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'time', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; time ; 3 }'}, 'name'], 'result': 'marianne limpert', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; time ; 3 } ; name }'}, 'marianne limpert'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; time ; 3 } ; name } ; marianne limpert } = true', 'tointer': 'select the row whose time record of all rows is 3rd minimum . the name record of this row is marianne limpert .'}

eq { hop { nth_argmin { all_rows ; time ; 3 } ; name } ; marianne limpert } = true

select the row whose time record of all rows is 3rd minimum . the name record of this row is marianne limpert .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'time_5': 5, '3_6': 6, 'name_7': 7, 'marianne limpert_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'time_5': 'time', '3_6': '3', 'name_7': 'name', 'marianne limpert_8': 'marianne limpert'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'time_5': [0], '3_6': [0], 'name_7': [1], 'marianne limpert_8': [2]}

['rank', 'lane', 'name', 'nationality', 'time']

[['1', '4', 'beatrice cäƒslaru', 'romania', '2:13.31'], ['2', '5', 'joanne malar', 'canada', '2:13.59'], ['3', '3', 'marianne limpert', 'canada', '2:13.90'], ['4', '6', 'gabrielle rose', 'united states', '2:14.40'], ['5', '2', 'federica biscia', 'italy', '2:15.71'], ['6', '1', 'elli overton', 'australia', '2:15.74'], ['7', '7', 'yseult gervy', 'belgium', '2:17.19'], ['8', '8', 'sabine herbst', 'germany', '2:17.51']]

los angeles lakers all - time roster

https://en.wikipedia.org/wiki/Los_Angeles_Lakers_all-time_roster

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10560886-17.html.csv

majority

all players in the los angeles lakers all - time roster are from the united states .

{'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'united states', 'subset': None}

{'func': 'all_str_eq', 'args': ['all_rows', 'nationality', 'united states'], 'result': True, 'ind': 0, 'tointer': 'for the nationality records of all rows , all of them fuzzily match to united states .', 'tostr': 'all_eq { all_rows ; nationality ; united states } = true'}

all_eq { all_rows ; nationality ; united states } = true

for the nationality records of all rows , all of them fuzzily match to united states .

1

1

{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'nationality_3': 3, 'united states_4': 4}

{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'nationality_3': 'nationality', 'united states_4': 'united states'}

{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'nationality_3': [0], 'united states_4': [0]}

['player', 'nationality', 'position', 'from', 'school / country']

[['jannero pargo', 'united states', 'guard', '2002', 'arkansas'], ['parker , smush smush parker', 'united states', 'guard', '2005', 'fordham'], ['myles patrick', 'united states', 'forward', '1980', 'auburn'], ['ruben patterson', 'united states', 'guard / forward', '1998', 'cincinnati'], ['jim paxson', 'united states', 'guard / forward', '1956', 'dayton'], ['gary payton', 'united states', 'guard', '2003', 'oregon state'], ['peeler , anthony anthony peeler', 'united states', 'guard', '1992', 'missouri'], ['mike penberthy', 'united states', 'guard', '2000', "the master 's college"], ['sam perkins', 'united states', 'forward / center', '1990', 'north carolina'], ['john pilch', 'united states', 'forward', '1951', 'wyoming'], ['jim pollard', 'united states', 'forward / center', '1949', 'stanford'], ['powell , josh josh powell', 'united states', 'forward', '2008', 'north carolina state'], ['jim price', 'united states', 'guard', '1972 , 1978', 'louisville'], ['laron profit', 'united states', 'guard / forward', '2005', 'maryland']]

1997 european judo championships

https://en.wikipedia.org/wiki/1997_European_Judo_Championships

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11755180-3.html.csv

majority

most of the competing nations won zero gold medals .

{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': '0', 'subset': None}

{'func': 'most_eq', 'args': ['all_rows', 'gold', '0'], 'result': True, 'ind': 0, 'tointer': 'for the gold records of all rows , most of them are equal to 0 .', 'tostr': 'most_eq { all_rows ; gold ; 0 } = true'}

most_eq { all_rows ; gold ; 0 } = true

for the gold records of all rows , most of them are equal to 0 .

1

1

{'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'gold_3': 3, '0_4': 4}

{'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'gold_3': 'gold', '0_4': '0'}

{'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'gold_3': [0], '0_4': [0]}

['rank', 'nation', 'gold', 'silver', 'bronze', 'total']

[['1', 'belgium', '6', '0', '3', '9'], ['2 =', 'germany', '2', '2', '2', '7'], ['2 =', 'netherlands', '2', '2', '2', '6'], ['4', 'turkey', '2', '0', '1', '3'], ['5', 'france', '1', '3', '6', '10'], ['6', 'belarus', '1', '2', '1', '4'], ['7', 'georgia', '1', '1', '0', '2'], ['8', 'poland', '1', '0', '4', '5'], ['9', 'great britain', '0', '2', '3', '5'], ['10', 'spain', '0', '2', '1', '3'], ['11', 'austria', '0', '1', '1', '2'], ['12', 'czech republic', '0', '1', '0', '1'], ['13', 'russia', '0', '0', '2', '2'], ['14 =', 'estonia', '0', '0', '1', '1'], ['14 =', 'italy', '0', '0', '1', '1'], ['14 =', 'lithuania', '0', '0', '1', '1'], ['14 =', 'romania', '0', '0', '1', '1'], ['14 =', 'portugal', '0', '0', '1', '1'], ['14 =', 'yugoslavia', '0', '0', '1', '1']]

australian technology network

https://en.wikipedia.org/wiki/Australian_Technology_Network

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1187124-1.html.csv

unique

queensland university of technology is the only austrialian technology network university ranked in the top 300 in the world university rankings .

{'scope': 'all', 'row': '2', 'col': '5', 'col_other': '1', 'criterion': 'less_than', 'value': '300', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'the world university rankings 2012 - 13', '300'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose the world university rankings 2012 - 13 record is less than 300 .', 'tostr': 'filter_less { all_rows ; the world university rankings 2012 - 13 ; 300 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; the world university rankings 2012 - 13 ; 300 } }', 'tointer': 'select the rows whose the world university rankings 2012 - 13 record is less than 300 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'the world university rankings 2012 - 13', '300'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose the world university rankings 2012 - 13 record is less than 300 .', 'tostr': 'filter_less { all_rows ; the world university rankings 2012 - 13 ; 300 }'}, 'university'], 'result': 'queensland university of technology', 'ind': 2, 'tostr': 'hop { filter_less { all_rows ; the world university rankings 2012 - 13 ; 300 } ; university }'}, 'queensland university of technology'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_less { all_rows ; the world university rankings 2012 - 13 ; 300 } ; university } ; queensland university of technology }', 'tointer': 'the university record of this unqiue row is queensland university of technology .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_less { all_rows ; the world university rankings 2012 - 13 ; 300 } } ; eq { hop { filter_less { all_rows ; the world university rankings 2012 - 13 ; 300 } ; university } ; queensland university of technology } } = true', 'tointer': 'select the rows whose the world university rankings 2012 - 13 record is less than 300 . there is only one such row in the table . the university record of this unqiue row is queensland university of technology .'}

and { only { filter_less { all_rows ; the world university rankings 2012 - 13 ; 300 } } ; eq { hop { filter_less { all_rows ; the world university rankings 2012 - 13 ; 300 } ; university } ; queensland university of technology } } = true

select the rows whose the world university rankings 2012 - 13 record is less than 300 . there is only one such row in the table . the university record of this unqiue row is queensland university of technology .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_less_0': 0, 'all_rows_6': 6, 'the world university rankings 2012 - 13_7': 7, '300_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'university_9': 9, 'queensland university of technology_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_less_0': 'filter_less', 'all_rows_6': 'all_rows', 'the world university rankings 2012 - 13_7': 'the world university rankings 2012 - 13', '300_8': '300', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'university_9': 'university', 'queensland university of technology_10': 'queensland university of technology'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_less_0': [1, 2], 'all_rows_6': [0], 'the world university rankings 2012 - 13_7': [0], '300_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'university_9': [2], 'queensland university of technology_10': [3]}

