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1945 vfl season | https://en.wikipedia.org/wiki/1945_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809271-7.html.csv | aggregation | for the 1945 vfl season the total crowd was 84000 . | {'scope': 'all', 'col': '6', 'type': 'sum', 'result': '84000', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'crowd'], 'result': '84000', 'ind': 0, 'tostr': 'sum { all_rows ; crowd }'}, '84000'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; crowd } ; 84000 } = true', 'tointer': 'the sum of the crowd record of all rows is 84000 .'} | round_eq { sum { all_rows ; crowd } ; 84000 } = true | the sum of the crowd record of all rows is 84000 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '84000_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '84000_5': '84000'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '84000_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['melbourne', '10.12 ( 72 )', 'north melbourne', '18.11 ( 119 )', 'punt road oval', '14000', '2 june 1945'], ['fitzroy', '7.23 ( 65 )', 'south melbourne', '10.15 ( 75 )', 'brunswick street oval', '19000', '2 june 1945'], ['essendon', '14.28 ( 112 )', 'geelong', '9.10 ( 64 )', 'windy hill', '8000', '2 june 1945'], ['carlton', '12.17 ( 89 )', 'richmond', '13.16 ( 94 )', 'princes park', '21000', '2 june 1945'], ['hawthorn', '11.15 ( 81 )', 'footscray', '17.14 ( 116 )', 'glenferrie oval', '10000', '2 june 1945'], ['st kilda', '7.12 ( 54 )', 'collingwood', '19.16 ( 130 )', 'junction oval', '12000', '2 june 1945']] |
fiba europe under - 16 championship | https://en.wikipedia.org/wiki/FIBA_Europe_Under-16_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17837875-2.html.csv | aggregation | a total of 27 silver medals were won by countries in the fiba europe under - 16 championship . | {'scope': 'all', 'col': '3', 'type': 'sum', 'result': '27', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'silver'], 'result': '27', 'ind': 0, 'tostr': 'sum { all_rows ; silver }'}, '27'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; silver } ; 27 } = true', 'tointer': 'the sum of the silver record of all rows is 27 .'} | round_eq { sum { all_rows ; silver } ; 27 } = true | the sum of the silver record of all rows is 27 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'silver_4': 4, '27_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'silver_4': 'silver', '27_5': '27'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'silver_4': [0], '27_5': [1]} | ['rank', 'gold', 'silver', 'bronze', 'total'] | [['1', '5', '3', '3', '11'], ['2', '3', '6', '5', '14'], ['3', '3', '4', '5', '12'], ['4', '3', '1', '4', '8'], ['5', '3', '0', '0', '3'], ['6', '2', '3', '2', '7'], ['7', '1', '4', '2', '7'], ['8', '1', '2', '1', '4'], ['9', '1', '2', '0', '3'], ['10', '0', '2', '1', '3'], ['11', '0', '0', '2', '2'], ['12', '0', '0', '1', '1']] |
2008 kentucky wildcats football team | https://en.wikipedia.org/wiki/2008_Kentucky_Wildcats_football_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14624447-26.html.csv | aggregation | for the middle tennessee game in the 2008 kentucky wildcats season , the average weight of the starters was 250.5 pounds . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '250.5', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'weight'], 'result': '250.5', 'ind': 0, 'tostr': 'avg { all_rows ; weight }'}, '250.5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; weight } ; 250.5 } = true', 'tointer': 'the average of the weight record of all rows is 250.5 .'} | round_eq { avg { all_rows ; weight } ; 250.5 } = true | the average of the weight record of all rows is 250.5 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'weight_4': 4, '250.5_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'weight_4': 'weight', '250.5_5': '250.5'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'weight_4': [0], '250.5_5': [1]} | ['position', 'number', 'name', 'height', 'weight', 'class', 'hometown', 'games ↑'] | [['qb', '5', 'mike hartline', "6 ' 6", '205', 'rs - so', 'canton , ohio', '3'], ['tb', '28', 'tony dixon', "5 ' 9", '203', 'sr', 'parrish , alabama', '3'], ['fb', '38', 'john conner', "5 ' 11", '230', 'jr', 'west chester , ohio', '3'], ['wr', '12', 'dicky lyons', "5 ' 11", '190', 'sr', 'new orleans , louisiana', '3'], ['wr', '81', 'kyrus lanxter', "6 ' 3", '193', 'so', 'alcoa , tennessee', '1'], ['te', '80', 'tc drake', "6 ' 6", '242', 'jr', 'bardstown , kentucky', '1'], ['lt', '52', 'billy joe murphy', "6 ' 6", '292', 'rs - fr', 'gamaliel , kentucky', '2'], ['lg', '72', 'zipp duncan', "6 ' 5", '295', 'jr', 'magnolia , kentucky', '3'], ['c', '61', 'jorge gonzález', "6 ' 3", '303', 'jr', 'tampa bay , florida', '3'], ['rg', '73', 'jess beets', "6 ' 2", '293', 'sr', 'dove canyon , california', '3'], ['rt', '76', 'justin jeffries', "6 ' 6", '310', 'jr', 'louisville , kentucky', '3']] |
2010 - 11 philadelphia flyers season | https://en.wikipedia.org/wiki/2010%E2%80%9311_Philadelphia_Flyers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27539808-3.html.csv | count | in the 2010 - 11 philadelphia flyers season , among the games with pittsburgh penguins , 2 of them were played in consol energy center . | {'scope': 'subset', 'criterion': 'fuzzily_match', 'value': 'consol energy center', 'result': '2', 'col': '5', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'pittsburgh penguins'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'pittsburgh penguins'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; opponent ; pittsburgh penguins }', 'tointer': 'select the rows whose opponent record fuzzily matches to pittsburgh penguins .'}, 'location / attendance', 'consol energy center'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to pittsburgh penguins . among these rows , select the rows whose location / attendance record fuzzily matches to consol energy center .', 'tostr': 'filter_eq { filter_eq { all_rows ; opponent ; pittsburgh penguins } ; location / attendance ; consol energy center }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; opponent ; pittsburgh penguins } ; location / attendance ; consol energy center } }', 'tointer': 'select the rows whose opponent record fuzzily matches to pittsburgh penguins . among these rows , select the rows whose location / attendance record fuzzily matches to consol energy center . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; opponent ; pittsburgh penguins } ; location / attendance ; consol energy center } } ; 2 } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to pittsburgh penguins . among these rows , select the rows whose location / attendance record fuzzily matches to consol energy center . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; opponent ; pittsburgh penguins } ; location / attendance ; consol energy center } } ; 2 } = true | select the rows whose opponent record fuzzily matches to pittsburgh penguins . among these rows , select the rows whose location / attendance record fuzzily matches to consol energy center . 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, 'opponent_6': 6, 'pittsburgh penguins_7': 7, 'location / attendance_8': 8, 'consol energy center_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', 'opponent_6': 'opponent', 'pittsburgh penguins_7': 'pittsburgh penguins', 'location / attendance_8': 'location / attendance', 'consol energy center_9': 'consol energy center', '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], 'opponent_6': [0], 'pittsburgh penguins_7': [0], 'location / attendance_8': [1], 'consol energy center_9': [1], '2_10': [3]} | ['game', 'october', 'opponent', 'score', 'location / attendance', 'record', 'points'] | [['1', '7', 'pittsburgh penguins', '3 - 2', 'consol energy center ( 18289 )', '1 - 0 - 0', '2'], ['2', '9', 'st louis blues', '1 - 2 ( ot )', 'scottrade center ( 19150 )', '1 - 0 - 1', '3'], ['3', '11', 'colorado avalanche', '4 - 2', 'wells fargo center ( 19652 )', '2 - 0 - 1', '5'], ['4', '14', 'tampa bay lightning', '2 - 3', 'wells fargo center ( 19592 )', '2 - 1 - 1', '5'], ['5', '16', 'pittsburgh penguins', '1 - 5', 'wells fargo center ( 19684 )', '2 - 2 - 1', '5'], ['6', '21', 'anaheim ducks', '2 - 3', 'wells fargo center ( 19012 )', '2 - 3 - 1', '5'], ['7', '23', 'toronto maple leafs', '5 - 2', 'wells fargo center ( 19382 )', '3 - 3 - 1', '7'], ['8', '25', 'columbus blue jackets', '1 - 2', 'nationwide arena ( 11727 )', '3 - 4 - 1', '7'], ['9', '26', 'buffalo sabres', '6 - 3', 'wells fargo center ( 19361 )', '4 - 4 - 1', '9'], ['10', '29', 'pittsburgh penguins', '3 - 2', 'consol energy center ( 18275 )', '5 - 4 - 1', '11']] |