['university', 'location', 'year of foundation', 'university status', 'the world university rankings 2012 - 13', 'academic ranking of world universities 2012', 'qs world university rankings 2012']

[['curtin university', 'perth , wa', '1902', '1986', 'not ranked', '401 - 500', '258'], ['queensland university of technology', 'brisbane , qld', '1908', '1989', '251 - 275', 'not ranked', '281'], ['royal melbourne institute of technology', 'melbourne , vic', '1887', '1992', 'not ranked', 'not ranked', '246'], ['university of south australia', 'adelaide , sa', '1856', '1991', '301 - 350', 'not ranked', '293'], ['university of technology , sydney', 'sydney , nsw', '1843', '1988', '351 - 400', '401 - 500', '284']]

1997 - 98 toronto raptors season

https://en.wikipedia.org/wiki/1997%E2%80%9398_Toronto_Raptors_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13619053-9.html.csv

unique

during april of the 1997 - 98 toronto raptors season , the team only won once , against new jersey .

{'scope': 'all', 'row': '8', 'col': '4', 'col_other': '3', 'criterion': 'fuzzily_match', 'value': 'w', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', 'w'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose score record fuzzily matches to w .', 'tostr': 'filter_eq { all_rows ; score ; w }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; score ; w } }', 'tointer': 'select the rows whose score record fuzzily matches to w . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', 'w'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose score record fuzzily matches to w .', 'tostr': 'filter_eq { all_rows ; score ; w }'}, 'team'], 'result': 'new jersey', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; score ; w } ; team }'}, 'new jersey'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; score ; w } ; team } ; new jersey }', 'tointer': 'the team record of this unqiue row is new jersey .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; score ; w } } ; eq { hop { filter_eq { all_rows ; score ; w } ; team } ; new jersey } } = true', 'tointer': 'select the rows whose score record fuzzily matches to w . there is only one such row in the table . the team record of this unqiue row is new jersey .'}

and { only { filter_eq { all_rows ; score ; w } } ; eq { hop { filter_eq { all_rows ; score ; w } ; team } ; new jersey } } = true

select the rows whose score record fuzzily matches to w . there is only one such row in the table . the team record of this unqiue row is new jersey .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'score_7': 7, 'w_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'team_9': 9, 'new jersey_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'score_7': 'score', 'w_8': 'w', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team_9': 'team', 'new jersey_10': 'new jersey'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'score_7': [0], 'w_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'team_9': [2], 'new jersey_10': [3]}

['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']

[['72', 'april 1', 'atlanta', 'l 91 - 105 ( ot )', 'doug christie , gary trent ( 14 )', 'marcus camby , tracy mcgrady ( 9 )', 'doug christie ( 3 )', 'georgia dome 10441', '15 - 57'], ['73', 'april 3', 'washington', 'l 112 - 120 ( ot )', 'dee brown ( 30 )', 'gary trent ( 10 )', 'dee brown ( 6 )', 'mci center 18324', '15 - 58'], ['74', 'april 5', 'philadelphia', 'l 104 - 116 ( ot )', 'gary trent ( 25 )', 'tracy mcgrady ( 13 )', 'dee brown ( 6 )', 'corestates center 15808', '15 - 59'], ['75', 'april 7', 'milwaukee', 'l 105 - 114 ( ot )', 'doug christie ( 20 )', 'reggie slater ( 8 )', 'doug christie ( 5 )', 'bradley center 13288', '15 - 60'], ['76', 'april 8', 'milwaukee', 'l 100 - 107 ( ot )', 'gary trent ( 24 )', 'chauncey billups , tracy mcgrady ( 9 )', 'doug christie ( 8 )', 'skydome 14168', '15 - 61'], ['77', 'april 10', 'miami', 'l 105 - 111 ( ot )', 'doug christie ( 26 )', 'tracy mcgrady ( 15 )', 'doug christie ( 7 )', 'skydome 16111', '15 - 62'], ['78', 'april 12', 'new jersey', 'l 109 - 116 ( ot )', 'dee brown ( 30 )', 'marcus camby ( 11 )', 'tracy mcgrady ( 6 )', 'skydome 14088', '15 - 63'], ['79', 'april 14', 'new jersey', 'w 96 - 92 ( ot )', 'doug christie ( 23 )', 'marcus camby ( 12 )', 'chauncey billups , doug christie ( 4 )', 'continental airlines arena 17521', '16 - 63'], ['80', 'april 16', 'new york', 'l 79 - 108 ( ot )', 'doug christie ( 14 )', 'doug christie , tracy mcgrady ( 7 )', 'dee brown ( 4 )', 'madison square garden 19763', '16 - 64'], ['81', 'april 17', 'indiana', 'l 98 - 107 ( ot )', 'doug christie ( 24 )', 'gary trent ( 12 )', 'chauncey billups ( 5 )', 'market square arena 16059', '16 - 65']]

2008 - 09 san antonio spurs season

https://en.wikipedia.org/wiki/2008%E2%80%9309_San_Antonio_Spurs_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17288845-11.html.csv

count

during this period of the 2008-09 san antonio spurs season , the san antonio spurs played two games at the american airlines center .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'american airlines center', 'result': '2', 'col': '8', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location attendance', 'american airlines center'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location attendance record fuzzily matches to american airlines center .', 'tostr': 'filter_eq { all_rows ; location attendance ; american airlines center }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location attendance ; american airlines center } }', 'tointer': 'select the rows whose location attendance record fuzzily matches to american airlines center . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location attendance ; american airlines center } } ; 2 } = true', 'tointer': 'select the rows whose location attendance record fuzzily matches to american airlines center . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; location attendance ; american airlines center } } ; 2 } = true

select the rows whose location attendance record fuzzily matches to american airlines center . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'location attendance_5': 5, 'american airlines center_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'location attendance_5': 'location attendance', 'american airlines center_6': 'american airlines center', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location attendance_5': [0], 'american airlines center_6': [0], '2_7': [2]}

['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'series']

[['1', 'april 18', 'dallas', 'l 97 - 105 ( ot )', 'tim duncan ( 27 )', 'tim duncan ( 9 )', 'tony parker ( 8 )', 'at & t center 18797', '0 - 1'], ['2', 'april 20', 'dallas', 'w 105 - 84 ( ot )', 'tony parker ( 38 )', 'tim duncan ( 11 )', 'tony parker ( 8 )', 'at & t center 18797', '1 - 1'], ['3', 'april 23', 'dallas', 'l 67 - 88 ( ot )', 'tony parker ( 12 )', 'kurt thomas ( 10 )', 'tony parker ( 3 )', 'american airlines center 20491', '1 - 2'], ['4', 'april 25', 'dallas', 'l 90 - 99 ( ot )', 'tony parker ( 43 )', 'tim duncan ( 10 )', 'tim duncan ( 7 )', 'american airlines center 20829', '1 - 3'], ['5', 'april 28', 'dallas', 'l 93 - 106 ( ot )', 'tim duncan ( 31 )', 'tim duncan ( 12 )', 'tony parker ( 6 )', 'at & t center 20829', '1 - 4']]

1960 philadelphia eagles season

https://en.wikipedia.org/wiki/1960_Philadelphia_Eagles_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16678519-2.html.csv

comparative

the game against the cleveland browns drew a bigger crowd than the game against the dallas cowboys .

{'row_1': '1', 'row_2': '2', 'col': '5', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'cleveland browns'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to cleveland browns .', 'tostr': 'filter_eq { all_rows ; opponent ; cleveland browns }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; cleveland browns } ; attendance }', 'tointer': 'select the rows whose opponent record fuzzily matches to cleveland browns . take the attendance record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'dallas cowboys'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to dallas cowboys .', 'tostr': 'filter_eq { all_rows ; opponent ; dallas cowboys }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; dallas cowboys } ; attendance }', 'tointer': 'select the rows whose opponent record fuzzily matches to dallas cowboys . take the attendance record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; opponent ; cleveland browns } ; attendance } ; hop { filter_eq { all_rows ; opponent ; dallas cowboys } ; attendance } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to cleveland browns . take the attendance record of this row . select the rows whose opponent record fuzzily matches to dallas cowboys . take the attendance record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; opponent ; cleveland browns } ; attendance } ; hop { filter_eq { all_rows ; opponent ; dallas cowboys } ; attendance } } = true

select the rows whose opponent record fuzzily matches to cleveland browns . take the attendance record of this row . select the rows whose opponent record fuzzily matches to dallas cowboys . take the attendance record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opponent_7': 7, 'cleveland browns_8': 8, 'attendance_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'dallas cowboys_12': 12, 'attendance_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opponent_7': 'opponent', 'cleveland browns_8': 'cleveland browns', 'attendance_9': 'attendance', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'dallas cowboys_12': 'dallas cowboys', 'attendance_13': 'attendance'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'cleveland browns_8': [0], 'attendance_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'dallas cowboys_12': [1], 'attendance_13': [3]}

['week', 'date', 'opponent', 'result', 'attendance']

[['1', 'september 25 , 1960', 'cleveland browns', 'l 24 - 41', '56303'], ['2', 'september 30 , 1960', 'dallas cowboys', 'w 27 - 25', '18500'], ['3', 'october 9 , 1960', 'st louis cardinals', 'w 31 - 27', '33701'], ['4', 'october 16 , 1960', 'detroit lions', 'w 28 - 10', '38065'], ['5', 'october 23 , 1960', 'cleveland browns', 'w 31 - 29', '64850'], ['7', 'november 6 , 1960', 'pittsburgh steelers', 'w 34 - 7', '58324'], ['8', 'november 13 , 1960', 'washington redskins', 'w 19 - 13', '39361'], ['9', 'november 20 , 1960', 'new york giants', 'w 17 - 10', '63571'], ['10', 'november 27 , 1960', 'new york giants', 'w 31 - 23', '60547'], ['11', 'december 4 , 1960', 'st louis cardinals', 'w 20 - 6', '21358'], ['12', 'december 11 , 1960', 'pittsburgh steelers', 'l 21 - 27', '22101'], ['13', 'december 18 , 1960', 'washington redskins', 'w 38 - 28', '20558']]

2010 - 11 boston celtics season

https://en.wikipedia.org/wiki/2010%E2%80%9311_Boston_Celtics_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27722408-10.html.csv

count

in the 2010 - 11 boston celtics season , when the celtics won , there were three times that ray allen had at least a share of the high points .

{'scope': 'subset', 'criterion': 'equal', 'value': 'ray allen', 'result': '3', 'col': '5', 'subset': {'col': '4', 'criterion': 'fuzzily_match', 'value': 'w'}}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', 'w'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; score ; w }', 'tointer': 'select the rows whose score record fuzzily matches to w .'}, 'high points', 'ray allen'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high points record fuzzily matches to ray allen .', 'tostr': 'filter_eq { filter_eq { all_rows ; score ; w } ; high points ; ray allen }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; score ; w } ; high points ; ray allen } }', 'tointer': 'select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high points record fuzzily matches to ray allen . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; score ; w } ; high points ; ray allen } } ; 3 } = true', 'tointer': 'select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high points record fuzzily matches to ray allen . the number of such rows is 3 .'}

eq { count { filter_eq { filter_eq { all_rows ; score ; w } ; high points ; ray allen } } ; 3 } = true

select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high points record fuzzily matches to ray allen . the number of such rows is 3 .

4

4

{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'score_6': 6, 'w_7': 7, 'high points_8': 8, 'ray allen_9': 9, '3_10': 10}

{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'score_6': 'score', 'w_7': 'w', 'high points_8': 'high points', 'ray allen_9': 'ray allen', '3_10': '3'}

{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'score_6': [0], 'w_7': [0], 'high points_8': [1], 'ray allen_9': [1], '3_10': [3]}

['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']

[['59', 'march 2', 'phoenix', 'w 115 - 103 ( ot )', 'kevin garnett ( 28 )', 'paul pierce ( 13 )', 'rajon rondo ( 15 )', 'td garden 18624', '44 - 15'], ['60', 'march 4', 'golden state', 'w 107 - 103 ( ot )', 'ray allen , paul pierce ( 27 )', 'paul pierce ( 7 )', 'rajon rondo ( 16 )', 'td garden 18624', '45 - 15'], ['61', 'march 6', 'milwaukee', 'w 89 - 83 ( ot )', 'paul pierce ( 23 )', 'kevin garnett ( 11 )', 'rajon rondo ( 8 )', 'bradley center 16110', '46 - 15'], ['62', 'march 9', 'la clippers', 'l 103 - 108 ( ot )', 'ray allen ( 23 )', 'nenad krstić ( 9 )', 'rajon rondo ( 9 )', 'td garden 18624', '46 - 16'], ['63', 'march 11', 'philadelphia', 'l 86 - 89 ( ot )', 'jeff green ( 18 )', 'nenad krstić ( 15 )', 'kevin garnett , paul pierce , rajon rondo ( 5 )', 'wells fargo center 20614', '46 - 17'], ['64', 'march 13', 'milwaukee', 'w 87 - 56 ( ot )', 'ray allen ( 17 )', 'nenad krstić ( 14 )', 'carlos arroyo ( 6 )', 'td garden 18624', '47 - 17'], ['65', 'march 14', 'new jersey', 'l 79 - 88 ( ot )', 'ray allen ( 19 )', 'glen davis ( 14 )', 'rajon rondo ( 9 )', 'prudential center 18711', '47 - 18'], ['66', 'march 16', 'indiana', 'w 92 - 80 ( ot )', 'paul pierce ( 20 )', 'glen davis ( 9 )', 'rajon rondo ( 8 )', 'td garden 18624', '48 - 18'], ['67', 'march 18', 'houston', 'l 77 - 93 ( ot )', 'jeff green ( 17 )', 'glen davis ( 7 )', 'rajon rondo ( 6 )', 'toyota center 18412', '48 - 19'], ['68', 'march 19', 'new orleans', 'w 89 - 85 ( ot )', 'ray allen , glen davis ( 20 )', 'kevin garnett ( 9 )', 'paul pierce ( 6 )', 'new orleans arena 18018', '49 - 19'], ['69', 'march 21', 'new york', 'w 96 - 86 ( ot )', 'kevin garnett ( 24 )', 'kevin garnett ( 11 )', 'rajon rondo ( 12 )', 'madison square garden 19763', '50 - 19'], ['70', 'march 23', 'memphis', 'l 87 - 90 ( ot )', 'paul pierce ( 22 )', 'rajon rondo ( 11 )', 'rajon rondo ( 11 )', 'td garden 18624', '50 - 20'], ['71', 'march 25', 'charlotte', 'l 81 - 83 ( ot )', 'paul pierce ( 18 )', 'kevin garnett ( 9 )', 'rajon rondo ( 5 )', 'td garden 18624', '50 - 21'], ['72', 'march 27', 'minnesota', 'w 85 - 82 ( ot )', 'paul pierce ( 23 )', 'kevin garnett ( 13 )', 'kevin garnett , delonte west ( 5 )', 'target center 19178', '51 - 21'], ['73', 'march 28', 'indiana', 'l 100 - 107 ( ot )', 'paul pierce ( 23 )', 'kevin garnett , paul pierce ( 6 )', 'rajon rondo ( 9 )', 'conseco fieldhouse 15932', '51 - 22']]

2008 brazilian grand prix

https://en.wikipedia.org/wiki/2008_Brazilian_Grand_Prix

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14270784-2.html.csv

count

2 drivers in the 2008 brazilian grand prix had cars constructed by honda .