gotōji line | https://en.wikipedia.org/wiki/Got%C5%8Dji_Line | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11482596-1.html.csv | aggregation | japanese railway stations are an average of 7.1 km away from the gotōji line . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '7.1', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'distance ( km )'], 'result': '7.1', 'ind': 0, 'tostr': 'avg { all_rows ; distance ( km ) }'}, '7.1'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; distance ( km ) } ; 7.1 } = true', 'tointer': 'the average of the distance ( km ) record of all rows is 7.1 .'} | round_eq { avg { all_rows ; distance ( km ) } ; 7.1 } = true | the average of the distance ( km ) record of all rows is 7.1 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'distance (km)_4': 4, '7.1_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'distance (km)_4': 'distance ( km )', '7.1_5': '7.1'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'distance (km)_4': [0], '7.1_5': [1]} | ['station', 'japanese', 'distance ( km )', 'rapid', 'location'] | [['tagawa - gotōji', '田川後藤寺', '0.0', '●', 'tagawa'], ['funao', '船尾', '3.4', '↑', 'tagawa'], ['chikuzen - shōnai', '筑前庄内', '7.1', '↑', 'iizuka'], ['shimo - kamoo', '下鴨生', '8.3', '↑', 'kama'], ['kami - mio', '上三緒', '10.2', '↑', 'iizuka'], ['shin - iizuka', '新飯塚', '13.3', '●', 'iizuka']] |
2010 - 13 ncaa conference realignment | https://en.wikipedia.org/wiki/2010%E2%80%9313_NCAA_conference_realignment | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27671835-3.html.csv | superlative | the hockey east ( men ) conerence had the highest new membership total . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '5', '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', 'new membership total'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; new membership total }'}, 'conference'], 'result': 'hockey east ( men )', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; new membership total } ; conference }'}, 'hockey east ( men )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; new membership total } ; conference } ; hockey east ( men ) } = true', 'tointer': 'select the row whose new membership total record of all rows is maximum . the conference record of this row is hockey east ( men ) .'} | eq { hop { argmax { all_rows ; new membership total } ; conference } ; hockey east ( men ) } = true | select the row whose new membership total record of all rows is maximum . the conference record of this row is hockey east ( men ) . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'new membership total_5': 5, 'conference_6': 6, 'hockey east (men)_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'new membership total_5': 'new membership total', 'conference_6': 'conference', 'hockey east (men)_7': 'hockey east ( men )'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'new membership total_5': [0], 'conference_6': [1], 'hockey east (men)_7': [2]} | ['conference', 'old membership total', 'new membership total', 'net change', 'members added', 'members lost'] | [['atlantic hockey ( men only )', '12', '11', '1', '0', '1'], ['big ten ( men only )', '0', '6', '6', '6', '0'], ['ccha ( men only )', '11', '0', '11', '0', '11'], ['cha ( women only )', '4', '6', '2', '3', '1'], ['hockey east ( men )', '10', '12', '2', '2', '0'], ['nchc ( men only )', '0', '8', '8', '8', '0']] |
the firebird | https://en.wikipedia.org/wiki/The_Firebird | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1060482-1.html.csv | unique | the only time the firebird has been released as a digital download was by deutsche grammophon . | {'scope': 'all', 'row': '14', 'col': '5', 'col_other': '3', 'criterion': 'equal', 'value': 'digital download', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'format', 'digital download'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose format record fuzzily matches to digital download .', 'tostr': 'filter_eq { all_rows ; format ; digital download }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; format ; digital download } }', 'tointer': 'select the rows whose format record fuzzily matches to digital download . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'format', 'digital download'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose format record fuzzily matches to digital download .', 'tostr': 'filter_eq { all_rows ; format ; digital download }'}, 'record company'], 'result': 'deutsche grammophon', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; format ; digital download } ; record company }'}, 'deutsche grammophon'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; format ; digital download } ; record company } ; deutsche grammophon }', 'tointer': 'the record company record of this unqiue row is deutsche grammophon .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; format ; digital download } } ; eq { hop { filter_eq { all_rows ; format ; digital download } ; record company } ; deutsche grammophon } } = true', 'tointer': 'select the rows whose format record fuzzily matches to digital download . there is only one such row in the table . the record company record of this unqiue row is deutsche grammophon .'} | and { only { filter_eq { all_rows ; format ; digital download } } ; eq { hop { filter_eq { all_rows ; format ; digital download } ; record company } ; deutsche grammophon } } = true | select the rows whose format record fuzzily matches to digital download . there is only one such row in the table . the record company record of this unqiue row is deutsche grammophon . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'format_7': 7, 'digital download_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'record company_9': 9, 'deutsche grammophon_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'format_7': 'format', 'digital download_8': 'digital download', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'record company_9': 'record company', 'deutsche grammophon_10': 'deutsche grammophon'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'format_7': [0], 'digital download_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'record company_9': [2], 'deutsche grammophon_10': [3]} | ['orchestra', 'conductor', 'record company', 'year of recording', 'format'] | [['london symphony orchestra', 'antal doráti', 'mercury records', '1959', 'cd'], ['columbia symphony orchestra', 'igor stravinsky', 'columbia masterworks', '1961', 'cd / lp'], ['royal concertgebouw orchestra', 'colin davis', 'philips', '1978', 'cd'], ['royal danish orchestra', 'paul jorgensen', 'kultur', '1982', 'dvd'], ['detroit symphony orchestra', 'antal doráti', 'decca records', '1982', 'cd'], ['montreal symphony orchestra', 'charles dutoit', 'decca records', '1984', 'cd'], ['seattle symphony orchestra', 'gerard schwarz', 'delos records', '1992', 'cd'], ['chicago symphony orchestra', 'pierre boulez', 'deutsche grammophon', '1993', 'cd'], ['kirov orchestra', 'valeri gergiev', 'philips classics records', '1998', 'cd'], ['philharmonia orchestra', 'robert craft', 'koch records / naxos records', '1996', 'cd'], ['orchestre de paris', 'seiji ozawa', 'emi', '1997', 'cd'], ['san francisco symphony orchestra', 'michael tilson thomas', 'rca', '1998', 'cd'], ['city of birmingham symphony orchestra', 'simon rattle', 'emi', '2008', 'cd'], ['los angeles philharmonic orchestra', 'esa - pekka salonen', 'deutsche grammophon', '2008', 'digital download']] |