{'scope': 'all', 'criterion': 'equal', 'value': 'honda', 'result': '2', 'col': '2', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'constructor', 'honda'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose constructor record fuzzily matches to honda .', 'tostr': 'filter_eq { all_rows ; constructor ; honda }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; constructor ; honda } }', 'tointer': 'select the rows whose constructor record fuzzily matches to honda . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; constructor ; honda } } ; 2 } = true', 'tointer': 'select the rows whose constructor record fuzzily matches to honda . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; constructor ; honda } } ; 2 } = true

select the rows whose constructor record fuzzily matches to honda . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'constructor_5': 5, 'honda_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'constructor_5': 'constructor', 'honda_6': 'honda', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'constructor_5': [0], 'honda_6': [0], '2_7': [2]}

['driver', 'constructor', 'laps', 'time / retired', 'grid']

[['felipe massa', 'ferrari', '71', '1:34:11.435', '1'], ['fernando alonso', 'renault', '71', '+ 13.298', '6'], ['kimi räikkönen', 'ferrari', '71', '+ 16.235', '3'], ['sebastian vettel', 'toro rosso - ferrari', '71', '+ 38.011', '7'], ['lewis hamilton', 'mclaren - mercedes', '71', '+ 38.907', '4'], ['timo glock', 'toyota', '71', '+ 44.368', '10'], ['heikki kovalainen', 'mclaren - mercedes', '71', '+ 55.074', '5'], ['jarno trulli', 'toyota', '71', '+ 1:08.433', '2'], ['mark webber', 'red bull - renault', '71', '+ 1:19.666', '12'], ['nick heidfeld', 'bmw sauber', '70', '+ 1 lap', '8'], ['robert kubica', 'bmw sauber', '70', '+ 1 lap', '13'], ['nico rosberg', 'williams - toyota', '70', '+ 1 lap', '18'], ['jenson button', 'honda', '70', '+ 1 lap', '17'], ['sébastien bourdais', 'toro rosso - ferrari', '70', '+ 1 lap', '9'], ['rubens barrichello', 'honda', '70', '+ 1 lap', '15'], ['adrian sutil', 'force india - ferrari', '69', '+ 2 laps', '20'], ['kazuki nakajima', 'williams - toyota', '69', '+ 2 laps', '16'], ['giancarlo fisichella', 'force india - ferrari', '69', '+ 2 laps', '19'], ['nelson piquet jr', 'renault', '0', 'accident', '11'], ['david coulthard', 'red bull - renault', '0', 'collision', '14']]

flavio cipolla

https://en.wikipedia.org/wiki/Flavio_Cipolla

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16474033-6.html.csv

comparative

flavio cipolla partnered with simon stadler before he partnered with paolo lorenzi .

{'row_1': '2', 'row_2': '17', 'col': '1', 'col_other': '4', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'partnering', 'simon stadler'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose partnering record fuzzily matches to simon stadler .', 'tostr': 'filter_eq { all_rows ; partnering ; simon stadler }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; partnering ; simon stadler } ; date }', 'tointer': 'select the rows whose partnering record fuzzily matches to simon stadler . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'partnering', 'paolo lorenzi'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose partnering record fuzzily matches to paolo lorenzi .', 'tostr': 'filter_eq { all_rows ; partnering ; paolo lorenzi }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; partnering ; paolo lorenzi } ; date }', 'tointer': 'select the rows whose partnering record fuzzily matches to paolo lorenzi . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; partnering ; simon stadler } ; date } ; hop { filter_eq { all_rows ; partnering ; paolo lorenzi } ; date } } = true', 'tointer': 'select the rows whose partnering record fuzzily matches to simon stadler . take the date record of this row . select the rows whose partnering record fuzzily matches to paolo lorenzi . take the date record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; partnering ; simon stadler } ; date } ; hop { filter_eq { all_rows ; partnering ; paolo lorenzi } ; date } } = true

select the rows whose partnering record fuzzily matches to simon stadler . take the date record of this row . select the rows whose partnering record fuzzily matches to paolo lorenzi . take the date record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'partnering_7': 7, 'simon stadler_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'partnering_11': 11, 'paolo lorenzi_12': 12, 'date_13': 13}

{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'partnering_7': 'partnering', 'simon stadler_8': 'simon stadler', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'partnering_11': 'partnering', 'paolo lorenzi_12': 'paolo lorenzi', 'date_13': 'date'}

{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'partnering_7': [0], 'simon stadler_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'partnering_11': [1], 'paolo lorenzi_12': [1], 'date_13': [3]}

['date', 'tournament', 'surface', 'partnering', 'opponents', 'score']

[['4 july 2005', 'mantova , italy', 'clay', 'alessandro motti', 'salvador navarro óscar serrano', '5 - 7 , 6 - 3 , 6 - 3'], ['2 august 2005', 'saransk , russia', 'clay', 'simon stadler', 'konstantin kravchuk alexander kudryavtsev', '7 - 6 ( 7 - 2 ) , 4 - 6 , 7 - 6 ( 7 - 3 )'], ['19 september 2005', 'banja luka , bosnia and herzegovina', 'clay', 'rainer eitzinger', 'jeroen masson stefan wauters', '4 - 6 , 6 - 3 , 6 - 3'], ['25 september 2006', 'bratislava , slovak republic', 'clay', 'marcel granollers', 'david marrero pablo santos', '7 - 6 ( 7 - 2 ) , 6 - 4'], ['9 october 2006', 'barcelona , spain', 'clay', 'tomas behrend', 'pablo andújar marcel granollers', '6 - 3 , 6 - 2'], ['26 march 2007', 'napoli , italy', 'clay', 'marcel granollers', 'marco crugnola alessio di mauro', '6 - 4 , 6 - 2'], ['30 april 2007', 'rome , italy', 'clay', 'marcel granollers', 'stefano galvani manuel jorquera', '3 - 6 , 6 - 1 ,'], ['1 january 2008', 'nouméa , new caledonia', 'hard', 'simone vagnozzi', 'jan mertl martin slanar', '6 - 4 , 6 - 4'], ['11 february 2008', 'belgrade , serbia', 'carpet', 'konstantinos economidis', 'alessandro motti filip polášek', '4 - 6 , 6 - 2 ,'], ['24 march 2008', 'barletta , italy', 'clay', 'marcel granollers', 'oliver marach michal mertiňák', '6 - 3 , 2 - 6 ,'], ['28 april 2008', 'rome , italy', 'clay', 'simone vagnozzi', 'paolo lorenzi giancarlo petrazzuolo', '6 - 3 , 6 - 3'], ['27 may 2008', 'alessandria , italy', 'clay', 'simone vagnozzi', 'matwé middelkoop melle van gemerden', '3 - 6 , 6 - 1 ,'], ['13 october 2008', 'tashkent , uzbekistan', 'hard', 'pavel šnobel', 'michail elgin alexandre kudryavtsev', '6 - 3 , 6 - 4'], ['28 february 2010', 'meknes , morocco', 'clay', 'pablo andújar', 'alexandr dolgopolov artem smirnov', '6 - 2 , 6 - 2'], ['12 september 2010', 'braşov , romania', 'clay', 'daniele giorgini', 'radu albot andrej ciumac', '6 - 3 , 6 - 4'], ['19 september 2010', 'todi , italy', 'clay', 'alessio di mauro', 'marcel granollers gerard granollers - pujol', '6 - 1 , 6 - 4'], ['3 april 2011', 'barranquilla , colombia', 'hard', 'paolo lorenzi', 'alejandro falla eduardo struvay', '6 - 3 , 6 - 4']]

variobahn

https://en.wikipedia.org/wiki/Variobahn

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14316789-1.html.csv

majority

all variobahn whose owner is mvv verkehr has 80 seats at least .

{'scope': 'subset', 'col': '8', 'most_or_all': 'all', 'criterion': 'greater_than_eq', 'value': '80', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'mvv verkehr'}}

{'func': 'all_greater_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'owner', 'mvv verkehr'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; owner ; mvv verkehr }', 'tointer': 'select the rows whose owner record fuzzily matches to mvv verkehr .'}, 'seats', '80'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose owner record fuzzily matches to mvv verkehr . for the seats records of these rows , all of them are greater than or equal to 80 .', 'tostr': 'all_greater_eq { filter_eq { all_rows ; owner ; mvv verkehr } ; seats ; 80 } = true'}

all_greater_eq { filter_eq { all_rows ; owner ; mvv verkehr } ; seats ; 80 } = true

select the rows whose owner record fuzzily matches to mvv verkehr . for the seats records of these rows , all of them are greater than or equal to 80 .

2

2

{'all_greater_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'owner_4': 4, 'mvv verkehr_5': 5, 'seats_6': 6, '80_7': 7}

{'all_greater_eq_1': 'all_greater_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'owner_4': 'owner', 'mvv verkehr_5': 'mvv verkehr', 'seats_6': 'seats', '80_7': '80'}

{'all_greater_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'owner_4': [0], 'mvv verkehr_5': [0], 'seats_6': [1], '80_7': [1]}

['system', 'owner', 'quantity', 'delivery', 'length', 'gauge', 'operation', 'seats', 'standing']

[['chemnitz stadtbahn', 'chemnitzer verkehrs - aktiengesellschaft', '14', '1993 - 2000', '-', 'standard', 'uni', '89', '123'], ['chemnitz stadtbahn', 'chemnitzer verkehrs - aktiengesellschaft', '10', '2000', '-', 'standard', 'bi', '74', '124'], ['city - bahn chemnitz', 'city - bahn chemnitz', '6', '2001', '-', 'standard', 'bi', '74', '124'], ['mannheim tramway', 'mvv verkehr', '6', '1996', '-', 'meter', 'bi', '90', '100'], ['mannheim tramway', 'mvv verkehr', '16', '2002 - 07', '-', 'meter', 'uni', '129', '130'], ['mannheim tramway', 'mvv verkehr', '20', '2002 - 07', '-', 'meter', 'bi', '80', '90'], ['duisberg stadtbahn', 'duisburger verkehrsgesellschaft', '1', '1996', '-', 'standard', 'bi', '38', '193'], ['inner west light rail', 'transport for new south wales', '7', '1997 - 98', '-', 'standard', 'bi', '74', '143'], ['helsinki tramway', 'helsinki city transport', '40', '1998 - 2004', '-', 'meter', 'uni', '55', '80'], ['heidelberg tramway', 'heidelberger straßen - und bergbahn', '8', '2002', '-', 'meter', 'bi', '100', '130'], ['ludwigshafen tramway', 'verkehrsbetriebe ludwigshafen am rhein', '8', '2003', '-', 'meter', 'uni', '88', '90'], ['bochum - gelsenkirchen tramway', 'bochum - gelsenkirchener straßenbahnen', '30', '2007 - 11', '-', 'meter', 'bi', '68', '120'], ['nuremberg tramway', 'verkehrs - aktiengesellschaft nürnberg', '8', '2007', '-', 'standard', 'uni', '87', '147'], ['munich tramway', 'münchner verkehrsgesellschaft', '14', '2008 - 11', '-', 'standard', 'uni', '87', '147'], ['bergen light rail', 'hordaland county municipality', '16', '2009 - 12', '-', 'standard', 'bi', '84', '128'], ['graz tramway', 'graz ag verkehrsbetriebe', '45', '2009 -', '-', 'standard', 'uni', '38', '113'], ['potsdam tramway', 'verkehrsbetrieb potsdam', '18', '2010 -', '-', 'standard', 'uni', '57', '118'], ['tramlink', 'transport for london', '6', '2012', '-', 'standard', 'bi', '72', '134']]

media in bismarck - mandan

https://en.wikipedia.org/wiki/Media_in_Bismarck-Mandan

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14623167-1.html.csv

aggregation

the average physical channel number for tv stations in bismarck , nd is around 22 .

{'scope': 'all', 'col': '2', 'type': 'average', 'result': '22', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'physical'], 'result': '22', 'ind': 0, 'tostr': 'avg { all_rows ; physical }'}, '22'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; physical } ; 22 } = true', 'tointer': 'the average of the physical record of all rows is 22 .'}

round_eq { avg { all_rows ; physical } ; 22 } = true

the average of the physical record of all rows is 22 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'physical_4': 4, '22_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'physical_4': 'physical', '22_5': '22'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'physical_4': [0], '22_5': [1]}

['virtual', 'physical', 'call sign', 'branding', 'network', 'owner']

[['3', '22', 'kbme - tv', 'prairie public', 'pbs', 'prairie public broadcasting'], ['5', '31', 'kfyr - tv', 'kfyr - tv nbc north dakota', 'nbc', 'hoak media corporation'], ['12', '12', 'kxmb - tv', 'kxmb cbs 12 kx television', 'cbs', 'reiten broadcasting'], ['17', '17', 'kbmy', 'kbmy 17', 'abc', 'forum communications'], ['26', '26', 'kndx', 'fox 26', 'fox', 'prime cities broadcasting']]

2008 - 09 atlanta hawks season

https://en.wikipedia.org/wiki/2008%E2%80%9309_Atlanta_Hawks_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17311759-9.html.csv

majority

all games of the atlanta hawks ' in the 2008 - 09 season were played in the month of april .

{'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'april', 'subset': None}

{'func': 'all_str_eq', 'args': ['all_rows', 'date', 'april'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to april .', 'tostr': 'all_eq { all_rows ; date ; april } = true'}

all_eq { all_rows ; date ; april } = true

for the date records of all rows , all of them fuzzily match to april .