television in italy | https://en.wikipedia.org/wiki/Television_in_Italy | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15887683-16.html.csv | unique | only diprè tv of the tv stations of italy has the content type of arte . | {'scope': 'all', 'row': '5', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': 'arte', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'content', 'arte'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose content record fuzzily matches to arte .', 'tostr': 'filter_eq { all_rows ; content ; arte }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; content ; arte } }', 'tointer': 'select the rows whose content record fuzzily matches to arte . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'content', 'arte'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose content record fuzzily matches to arte .', 'tostr': 'filter_eq { all_rows ; content ; arte }'}, 'television service'], 'result': 'diprè tv', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; content ; arte } ; television service }'}, 'diprè tv'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; content ; arte } ; television service } ; diprè tv }', 'tointer': 'the television service record of this unqiue row is diprè tv .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; content ; arte } } ; eq { hop { filter_eq { all_rows ; content ; arte } ; television service } ; diprè tv } } = true', 'tointer': 'select the rows whose content record fuzzily matches to arte . there is only one such row in the table . the television service record of this unqiue row is diprè tv .'} | and { only { filter_eq { all_rows ; content ; arte } } ; eq { hop { filter_eq { all_rows ; content ; arte } ; television service } ; diprè tv } } = true | select the rows whose content record fuzzily matches to arte . there is only one such row in the table . the television service record of this unqiue row is diprè tv . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'content_7': 7, 'arte_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'television service_9': 9, 'diprè tv_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'content_7': 'content', 'arte_8': 'arte', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'television service_9': 'television service', 'diprè tv_10': 'diprè tv'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'content_7': [0], 'arte_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'television service_9': [2], 'diprè tv_10': [3]} | ['n degree', 'television service', 'country', 'language', 'content', 'dar', 'hdtv', 'package / option'] | [['861', 'telemarket', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['862', 'noello sat', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['863', 'elite shopping tv', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['864', 'juwelo', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['865', 'diprè tv', 'italy', 'italian', 'arte', '4:3', 'no', 'no ( fta )'], ['866', 'telemarket for you', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['867', 'la sorgente sat 1', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['868', 'la sorgente sat 2', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )'], ['869', 'la sorgente sat 3', 'italy', 'italian', 'televendita', '4:3', 'no', 'no ( fta )']] |
venezuela at the olympics | https://en.wikipedia.org/wiki/Venezuela_at_the_Olympics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14778294-1.html.csv | count | for venezuela at the olympics , when the sport is boxing , there were two times when the event was men 's light flyweight . | {'scope': 'subset', 'criterion': 'equal', 'value': "men 's light flyweight", 'result': '2', 'col': '5', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'boxing'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'sport', 'boxing'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; sport ; boxing }', 'tointer': 'select the rows whose sport record fuzzily matches to boxing .'}, 'event', "men 's light flyweight"], 'result': None, 'ind': 1, 'tointer': "select the rows whose sport record fuzzily matches to boxing . among these rows , select the rows whose event record fuzzily matches to men 's light flyweight .", 'tostr': "filter_eq { filter_eq { all_rows ; sport ; boxing } ; event ; men 's light flyweight }"}], 'result': '2', 'ind': 2, 'tostr': "count { filter_eq { filter_eq { all_rows ; sport ; boxing } ; event ; men 's light flyweight } }", 'tointer': "select the rows whose sport record fuzzily matches to boxing . among these rows , select the rows whose event record fuzzily matches to men 's light flyweight . the number of such rows is 2 ."}, '2'], 'result': True, 'ind': 3, 'tostr': "eq { count { filter_eq { filter_eq { all_rows ; sport ; boxing } ; event ; men 's light flyweight } } ; 2 } = true", 'tointer': "select the rows whose sport record fuzzily matches to boxing . among these rows , select the rows whose event record fuzzily matches to men 's light flyweight . the number of such rows is 2 ."} | eq { count { filter_eq { filter_eq { all_rows ; sport ; boxing } ; event ; men 's light flyweight } } ; 2 } = true | select the rows whose sport record fuzzily matches to boxing . among these rows , select the rows whose event record fuzzily matches to men 's light flyweight . 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, 'sport_6': 6, 'boxing_7': 7, 'event_8': 8, "men 's light flyweight_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', 'sport_6': 'sport', 'boxing_7': 'boxing', 'event_8': 'event', "men 's light flyweight_9": "men 's light flyweight", '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], 'sport_6': [0], 'boxing_7': [0], 'event_8': [1], "men 's light flyweight_9": [1], '2_10': [3]} | ['medal', 'name', 'games', 'sport', 'event'] | [['bronze', 'arnoldo devonish', '1952 helsinki', 'athletics', "men 's triple jump"], ['bronze', 'enrico forcella', '1960 rome', 'shooting', "men 's 50 metre rifle prone"], ['gold', 'francisco rodriguez', '1968 mexico city', 'boxing', "men 's light flyweight"], ['silver', 'pedro gamarro', '1976 montreal', 'boxing', "men 's welterweight"], ['silver', 'bernardo piñango', '1980 moscow', 'boxing', "men 's bantamweight"], ['bronze', 'marcelino bolivar', '1984 los angeles', 'boxing', "men 's light flyweight"], ['bronze', 'omar catari', '1984 los angeles', 'boxing', "men 's featherweight"], ['bronze', 'rafael vidal', '1984 los angeles', 'swimming', "men 's 200 m butterfly"], ['bronze', 'adriana carmona', '2004 athens', 'taekwondo', 'women + 67 kg'], ['bronze', 'israel jose rubio', '2004 athens', 'weightlifting', "men 's featherweight"], ['bronze', 'dalia contreras', '2008 beijing', 'taekwondo', 'women 49 kg'], ['gold', 'rubén limardo', '2012 london', 'fencing', "men 's épée"]] |
list of fc barcelona records and statistics | https://en.wikipedia.org/wiki/List_of_FC_Barcelona_records_and_statistics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14707564-2.html.csv | comparative | víctor valdés has played more games for fc barcelona than joan segarra did . | {'row_1': '5', 'row_2': '8', 'col': '4', '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', 'name', 'víctor valdés'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to víctor valdés .', 'tostr': 'filter_eq { all_rows ; name ; víctor valdés }'}, 'games'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; víctor valdés } ; games }', 'tointer': 'select the rows whose name record fuzzily matches to víctor valdés . take the games record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'joan segarra'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to joan segarra .', 'tostr': 'filter_eq { all_rows ; name ; joan segarra }'}, 'games'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; joan segarra } ; games }', 'tointer': 'select the rows whose name record fuzzily matches to joan segarra . take the games record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; name ; víctor valdés } ; games } ; hop { filter_eq { all_rows ; name ; joan segarra } ; games } } = true', 'tointer': 'select the rows whose name record fuzzily matches to víctor valdés . take the games record of this row . select the rows whose name record fuzzily matches to joan segarra . take the games record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; name ; víctor valdés } ; games } ; hop { filter_eq { all_rows ; name ; joan segarra } ; games } } = true | select the rows whose name record fuzzily matches to víctor valdés . take the games record of this row . select the rows whose name record fuzzily matches to joan segarra . take the games 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, 'name_7': 7, 'víctor valdés_8': 8, 'games_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'joan segarra_12': 12, 'games_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', 'name_7': 'name', 'víctor valdés_8': 'víctor valdés', 'games_9': 'games', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'joan segarra_12': 'joan segarra', 'games_13': 'games'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'víctor valdés_8': [0], 'games_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'joan segarra_12': [1], 'games_13': [3]} | ['ranking', 'nationality', 'name', 'games', 'years'] | [['1', 'spain', 'xavi', '833', '1997 -'], ['2', 'spain', 'carles puyol', '724', '1996 -'], ['3', 'spain', 'migueli', '664', '1973 - 1989'], ['4', 'spain', 'carles rexach', '656', '1965 - 1981'], ['5', 'spain', 'víctor valdés', '639', '2000 -'], ['6', 'spain', 'guillermo amor', '550', '1988 - 1998'], ['7', 'spain', 'joaquim rifé', '535', '1964 - 1976'], ['8', 'spain', 'joan segarra', '528', '1949 - 1964']] |