1

1

{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, 'april_4': 4}

{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', 'april_4': 'april'}

{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], 'april_4': [0]}

['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']

[['76', 'april 3', 'boston', 'l 92 - 104 ( ot )', 'ronald murray ( 21 )', 'josh smith ( 10 )', 'mike bibby ( 6 )', 'td banknorth garden 18624', '43 - 33'], ['77', 'april 4', 'orlando', 'l 82 - 88 ( ot )', 'joe johnson ( 21 )', 'al horford ( 13 )', 'mike bibby ( 5 )', 'philips arena 19608', '43 - 34'], ['78', 'april 7', 'toronto', 'w 118 - 110 ( ot )', 'joe johnson , josh smith ( 25 )', 'al horford ( 12 )', 'mike bibby ( 10 )', 'air canada centre 17613', '44 - 34'], ['79', 'april 8', 'milwaukee', 'w 113 - 105 ( ot )', 'joe johnson ( 30 )', 'al horford ( 9 )', 'mike bibby ( 8 )', 'bradley center 13073', '45 - 34'], ['80', 'april 10', 'indiana', 'w 122 - 118 ( ot )', 'josh smith ( 30 )', 'al horford ( 15 )', 'mike bibby ( 9 )', 'philips arena 17222', '46 - 34'], ['81', 'april 14', 'miami', 'w 81 - 79 ( ot )', 'ronald murray ( 17 )', 'mario west ( 9 )', 'ronald murray ( 5 )', 'philips arena 18179', '47 - 34']]

1961 san francisco 49ers season

https://en.wikipedia.org/wiki/1961_San_Francisco_49ers_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16715979-2.html.csv

count

there were three games during this season where there were less than 40000 fans at the 49ers game .

{'scope': 'all', 'criterion': 'less_than', 'value': '40000', 'result': '3', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'attendance', '40000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose attendance record is less than 40000 .', 'tostr': 'filter_less { all_rows ; attendance ; 40000 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_less { all_rows ; attendance ; 40000 } }', 'tointer': 'select the rows whose attendance record is less than 40000 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_less { all_rows ; attendance ; 40000 } } ; 3 } = true', 'tointer': 'select the rows whose attendance record is less than 40000 . the number of such rows is 3 .'}

eq { count { filter_less { all_rows ; attendance ; 40000 } } ; 3 } = true

select the rows whose attendance record is less than 40000 . the number of such rows is 3 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_less_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '40000_6': 6, '3_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_less_0': 'filter_less', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', '40000_6': '40000', '3_7': '3'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_less_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '40000_6': [0], '3_7': [2]}

['week', 'date', 'opponent', 'result', 'attendance']

[['1', 'september 17 , 1961', 'washington redskins', 'w 35 - 3', '43412'], ['2', 'september 24 , 1961', 'green bay packers', 'l 30 - 10', '38624'], ['3', 'october 1 , 1961', 'detroit lions', 'w 49 - 0', '53155'], ['4', 'october 8 , 1961', 'los angeles rams', 'w 35 - 0', '59004'], ['5', 'october 15 , 1961', 'minnesota vikings', 'w 38 - 24', '34415'], ['6', 'october 22 , 1961', 'chicago bears', 'l 31 - 0', '49070'], ['7', 'october 29 , 1961', 'pittsburgh steelers', 'l 20 - 10', '19686'], ['8', 'november 5 , 1961', 'detroit lions', 't 20 - 20', '56878'], ['9', 'november 12 , 1961', 'los angeles rams', 'l 17 - 7', '63766'], ['10', 'november 19 , 1961', 'chicago bears', 'w 41 - 31', '52972'], ['11', 'november 26 , 1961', 'minnesota vikings', 'w 38 - 28', '48905'], ['12', 'december 3 , 1961', 'baltimore colts', 'l 20 - 17', '57641'], ['13', 'december 10 , 1961', 'green bay packers', 'w 22 - 21', '55722'], ['14', 'december 16 , 1961', 'baltimore colts', 'l 27 - 24', '45517']]

swimming at the 2000 summer olympics - men 's 200 metre butterfly

https://en.wikipedia.org/wiki/Swimming_at_the_2000_Summer_Olympics_%E2%80%93_Men%27s_200_metre_butterfly

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12446425-5.html.csv

count

the united states had two swimmers at the 2000 summer olympics - men 's 200 metre butterfly .

{'scope': 'all', 'criterion': 'equal', 'value': 'united states', 'result': '2', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to united states .', 'tostr': 'filter_eq { all_rows ; nationality ; united states }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; nationality ; united states } }', 'tointer': 'select the rows whose nationality record fuzzily matches to united states . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; nationality ; united states } } ; 2 } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to united states . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; nationality ; united states } } ; 2 } = true

select the rows whose nationality record fuzzily matches to united states . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'nationality_5': 5, 'united states_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'nationality_5': 'nationality', 'united states_6': 'united states', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'nationality_5': [0], 'united states_6': [0], '2_7': [2]}

['rank', 'lane', 'name', 'nationality', 'time']

[['1', '4', 'tom malchow', 'united states', '1:56.02'], ['2', '3', 'anatoly polyakov', 'russia', '1:56.78'], ['3', '5', 'michael phelps', 'united states', '1:57.00'], ['4', '6', 'franck esposito', 'france', '1:57.04'], ['5', '2', 'denis pankratov', 'russia', '1:57.24'], ['6', '8', 'andrew livingston', 'puerto rico', '1:58.63'], ['7', '1', 'stefan aartsen', 'netherlands', '1:58.66'], ['8', '7', 'thomas rupprath', 'germany', '1:58.96']]

2008 detroit shock season

https://en.wikipedia.org/wiki/2008_Detroit_Shock_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17103729-10.html.csv

ordinal

the detroit shock 's game against new york recorded their highest attendance of the 2008 season .

{'row': '5', 'col': '8', 'order': '1', 'col_other': '3', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'location / attendance', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; location / attendance ; 1 }'}, 'opponent'], 'result': 'new york', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; location / attendance ; 1 } ; opponent }'}, 'new york'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; location / attendance ; 1 } ; opponent } ; new york } = true', 'tointer': 'select the row whose location / attendance record of all rows is 1st maximum . the opponent record of this row is new york .'}

eq { hop { nth_argmax { all_rows ; location / attendance ; 1 } ; opponent } ; new york } = true

select the row whose location / attendance record of all rows is 1st maximum . the opponent record of this row is new york .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'location / attendance_5': 5, '1_6': 6, 'opponent_7': 7, 'new york_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'location / attendance_5': 'location / attendance', '1_6': '1', 'opponent_7': 'opponent', 'new york_8': 'new york'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'location / attendance_5': [0], '1_6': [0], 'opponent_7': [1], 'new york_8': [2]}

['game', 'date', 'opponent', 'score', 'high points', 'high rebounds', 'high assists', 'location / attendance', 'record']

[['30', 'september 5', 'indiana', '90 - 68', 'pierson ( 20 )', 'pierson ( 6 )', 'mcwilliams - franklin , pierson ( 4 )', 'palace of auburn hills 9287', '18 - 12'], ['31', 'september 6', 'washington', '84 - 69', 'mcwilliams - franklin ( 21 )', 'nolan ( 10 )', 'smith ( 8 )', 'verizon center 9976', '19 - 12'], ['32', 'september 9', 'phoenix', '89 - 78', 'nolan ( 18 )', 'braxton , hornbuckle , mcwilliams - franklin ( 8 )', 'pierson , smith ( 5 )', 'palace of auburn hills 7495', '20 - 12'], ['33', 'september 11', 'washington', '78 - 66', 'nolan ( 17 )', 'mcwilliams - franklin ( 8 )', 'smith ( 6 )', 'palace of auburn hills 8145', '21 - 12'], ['34', 'september 14', 'new york', '61 - 59', 'nolan , pierson ( 11 )', 'hornbuckle , nolan ( 7 )', 'powell ( 4 )', 'madison square garden 10042', '22 - 12']]

1962 u.s. open ( golf )

https://en.wikipedia.org/wiki/1962_U.S._Open_%28golf%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17277219-5.html.csv

aggregation

the total score of all players in the 1962 us open was 1419 .

{'scope': 'all', 'col': '4', 'type': 'sum', 'result': '1419', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'score'], 'result': '1419', 'ind': 0, 'tostr': 'sum { all_rows ; score }'}, '1419'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; score } ; 1419 } = true', 'tointer': 'the sum of the score record of all rows is 1419 .'}

round_eq { sum { all_rows ; score } ; 1419 } = true

the sum of the score record of all rows is 1419 .

2

2

{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'score_4': 4, '1419_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'score_4': 'score', '1419_5': '1419'}

{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'score_4': [0], '1419_5': [1]}

['place', 'player', 'country', 'score', 'to par']

[['t1', 'arnold palmer', 'united states', '71 + 68 = 139', '- 3'], ['t1', 'bob rosburg', 'united states', '70 + 69 = 139', '- 3'], ['3', 'billy maxwell', 'united states', '71 + 70 = 141', '- 1'], ['t4', 'bobby nichols', 'united states', '70 + 72 = 142', 'e'], ['t4', 'jack nicklaus', 'united states', '72 + 70 = 142', 'e'], ['t4', 'gary player', 'south africa', '71 + 71 = 142', 'e'], ['t7', 'miller barber', 'united states', '73 + 70 = 143', '+ 1'], ['t7', 'gene littler', 'united states', '69 + 74 = 143', '+ 1'], ['t9', 'phil rodgers', 'united states', '74 + 70 = 144', '+ 2'], ['t9', 'don whitt', 'united states', '73 + 71 = 144', '+ 2']]

united states house of representatives elections , 1954

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1954

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342013-10.html.csv

unique

the only candidate to have died in office under the democratic party was john james flynt .

{'scope': 'all', 'row': '4', 'col': '5', 'col_other': '6', 'criterion': 'fuzzily_match', 'value': 'died in office', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'died in office'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to died in office .', 'tostr': 'filter_eq { all_rows ; result ; died in office }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; died in office } }', 'tointer': 'select the rows whose result record fuzzily matches to died in office . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'died in office'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to died in office .', 'tostr': 'filter_eq { all_rows ; result ; died in office }'}, 'candidates'], 'result': 'john james flynt , jr ( d ) unopposed', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; died in office } ; candidates }'}, 'john james flynt , jr ( d ) unopposed'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; died in office } ; candidates } ; john james flynt , jr ( d ) unopposed }', 'tointer': 'the candidates record of this unqiue row is john james flynt , jr ( d ) unopposed .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; result ; died in office } } ; eq { hop { filter_eq { all_rows ; result ; died in office } ; candidates } ; john james flynt , jr ( d ) unopposed } } = true', 'tointer': 'select the rows whose result record fuzzily matches to died in office . there is only one such row in the table . the candidates record of this unqiue row is john james flynt , jr ( d ) unopposed .'}

and { only { filter_eq { all_rows ; result ; died in office } } ; eq { hop { filter_eq { all_rows ; result ; died in office } ; candidates } ; john james flynt , jr ( d ) unopposed } } = true

select the rows whose result record fuzzily matches to died in office . there is only one such row in the table . the candidates record of this unqiue row is john james flynt , jr ( d ) unopposed .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'result_7': 7, 'died in office_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'candidates_9': 9, 'john james flynt , jr (d) unopposed_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'result_7': 'result', 'died in office_8': 'died in office', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'candidates_9': 'candidates', 'john james flynt , jr (d) unopposed_10': 'john james flynt , jr ( d ) unopposed'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'result_7': [0], 'died in office_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'candidates_9': [2], 'john james flynt , jr (d) unopposed_10': [3]}

['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']

[['georgia 1', 'prince hulon preston , jr', 'democratic', '1946', 're - elected', 'prince hulon preston , jr ( d ) 83.7 % others 16.3 %'], ['georgia 2', 'j l pilcher', 'democratic', '1953', 're - elected', 'j l pilcher ( d ) unopposed'], ['georgia 3', 'tic forrester', 'democratic', '1950', 're - elected', 'tic forrester ( d ) unopposed'], ['georgia 4', 'albert sidney camp', 'democratic', '1939', 'died in office democratic hold', 'john james flynt , jr ( d ) unopposed'], ['georgia 6', 'carl vinson', 'democratic', '1914', 're - elected', 'carl vinson ( d ) unopposed'], ['georgia 7', 'henderson lovelace lanham', 'democratic', '1946', 're - elected', 'henderson lovelace lanham ( d ) unopposed'], ['georgia 8', 'william m wheeler', 'democratic', '1946', 'lost renomination democratic hold', 'iris faircloth blitch ( d ) unopposed'], ['georgia 9', 'phillip m landrum', 'democratic', '1952', 're - elected', 'phillip m landrum ( d ) unopposed']]

34th united states congress

https://en.wikipedia.org/wiki/34th_United_States_Congress

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2417308-3.html.csv

comparative

john parker hale was installed before william bigler was installed .

{'row_1': '1', 'row_2': '3', 'col': '5', 'col_other': '4', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'successor', 'john parker hale ( r )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose successor record fuzzily matches to john parker hale ( r ) .', 'tostr': 'filter_eq { all_rows ; successor ; john parker hale ( r ) }'}, 'date of successors formal installation'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; successor ; john parker hale ( r ) } ; date of successors formal installation }', 'tointer': 'select the rows whose successor record fuzzily matches to john parker hale ( r ) . take the date of successors formal installation record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'successor', 'william bigler ( d )'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose successor record fuzzily matches to william bigler ( d ) .', 'tostr': 'filter_eq { all_rows ; successor ; william bigler ( d ) }'}, 'date of successors formal installation'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; successor ; william bigler ( d ) } ; date of successors formal installation }', 'tointer': 'select the rows whose successor record fuzzily matches to william bigler ( d ) . take the date of successors formal installation record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; successor ; john parker hale ( r ) } ; date of successors formal installation } ; hop { filter_eq { all_rows ; successor ; william bigler ( d ) } ; date of successors formal installation } } = true', 'tointer': 'select the rows whose successor record fuzzily matches to john parker hale ( r ) . take the date of successors formal installation record of this row . select the rows whose successor record fuzzily matches to william bigler ( d ) . take the date of successors formal installation record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; successor ; john parker hale ( r ) } ; date of successors formal installation } ; hop { filter_eq { all_rows ; successor ; william bigler ( d ) } ; date of successors formal installation } } = true

select the rows whose successor record fuzzily matches to john parker hale ( r ) . take the date of successors formal installation record of this row . select the rows whose successor record fuzzily matches to william bigler ( d ) . take the date of successors formal installation record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'successor_7': 7, 'john parker hale (r)_8': 8, 'date of successors formal installation_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'successor_11': 11, 'william bigler (d)_12': 12, 'date of successors formal installation_13': 13}

{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'successor_7': 'successor', 'john parker hale (r)_8': 'john parker hale ( r )', 'date of successors formal installation_9': 'date of successors formal installation', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'successor_11': 'successor', 'william bigler (d)_12': 'william bigler ( d )', 'date of successors formal installation_13': 'date of successors formal installation'}

{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'successor_7': [0], 'john parker hale (r)_8': [0], 'date of successors formal installation_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'successor_11': [1], 'william bigler (d)_12': [1], 'date of successors formal installation_13': [3]}

['state ( class )', 'vacator', 'reason for change', 'successor', 'date of successors formal installation']

[['new hampshire ( 2 )', 'vacant', 'legislature failed to elect on time', 'john parker hale ( r )', 'july 30 , 1855'], ['alabama ( 3 )', 'vacant', 'legislature failed to elect on time', 'benjamin fitzpatrick ( d )', 'november 26 , 1855'], ['pennsylvania ( 3 )', 'vacant', 'legislature failed to elect on time', 'william bigler ( d )', 'january 14 , 1856'], ['california ( 3 )', 'vacant', 'legislature failed to elect on time', 'william m gwin ( d )', 'january 13 , 1857'], ['indiana ( 3 )', 'vacant', 'legislature failed to elect on time', 'graham n fitch ( d )', 'february 4 , 1857']]

bharatiya janata party

https://en.wikipedia.org/wiki/Bharatiya_Janata_Party

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-149330-1.html.csv

unique

the 9th lok sabha is the only general election where 85 seats were won by the bharatiya janata party .