equestrian at the 1980 summer olympics | https://en.wikipedia.org/wiki/Equestrian_at_the_1980_Summer_Olympics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1461487-1.html.csv | unique | for equestrian at the 1980 summer olympics , of the countries that won gold medals , the only one with 2 bronze medals is the soviet union . | {'scope': 'subset', 'row': '1', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': '2', 'subset': {'col': '3', 'criterion': 'greater_than', 'value': '0'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'gold', '0'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; gold ; 0 }', 'tointer': 'select the rows whose gold record is greater than 0 .'}, 'bronze', '2'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose gold record is greater than 0 . among these rows , select the rows whose bronze record is equal to 2 .', 'tostr': 'filter_eq { filter_greater { all_rows ; gold ; 0 } ; bronze ; 2 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_greater { all_rows ; gold ; 0 } ; bronze ; 2 } }', 'tointer': 'select the rows whose gold record is greater than 0 . among these rows , select the rows whose bronze record is equal to 2 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'gold', '0'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; gold ; 0 }', 'tointer': 'select the rows whose gold record is greater than 0 .'}, 'bronze', '2'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose gold record is greater than 0 . among these rows , select the rows whose bronze record is equal to 2 .', 'tostr': 'filter_eq { filter_greater { all_rows ; gold ; 0 } ; bronze ; 2 }'}, 'nation'], 'result': 'soviet union ( urs )', 'ind': 3, 'tostr': 'hop { filter_eq { filter_greater { all_rows ; gold ; 0 } ; bronze ; 2 } ; nation }'}, 'soviet union ( urs )'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_greater { all_rows ; gold ; 0 } ; bronze ; 2 } ; nation } ; soviet union ( urs ) }', 'tointer': 'the nation record of this unqiue row is soviet union ( urs ) .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_greater { all_rows ; gold ; 0 } ; bronze ; 2 } } ; eq { hop { filter_eq { filter_greater { all_rows ; gold ; 0 } ; bronze ; 2 } ; nation } ; soviet union ( urs ) } } = true', 'tointer': 'select the rows whose gold record is greater than 0 . among these rows , select the rows whose bronze record is equal to 2 . there is only one such row in the table . the nation record of this unqiue row is soviet union ( urs ) .'} | and { only { filter_eq { filter_greater { all_rows ; gold ; 0 } ; bronze ; 2 } } ; eq { hop { filter_eq { filter_greater { all_rows ; gold ; 0 } ; bronze ; 2 } ; nation } ; soviet union ( urs ) } } = true | select the rows whose gold record is greater than 0 . among these rows , select the rows whose bronze record is equal to 2 . there is only one such row in the table . the nation record of this unqiue row is soviet union ( urs ) . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_eq_1': 1, 'filter_greater_0': 0, 'all_rows_7': 7, 'gold_8': 8, '0_9': 9, 'bronze_10': 10, '2_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'nation_12': 12, 'soviet union (urs)_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_eq_1': 'filter_eq', 'filter_greater_0': 'filter_greater', 'all_rows_7': 'all_rows', 'gold_8': 'gold', '0_9': '0', 'bronze_10': 'bronze', '2_11': '2', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'nation_12': 'nation', 'soviet union (urs)_13': 'soviet union ( urs )'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_eq_1': [2, 3], 'filter_greater_0': [1], 'all_rows_7': [0], 'gold_8': [0], '0_9': [0], 'bronze_10': [1], '2_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'nation_12': [3], 'soviet union (urs)_13': [4]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'soviet union ( urs )', '3', '3', '2', '8'], ['2', 'italy ( ita )', '1', '1', '0', '2'], ['2', 'poland ( pol )', '1', '1', '0', '2'], ['4', 'austria ( aut )', '1', '0', '0', '1'], ['5', 'bulgaria ( bul )', '0', '1', '0', '1'], ['6', 'mexico ( mex )', '0', '0', '3', '3'], ['7', 'romania ( rou )', '0', '0', '1', '1']] |
list of eintracht frankfurt records and statistics | https://en.wikipedia.org/wiki/List_of_Eintracht_Frankfurt_records_and_statistics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15453888-2.html.csv | aggregation | on average , the top goalscorers in the history of eintracht frankfurt football club have made 113 goals each . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '113', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'goals'], 'result': '113', 'ind': 0, 'tostr': 'avg { all_rows ; goals }'}, '113'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; goals } ; 113 } = true', 'tointer': 'the average of the goals record of all rows is 113 .'} | round_eq { avg { all_rows ; goals } ; 113 } = true | the average of the goals record of all rows is 113 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'goals_4': 4, '113_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'goals_4': 'goals', '113_5': '113'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'goals_4': [0], '113_5': [1]} | ['name', 'career', 'apps', 'goals', 'average'] | [['bernd hölzenbein', '1967 - 1981', '512', '201', '0.39'], ['bernd nickel', '1967 - 1983', '522', '175', '0.34'], ['jürgen grabowski', '1965 - 1980', '526', '137', '0.26'], ['alfred pfaff', '1949 - 1961', '324', '111', '0.34'], ['erwin stein', '1959 - 1966', '174', '108', '0.62'], ['tony yeboah', '1990 - 1995', '156', '90', '0.58'], ['richard kress', '1953 - 1964', '326', '82', '0.25'], ['willi huberts', '1963 - 1970', '246', '80', '0.33'], ['lothar schämer', '1960 - 1973', '338', '73', '0.22'], ['wolfgang solz', '1959 - 1968', '208', '72', '0.35']] |
1994 - 95 cleveland cavaliers season | https://en.wikipedia.org/wiki/1994%E2%80%9395_Cleveland_Cavaliers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16188254-7.html.csv | aggregation | the average crowd attendance in the 1994 - 95 cleveland cavaliers season was 18728 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '18728', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '18728', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '18728'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 18728 } = true', 'tointer': 'the average of the attendance record of all rows is 18728 .'} | round_eq { avg { all_rows ; attendance } ; 18728 } = true | the average of the attendance record of all rows is 18728 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '18728_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '18728_5': '18728'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '18728_5': [1]} | ['date', 'visitor', 'score', 'home', 'leading scorer', 'attendance', 'record'] | [['march 2', 'cleveland', '84 - 90', 'dallas', 'chris mills , 16 points', 'reunion arena 12194', '33 - 23'], ['march 4', 'new york', '89 - 76', 'cleveland', 'hot rod williams , 20 points', 'gund arena 20562', '33 - 24'], ['march 7', 'detroit', '81 - 89', 'cleveland', 'chris mills , 24 points', 'gund arena 20562', '34 - 24'], ['march 9', 'san antonio', '100 - 98', 'cleveland', 'terrell brandon , 24 points', 'gund arena 20562', '34 - 25'], ['march 10', 'cleveland', '76 - 99', 'chicago', 'tyrone hill , 13 points', 'united center 22362', '34 - 26'], ['march 12', 'cleveland', '92 - 72', 'philadelphia', '3 way tie , 14 points', 'corestates spectrum 10221', '35 - 26'], ['march 16', 'utah', '85 - 93', 'cleveland', 'bobby phills , 24 points', 'gund arena 20562', '36 - 26'], ['march 17', 'cleveland', '77 - 80', 'minnesota', 'mark price , 18 points', 'target center 14222', '36 - 27'], ['march 19', 'cleveland', '90 - 96', 'washington', 'mark price , 16 points', 'usair arena 17110', '36 - 28'], ['march 20', 'dallas', '102 - 100', 'cleveland', 'tyrone hill , 29 points', 'gund arena 20562', '36 - 29'], ['march 22', 'sacramento', '89 - 101', 'cleveland', 'mark price , 23 points', 'gund arena 20562', '37 - 29'], ['march 24', 'atlanta', '74 - 75', 'cleveland', 'tyrone hill , 24 points', 'gund arena 20562', '38 - 29'], ['march 25', 'cleveland', '97 - 105', 'charlotte', 'chris mills , 26 points', 'charlotte coliseum 23698', '38 - 30'], ['march 29', 'cleveland', '96 - 107', 'indiana', 'chris mills , 22 points', 'market square arena 16619', '38 - 31'], ['march 31', 'washington', '88 - 98', 'cleveland', 'chris mills , 24 points', 'gund arena 20562', '39 - 31']] |
scott ferrozzo | https://en.wikipedia.org/wiki/Scott_Ferrozzo | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17958251-2.html.csv | comparative | scott ferrozzo 's fight against vitor belfort had a shorter time than his fight against jim mullen . | {'row_1': '1', 'row_2': '2', 'col': '7', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'vitor belfort'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to vitor belfort .', 'tostr': 'filter_eq { all_rows ; opponent ; vitor belfort }'}, 'time'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; vitor belfort } ; time }', 'tointer': 'select the rows whose opponent record fuzzily matches to vitor belfort . take the time record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'jim mullen'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to jim mullen .', 'tostr': 'filter_eq { all_rows ; opponent ; jim mullen }'}, 'time'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; jim mullen } ; time }', 'tointer': 'select the rows whose opponent record fuzzily matches to jim mullen . take the time record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; opponent ; vitor belfort } ; time } ; hop { filter_eq { all_rows ; opponent ; jim mullen } ; time } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to vitor belfort . take the time record of this row . select the rows whose opponent record fuzzily matches to jim mullen . take the time record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; opponent ; vitor belfort } ; time } ; hop { filter_eq { all_rows ; opponent ; jim mullen } ; time } } = true | select the rows whose opponent record fuzzily matches to vitor belfort . take the time record of this row . select the rows whose opponent record fuzzily matches to jim mullen . take the time 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, 'opponent_7': 7, 'vitor belfort_8': 8, 'time_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'jim mullen_12': 12, 'time_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', 'opponent_7': 'opponent', 'vitor belfort_8': 'vitor belfort', 'time_9': 'time', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'jim mullen_12': 'jim mullen', 'time_13': 'time'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'vitor belfort_8': [0], 'time_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'jim mullen_12': [1], 'time_13': [3]} | ['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location'] | [['loss', '4 - 2', 'vitor belfort', 'tko ( punches )', 'ufc 12', '1', '0:43', 'dothan , alabama , united states'], ['win', '4 - 1', 'jim mullen', 'tko ( punches )', 'ufc 12', '1', '8:02', 'dothan , alabama , united states'], ['win', '3 - 1', 'tank abbott', 'decision ( unanimous )', 'ufc 11', '1', '15:00', 'augusta , georgia , united states'], ['win', '2 - 1', 'sam fulton', 'submission ( strikes )', 'ufc 11', '1', '9:00', 'augusta , georgia , united states'], ['win', '1 - 1', 'steve grinnow', 'ko', 'atlanta fights', '1', '11:58', 'atlanta , georgia , united states'], ['loss', '0 - 1', 'jerry bohlander', 'submission ( guillotine choke )', 'ufc 8', '1', '9:03', 'san juan , puerto rico']] |
2008 - 09 fa cup qualifying rounds | https://en.wikipedia.org/wiki/2008%E2%80%9309_FA_Cup_Qualifying_Rounds | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18054397-18.html.csv | aggregation | the average attendance for the 2008 - 09 fa cup qualifying rounds was 743 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '743', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '743', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '743'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 743 } = true', 'tointer': 'the average of the attendance record of all rows is 743 .'} | round_eq { avg { all_rows ; attendance } ; 743 } = true | the average of the attendance record of all rows is 743 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '743_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '743_5': '743'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '743_5': [1]} | ['tie no', 'home team', 'score', 'away team', 'attendance'] | [['1', 'curzon ashton', '1 - 1', 'hinckley united', '519'], ['curzon ashton won 3 - 2 on penalties', 'curzon ashton won 3 - 2 on penalties', 'curzon ashton won 3 - 2 on penalties', 'curzon ashton won 3 - 2 on penalties', 'curzon ashton won 3 - 2 on penalties'], ['7', 'belper town', '1 - 2', 'droylsden', '568'], ['8', 'histon', '5 - 2', 'durham city', '441'], ['12', 'mansfield town', '1 - 0', 'york city', '2004'], ['14', 'eastwood town', '2 - 0', 'wrexham', '860'], ['15', 'horsham', '1 - 4', 'stevenage borough', '641'], ['16', 'ebbsfleet united', '1 - 0', 'woking', '869'], ['23', 'forest green rovers', '4 - 0', 'ashford town ( middx )', '425'], ['30', 'lewes', '1 - 3', 'leiston', '363']] |
2010 atlantic coast conference football season | https://en.wikipedia.org/wiki/2010_Atlantic_Coast_Conference_football_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28744929-1.html.csv | unique | in the public school type , the only one that joined the acc in the 1970s was georgia tech . | {'scope': 'subset', 'row': '5', 'col': '5', 'col_other': '1', 'criterion': 'fuzzily_match', 'value': '197', 'subset': {'col': '6', 'criterion': 'equal', 'value': 'public'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school type', 'public'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; school type ; public }', 'tointer': 'select the rows whose school type record fuzzily matches to public .'}, 'joined acc', '197'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose school type record fuzzily matches to public . among these rows , select the rows whose joined acc record fuzzily matches to 197 .', 'tostr': 'filter_eq { filter_eq { all_rows ; school type ; public } ; joined acc ; 197 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; school type ; public } ; joined acc ; 197 } }', 'tointer': 'select the rows whose school type record fuzzily matches to public . among these rows , select the rows whose joined acc record fuzzily matches to 197 . 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', 'school type', 'public'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; school type ; public }', 'tointer': 'select the rows whose school type record fuzzily matches to public .'}, 'joined acc', '197'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose school type record fuzzily matches to public . among these rows , select the rows whose joined acc record fuzzily matches to 197 .', 'tostr': 'filter_eq { filter_eq { all_rows ; school type ; public } ; joined acc ; 197 }'}, 'institution'], 'result': 'georgia tech', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; school type ; public } ; joined acc ; 197 } ; institution }'}, 'georgia tech'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; school type ; public } ; joined acc ; 197 } ; institution } ; georgia tech }', 'tointer': 'the institution record of this unqiue row is georgia tech .