{'scope': 'all', 'row': '3', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': '85', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'seats won', '85'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose seats won record is equal to 85 .', 'tostr': 'filter_eq { all_rows ; seats won ; 85 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; seats won ; 85 } }', 'tointer': 'select the rows whose seats won record is equal to 85 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'seats won', '85'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose seats won record is equal to 85 .', 'tostr': 'filter_eq { all_rows ; seats won ; 85 }'}, 'general election'], 'result': '9th lok sabha', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; seats won ; 85 } ; general election }'}, '9th lok sabha'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; seats won ; 85 } ; general election } ; 9th lok sabha }', 'tointer': 'the general election record of this unqiue row is 9th lok sabha .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; seats won ; 85 } } ; eq { hop { filter_eq { all_rows ; seats won ; 85 } ; general election } ; 9th lok sabha } } = true', 'tointer': 'select the rows whose seats won record is equal to 85 . there is only one such row in the table . the general election record of this unqiue row is 9th lok sabha .'}

and { only { filter_eq { all_rows ; seats won ; 85 } } ; eq { hop { filter_eq { all_rows ; seats won ; 85 } ; general election } ; 9th lok sabha } } = true

select the rows whose seats won record is equal to 85 . there is only one such row in the table . the general election record of this unqiue row is 9th lok sabha .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'seats won_7': 7, '85_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'general election_9': 9, '9th lok sabha_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'seats won_7': 'seats won', '85_8': '85', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'general election_9': 'general election', '9th lok sabha_10': '9th lok sabha'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'seats won_7': [0], '85_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'general election_9': [2], '9th lok sabha_10': [3]}

['year', 'general election', 'seats won', 'change in seat', '% of votes', 'votes swing']

[['indian general election , 1980', '7th lok sabha', '12', '12', '8.75 %', '8.75'], ['indian general election , 1984', '8th lok sabha', '2', '10', '7.74 %', '1.01'], ['indian general election , 1989', '9th lok sabha', '85', '83', '11.36', '3.62'], ['indian general election , 1991', '10th lok sabha', '120', '37', '20.11', '8.75'], ['indian general election , 1996', '11th lok sabha', '161', '41', '20.29', '0.18'], ['indian general election , 1998', '12th lok sabha', '183', '21', '25.59 %', '5.30'], ['indian general election , 1999', '13th lok sabha', '189', '6', '23.75', '1.84'], ['indian general election , 2004', '14th lok sabha', '144', '45', '22.16 %', '1.69']]

list of benedictine colleges and universities

https://en.wikipedia.org/wiki/List_of_Benedictine_colleges_and_universities

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14014822-1.html.csv

unique

benedictine university is the only benedictine university in illinois to be founded in 1887 .

{'scope': 'subset', 'row': '3', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': '1887', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'illinois'}}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'state', 'illinois'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; state ; illinois }', 'tointer': 'select the rows whose state record fuzzily matches to illinois .'}, 'founded', '1887'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose state record fuzzily matches to illinois . among these rows , select the rows whose founded record is equal to 1887 .', 'tostr': 'filter_eq { filter_eq { all_rows ; state ; illinois } ; founded ; 1887 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; state ; illinois } ; founded ; 1887 } }', 'tointer': 'select the rows whose state record fuzzily matches to illinois . among these rows , select the rows whose founded record is equal to 1887 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'state', 'illinois'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; state ; illinois }', 'tointer': 'select the rows whose state record fuzzily matches to illinois .'}, 'founded', '1887'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose state record fuzzily matches to illinois . among these rows , select the rows whose founded record is equal to 1887 .', 'tostr': 'filter_eq { filter_eq { all_rows ; state ; illinois } ; founded ; 1887 }'}, 'school'], 'result': 'benedictine university', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; state ; illinois } ; founded ; 1887 } ; school }'}, 'benedictine university'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; state ; illinois } ; founded ; 1887 } ; school } ; benedictine university }', 'tointer': 'the school record of this unqiue row is benedictine university .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; state ; illinois } ; founded ; 1887 } } ; eq { hop { filter_eq { filter_eq { all_rows ; state ; illinois } ; founded ; 1887 } ; school } ; benedictine university } } = true', 'tointer': 'select the rows whose state record fuzzily matches to illinois . among these rows , select the rows whose founded record is equal to 1887 . there is only one such row in the table . the school record of this unqiue row is benedictine university .'}

and { only { filter_eq { filter_eq { all_rows ; state ; illinois } ; founded ; 1887 } } ; eq { hop { filter_eq { filter_eq { all_rows ; state ; illinois } ; founded ; 1887 } ; school } ; benedictine university } } = true

select the rows whose state record fuzzily matches to illinois . among these rows , select the rows whose founded record is equal to 1887 . there is only one such row in the table . the school record of this unqiue row is benedictine university .

8

6

{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'state_8': 8, 'illinois_9': 9, 'founded_10': 10, '1887_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'school_12': 12, 'benedictine university_13': 13}

{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_eq_1': 'filter_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'state_8': 'state', 'illinois_9': 'illinois', 'founded_10': 'founded', '1887_11': '1887', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'school_12': 'school', 'benedictine university_13': 'benedictine university'}

{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'state_8': [0], 'illinois_9': [0], 'founded_10': [1], '1887_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'school_12': [3], 'benedictine university_13': [4]}

['school', 'city', 'state', 'enrollment', 'founded']

[['belmont abbey college', 'belmont', 'north carolina', '1320', '1876'], ['benedictine college', 'atchison', 'kansas', '1855', '1858'], ['benedictine university', 'lisle', 'illinois', '6857', '1887'], ['benedictine university at springfield', 'springfield', 'illinois', '981', '1929'], ['college of saint benedict', 'st joseph', 'minnesota', '2042', '1913'], ['college of saint scholastica', 'duluth', 'minnesota', '3309', '1912'], ['conception seminary college', 'conception', 'missouri', '108', '1886'], ['mount marty college', 'yankton', 'south dakota', '1100', '1936'], ['saint anselm college', 'goffstown', 'new hampshire', '2000', '1889'], ["saint gregory 's university", 'shawnee', 'oklahoma', '800', '1875'], ["saint john 's university", 'collegeville', 'minnesota', '1886', '1857'], ['saint joseph seminary college', 'covington', 'louisiana', '171', '1889'], ['saint leo university', 'saint leo', 'florida', '1628', '1889'], ["saint martin 's university", 'lacey', 'washington', '1650', '1895'], ['saint vincent college', 'latrobe', 'pennsylvania', '1848', '1846'], ['thomas more college ( kentucky )', 'crestview hills', 'kentucky', '1500', '1921'], ['university of mary', 'bismarck', 'north dakota', '2900', '1959'], ['colegio san carlos', 'bogotã ¡', 'colombia', '1400', '1960']]