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; school type ; public } ; joined acc ; 197 } } ; eq { hop { filter_eq { filter_eq { all_rows ; school type ; public } ; joined acc ; 197 } ; institution } ; georgia tech } } = true', 'tointer': 'select the rows whose school type record fuzzily matches to public . among these rows , select the rows whose joined acc record fuzzily matches to 197 . there is only one such row in the table . the institution record of this unqiue row is georgia tech .'} | and { only { filter_eq { filter_eq { all_rows ; school type ; public } ; joined acc ; 197 } } ; eq { hop { filter_eq { filter_eq { all_rows ; school type ; public } ; joined acc ; 197 } ; institution } ; georgia tech } } = true | select the rows whose school type record fuzzily matches to public . among these rows , select the rows whose joined acc record fuzzily matches to 197 . there is only one such row in the table . the institution record of this unqiue row is georgia tech . | 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, 'school type_8': 8, 'public_9': 9, 'joined acc_10': 10, '197_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'institution_12': 12, 'georgia tech_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', 'school type_8': 'school type', 'public_9': 'public', 'joined acc_10': 'joined acc', '197_11': '197', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'institution_12': 'institution', 'georgia tech_13': 'georgia tech'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'school type_8': [0], 'public_9': [0], 'joined acc_10': [1], '197_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'institution_12': [3], 'georgia tech_13': [4]} | ['institution', 'nickname', 'location', 'founded', 'joined acc', 'school type', 'acc football titles'] | [['boston college', 'eagles', 'chestnut hill , massachusetts', '1863', '2005', 'private / jesuit', '0'], ['clemson', 'tigers', 'clemson , south carolina', '1889', '1953', 'public', '13'], ['duke', 'blue devils', 'durham , north carolina', '1838', '1953', 'private / non - sectarian', '7'], ['florida state', 'seminoles', 'tallahassee , florida', '1851', '1991', 'public', '12'], ['georgia tech', 'yellow jackets', 'atlanta , georgia', '1885', '1979', 'public', '3'], ['maryland', 'terrapins', 'college park , maryland', '1856', '1953', 'public', '9'], ['miami', 'hurricanes', 'coral gables , florida', '1925', '2004', 'private / non - sectarian', '0'], ['north carolina', 'tar heels', 'chapel hill , north carolina', '1789', '1953', 'public', '5'], ['nc state', 'wolfpack', 'raleigh , north carolina', '1887', '1953', 'public', '7'], ['virginia', 'cavaliers', 'charlottesville , virginia', '1819', '1953', 'public', '2'], ['virginia tech', 'hokies', 'blacksburg , virginia', '1872', '2004', 'public', '3']] |
demographics of the faroe islands | https://en.wikipedia.org/wiki/Demographics_of_the_Faroe_Islands | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10700-2.html.csv | unique | the only place that has no inhabitants is litla dimun . | {'scope': 'all', 'row': '18', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': '0', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'inhabitants', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose inhabitants record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; inhabitants ; 0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; inhabitants ; 0 } }', 'tointer': 'select the rows whose inhabitants record is equal to 0 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'inhabitants', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose inhabitants record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; inhabitants ; 0 }'}, 'name'], 'result': 'lítla dímun', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; inhabitants ; 0 } ; name }'}, 'lítla dímun'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; inhabitants ; 0 } ; name } ; lítla dímun }', 'tointer': 'the name record of this unqiue row is lítla dímun .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; inhabitants ; 0 } } ; eq { hop { filter_eq { all_rows ; inhabitants ; 0 } ; name } ; lítla dímun } } = true', 'tointer': 'select the rows whose inhabitants record is equal to 0 . there is only one such row in the table . the name record of this unqiue row is lítla dímun .'} | and { only { filter_eq { all_rows ; inhabitants ; 0 } } ; eq { hop { filter_eq { all_rows ; inhabitants ; 0 } ; name } ; lítla dímun } } = true | select the rows whose inhabitants record is equal to 0 . there is only one such row in the table . the name record of this unqiue row is lítla dímun . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'inhabitants_7': 7, '0_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'lítla dímun_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'inhabitants_7': 'inhabitants', '0_8': '0', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'lítla dímun_10': 'lítla dímun'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'inhabitants_7': [0], '0_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'lítla dímun_10': [3]} | ['name', 'area', 'inhabitants', 'people per km square', 'main places', 'regions'] | [['streymoy', '373.5', '21717', '57.4', 'tórshavn and vestmanna', 'tórshavn and rest of streymoy'], ['eysturoy', '286.3', '10738', '37', 'fuglafjørður and runavík', 'north eysturoy and south eysturoy'], ['vágar', '177.6', '2856', '15.7', 'míðvágur and sørvágur', 'vágar'], ['suðuroy', '166', '5074', '30.9', 'tvøroyri and vágur', 'suðuroy'], ['sandoy', '112.1', '1428', '12.4', 'sandur', 'sandoy'], ['borðoy', '95', '5030', '52.4', 'klaksvík', 'klaksvík and rest of northern faroes ( norðoyar )'], ['viðoy', '41', '605', '15', 'viðareiði', 'norðoyar'], ['kunoy', '35.5', '135', '3.8', 'kunoy', 'norðoyar'], ['kalsoy', '30.9', '136', '4.8', 'mikladalur and húsar', 'norðoyar'], ['svínoy', '27.4', '58', '2.7', 'svínoy', 'norðoyar'], ['fugloy', '11.2', '46', '4', 'kirkja', 'norðoyar'], ['nólsoy', '10.3', '262', '26.1', 'nólsoy', 'streymoy'], ['mykines', '10.3', '19', '2', 'mykines', 'vágar'], ['skúvoy', '10', '61', '5.7', 'skúvoy', 'sandoy'], ['hestur', '6.1', '40', '7.1', 'hestur', 'streymoy'], ['stóra dímun', '2.7', '7', '1.9', 'dímun', 'sandoy'], ['koltur', '2.5', '2', '0.8', 'koltur', 'streymoy'], ['lítla dímun', '0.8', '0', '0', '-', 'sandoy']] |
2007 - 08 nashville predators season | https://en.wikipedia.org/wiki/2007%E2%80%9308_Nashville_Predators_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11756731-14.html.csv | count | in the 2007-08 nashville predators season , when the nationality was united states , there were two players from the university of notre dame . | {'scope': 'subset', 'criterion': 'fuzzily_match', 'value': 'university of notre dame', 'result': '2', 'col': '5', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'united states'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'united states'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; nationality ; united states }', 'tointer': 'select the rows whose nationality record fuzzily matches to united states .'}, 'college / junior / club team ( league )', 'university of notre dame'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose college / junior / club team ( league ) record fuzzily matches to university of notre dame .', 'tostr': 'filter_eq { filter_eq { all_rows ; nationality ; united states } ; college / junior / club team ( league ) ; university of notre dame }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; nationality ; united states } ; college / junior / club team ( league ) ; university of notre dame } }', 'tointer': 'select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose college / junior / club team ( league ) record fuzzily matches to university of notre dame . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; nationality ; united states } ; college / junior / club team ( league ) ; university of notre dame } } ; 2 } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose college / junior / club team ( league ) record fuzzily matches to university of notre dame . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; nationality ; united states } ; college / junior / club team ( league ) ; university of notre dame } } ; 2 } = true | select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose college / junior / club team ( league ) record fuzzily matches to university of notre dame . 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, 'nationality_6': 6, 'united states_7': 7, 'college / junior / club team (league)_8': 8, 'university of notre dame_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', 'nationality_6': 'nationality', 'united states_7': 'united states', 'college / junior / club team (league)_8': 'college / junior / club team ( league )', 'university of notre dame_9': 'university of notre dame', '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], 'nationality_6': [0], 'united states_7': [0], 'college / junior / club team (league)_8': [1], 'university of notre dame_9': [1], '2_10': [3]} | ['round', 'player', 'position', 'nationality', 'college / junior / club team ( league )'] | [['1', 'jonathon blum', 'd', 'united states', 'vancouver giants ( whl )'], ['2', 'jeremy smith', 'g', 'united states', 'plymouth whalers ( ohl )'], ['2', 'nick spaling', 'c', 'canada', 'kitchener rangers ( ohl )'], ['3', 'ryan thang', 'lw', 'united states', 'university of notre dame ( ccha )'], ['4', 'ben ryan', 'c', 'united states', 'university of notre dame ( ccha )'], ['4', 'mark santorelli', 'c', 'canada', 'chilliwack bruins ( whl )'], ['5', 'andreas thuresson', 'w', 'sweden', 'malmã redhawks ( sel )'], ['6', 'robert dietrich', 'd', 'germany', 'deg metro stars ( germany )'], ['7', 'atte engren', 'g', 'finland', 'lukko ( sm - liiga )']] |
acc - big ten challenge | https://en.wikipedia.org/wiki/ACC%E2%80%93Big_Ten_Challenge | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1672976-5.html.csv | ordinal | the duke acc team game recorded the highest attendance of the acc - big ten challenge . | {'row': '6', 'col': '7', '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', 'attendance', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; attendance ; 1 }'}, 'acc team'], 'result': '4 duke', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; attendance ; 1 } ; acc team }'}, '4 duke'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; acc team } ; 4 duke } = true', 'tointer': 'select the row whose attendance record of all rows is 1st maximum . the acc team record of this row is 4 duke .'} | eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; acc team } ; 4 duke } = true | select the row whose attendance record of all rows is 1st maximum . the acc team record of this row is 4 duke . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '1_6': 6, 'acc team_7': 7, '4 duke_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', '1_6': '1', 'acc team_7': 'acc team', '4 duke_8': '4 duke'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '1_6': [0], 'acc team_7': [1], '4 duke_8': [2]} | ['date', 'time', 'acc team', 'big ten team', 'location', 'television', 'attendance', 'winner', 'challenge leader'] | [['tue , nov 29', '7:00 pm', 'virginia', '15 michigan', 'john paul jones arena charlottesville , va', 'espn2', '10564', 'virginia ( 70 - 58 )', 'acc ( 1 - 0 )'], ['tue , nov 29', '7:15 pm', 'georgia tech', 'northwestern', 'philips arena atlanta , ga', 'espnu', '5619', 'northwestern ( 76 - 60 )', 'tied ( 1 - 1 )'], ['tue , nov 29', '7:30 pm', 'maryland', 'illinois', 'comcast center college park , md', 'espn', '13187', 'illinois ( 71 - 62 )', 'big ten ( 2 - 1 )'], ['tue , nov 29', '9:00 pm', 'miami', 'purdue', 'mackey arena west lafayette , in', 'espn2', '13927', 'purdue ( 76 - 65 )', 'big ten ( 3 - 1 )'], ['tue , nov 29', '9:15 pm', 'clemson', 'iowa', 'carver - hawkeye arena iowa city , ia', 'espnu', '10449', 'clemson ( 71 - 55 )', 'big ten ( 3 - 2 )'], ['tue , nov 29', '9:30 pm', '4 duke', '2 ohio state', 'value city arena columbus , oh', 'espn', '18809', 'ohio state ( 85 - 63 )', 'big ten ( 4 - 2 )'], ['wed , nov 30', '7:15 pm', 'nc state', 'indiana', 'rbc center raleigh , nc', 'espn2', '16597', 'indiana ( 86 - 75 )', 'big ten ( 5 - 2 )'], ['wed , nov 30', '7:15 pm', 'boston college', 'penn state', 'conte forum chestnut hill , ma', 'espnu', '4326', 'penn state ( 62 - 54 )', 'big ten ( 6 - 2 )'], ['wed , nov 30', '7:30 pm', 'florida state', 'michigan state', 'breslin student events center east lansing , mi', 'espn', '14797', 'michigan state ( 65 - 49 )', 'big ten ( 7 - 2 )'], ['wed , nov 30', '9:15 pm', 'virginia tech', 'minnesota', 'williams arena minneapolis , mn', 'espn2', '10487', 'minnesota ( 58 - 55 )', 'big ten ( 8 - 2 )'], ['wed , nov 30', '9:15 pm', 'wake forest', 'nebraska', 'bob devaney sports center lincoln , ne', 'espnu', '9769', 'wake forest ( 55 - 53 )', 'big ten ( 8 - 3 )']] |
list of indoor arenas in the philippines | https://en.wikipedia.org/wiki/List_of_indoor_arenas_in_the_Philippines | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12258195-2.html.csv | ordinal | the quadricentennial pavilion has the second smallest seating capacity of any of these arenas . | {'row': '4', 'col': '5', 'order': '2', 'col_other': '1', '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', 'maximum seating capacity', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; maximum seating capacity ; 2 }'}, 'arena / venue'], 'result': 'quadricentennial pavilion', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; maximum seating capacity ; 2 } ; arena / venue }'}, 'quadricentennial pavilion'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; maximum seating capacity ; 2 } ; arena / venue } ; quadricentennial pavilion } = true', 'tointer': 'select the row whose maximum seating capacity record of all rows is 2nd minimum . the arena / venue record of this row is quadricentennial pavilion .'} | eq { hop { nth_argmin { all_rows ; maximum seating capacity ; 2 } ; arena / venue } ; quadricentennial pavilion } = true | select the row whose maximum seating capacity record of all rows is 2nd minimum . the arena / venue record of this row is quadricentennial pavilion . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'maximum seating capacity_5': 5, '2_6': 6, 'arena / venue_7': 7, 'quadricentennial pavilion_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', 'maximum seating capacity_5': 'maximum seating capacity', '2_6': '2', 'arena / venue_7': 'arena / venue', 'quadricentennial pavilion_8': 'quadricentennial pavilion'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'maximum seating capacity_5': [0], '2_6': [0], 'arena / venue_7': [1], 'quadricentennial pavilion_8': [2]} | ['arena / venue', 'home campus', 'location', 'province / region', 'maximum seating capacity', 'year opened'] | [['blue eagle gym', 'ateneo de manila university', 'quezon city', 'metro manila', '7500', '1949'], ['la salle coliseum', 'university of st la salle', 'bacolod city', 'negros occidental', '8000', '1998'], ['olivarez sports center', 'olivarez college', 'paraã ± aque city', 'metro manila', 'unknown', 'unknown'], ['quadricentennial pavilion', 'university of sto tomas', 'manila', 'metro manila', '5792', '2009'], ['san agustin gym', 'university of san agustin', 'iloilo city', 'iloilo', 'unknown', 'unknown'], ['university of baguio gym', 'university of baguio', 'baguio city', 'benguet', '5000', 'unknown'], ['west negros university gym', 'west negros university', 'bacolod city', 'negros occidental', 'unknown', 'unknown'], ['xavier university gym', 'xavier university - ateneo de cagayan', 'cagayan de oro city', 'misamis oriental', 'unknown', 'unknown'], ['holy cross of davao college gym ( hcdc )', 'holy cross of davao college', 'davao city', 'davao del sur', '7000', '2001']] |
1992 - 93 argentine primera división | https://en.wikipedia.org/wiki/1992%E2%80%9393_Argentine_Primera_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17968282-1.html.csv | aggregation | in 1992 - 93 argentine primera división , an average number of points was 111 . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '111', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'points'], 'result': '111', 'ind': 0, 'tostr': 'avg { all_rows ; points }'}, '111'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; points } ; 111 } = true', 'tointer': 'the average of the points record of all rows is 111 .'} | round_eq { avg { all_rows ; points } ; 111 } = true | the average of the points record of all rows is 111 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'points_4': 4, '111_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'points_4': 'points', '111_5': '111'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'points_4': [0], '111_5': [1]} | ['team', 'average', 'points', 'played', '1991 - 92', '1992 - 93', '1993 - 94'] | [['boca juniors', '1.307', '149', '114', '51', '50', '48'], ['river plate', '1.281', '146', '114', '45', '55', '46'], ['vélez sársfield', '1.237', '141', '114', '45', '48', '48'], ['san lorenzo', '1.088', '124', '114', '45', '45', '45'], ['huracán', '1.061', '121', '114', '40', '38', '43'], ['independiente', '1.026', '117', '114', '40', '36', '41'], ["newell 's old boys", '1.026', '117', '114', '48', '44', '25'], ['racing club', '1.009', '115', '114', '40', '39', '36'], ['deportivo español', '1.000', '114', '114', '28', '45', '41'], ['ferro carril oeste', '0.991', '113', '114', '38', '37', '38'], ['rosario central', '0.982', '112', '114', '39', '34', '39'], ['lanús', '0.974', '37', '38', 'n / a', 'n / a', '37'], ['belgrano de córdoba', '0.961', '73', '76', 'n / a', '35', '38'], ['deportivo mandiyú', '0.947', '108', '114', '38', '33', '37'], ['gimnasia de la plata', '0.947', '108', '114', '33', '41', '34'], ['estudiantes de la plata', '0.930', '106', '114', '39', '29', '38'], ['platense', '0.921', '105', '114', '35', '42', '28'], ['argentinos juniors', '0.912', '104', '114', '36', '35', '33'], ['talleres de córdoba', '0.851', '97', '114', '29', '37', '31']] |
maria joão koehler | https://en.wikipedia.org/wiki/Maria_Jo%C3%A3o_Koehler | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22656187-9.html.csv | unique | maria joão koehler partnered with neuza silva only once during the fed cup europe / africa group games . | {'scope': 'all', 'row': '4', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'neuza silva', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'partnering', 'neuza silva'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose partnering record fuzzily matches to neuza silva .', 'tostr': 'filter_eq { all_rows ; partnering ; neuza silva }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; partnering ; neuza silva } }', 'tointer': 'select the rows whose partnering record fuzzily matches to neuza silva . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'partnering', 'neuza silva'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose partnering record fuzzily matches to neuza silva .', 'tostr': 'filter_eq { all_rows ; partnering ; neuza silva }'}, 'edition'], 'result': '2010 fed cup europe / africa group i', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; partnering ; neuza silva } ; edition }'}, '2010 fed cup europe / africa group i'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; partnering ; neuza silva } ; edition } ; 2010 fed cup europe / africa group i }', 'tointer': 'the edition record of this unqiue row is 2010 fed cup europe / africa group i .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; partnering ; neuza silva } } ; eq { hop { filter_eq { all_rows ; partnering ; neuza silva } ; edition } ; 2010 fed cup europe / africa group i } } = true', 'tointer': 'select the rows whose partnering record fuzzily matches to neuza silva . there is only one such row in the table . the edition record of this unqiue row is 2010 fed cup europe / africa group i .'} | and { only { filter_eq { all_rows ; partnering ; neuza silva } } ; eq { hop { filter_eq { all_rows ; partnering ; neuza silva } ; edition } ; 2010 fed cup europe / africa group i } } = true | select the rows whose partnering record fuzzily matches to neuza silva . there is only one such row in the table . the edition record of this unqiue row is 2010 fed cup europe / africa group i . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'partnering_7': 7, 'neuza silva_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'edition_9': 9, '2010 fed cup europe / africa group i_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'partnering_7': 'partnering', 'neuza silva_8': 'neuza silva', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'edition_9': 'edition', '2010 fed cup europe / africa group i_10': '2010 fed cup europe / africa group i'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'partnering_7': [0], 'neuza silva_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'edition_9': [2], '2010 fed cup europe / africa group i_10': [3]} | ['edition', 'round', 'date', 'partnering', 'against', 'surface', 'opponents', 'w - l', 'result'] | [['2008 fed cup europe / africa group i', 'rr', '30 january - 3 february 2008', 'magali de lattre', 'bulgaria', 'carpet', 'dia evtimova tsvetana pironkova', 'loss', '1 - 6 , 2 - 6'], ['2008 fed cup europe / africa group i', 'rr', '30 january - 3 february 2008', 'magali de lattre', 'the netherlands', 'carpet', 'nicole thijssen pauline wong', 'loss', '2 - 6 , 4 - 6'], ['2010 fed cup europe / africa group i', 'rr', '4 - 5 february 2010', 'frederica piedade', 'switzerland', 'hard', 'sarah moundir amra sadikovic', 'loss', '5 - 7 , 7 - 5 , 6 - 4'], ['2010 fed cup europe / africa group i', 'rr', '4 - 5 february 2010', 'neuza silva', 'romania', 'hard', 'irina - camelia begu ioana raluca olaru', 'win', '7 - 5 , 7 - 5'], ['2011 fed cup europe / africa group ii', 'rr', '4 - 6 may 2011', 'michelle larcher de brito', 'morocco', 'clay', 'fatima el allami nadia lalami', 'win', '6 - 3 , 6 - 2'], ['2011 fed cup europe / africa group ii', 'rr', '4 - 6 may 2011', 'michelle larcher de brito', 'finland', 'clay', 'emma laine piia suomalainen', 'win', '6 - 3 , 6 - 2'], ['2012 fed cup europe / africa group i', 'rr', '1 - 3 february 2012', 'michelle larcher de brito', 'great britain', 'hard', 'laura robson heather watson', 'loss', '5 - 7 , 0 - 6']] |
1977 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1977_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17245483-3.html.csv | count | in the 1977 u.s. open ( golf ) , among the players that had place t1 , 6 of them were from united states . | {'scope': 'subset', 'criterion': 'equal', 'value': 'united states', 'result': '6', 'col': '3', 'subset': {'col': '1', 'criterion': 'equal', 'value': 't1'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'place', 't1'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; place ; t1 }', 'tointer': 'select the rows whose place record fuzzily matches to t1 .'}, 'country', 'united states'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose place record fuzzily matches to t1 . among these rows , select the rows whose country record fuzzily matches to united states .', 'tostr': 'filter_eq { filter_eq { all_rows ; place ; t1 } ; country ; united states }'}], 'result': '6', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; place ; t1 } ; country ; united states } }', 'tointer': 'select the rows whose place record fuzzily matches to t1 . among these rows , select the rows whose country record fuzzily matches to united states . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; place ; t1 } ; country ; united states } } ; 6 } = true', 'tointer': 'select the rows whose place record fuzzily matches to t1 . among these rows , select the rows whose country record fuzzily matches to united states . the number of such rows is 6 .'} | eq { count { filter_eq { filter_eq { all_rows ; place ; t1 } ; country ; united states } } ; 6 } = true | select the rows whose place record fuzzily matches to t1 . among these rows , select the rows whose country record fuzzily matches to united states . the number of such rows is 6 . | 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, 'place_6': 6, 't1_7': 7, 'country_8': 8, 'united states_9': 9, '6_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', 'place_6': 'place', 't1_7': 't1', 'country_8': 'country', 'united states_9': 'united states', '6_10': '6'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'place_6': [0], 't1_7': [0], 'country_8': [1], 'united states_9': [1], '6_10': [3]} | ['place', 'player', 'country', 'score', 'to par'] | [['t1', 'terry diehl', 'united states', '69', '- 1'], ['t1', 'rod funseth', 'united states', '69', '- 1'], ['t1', 'hubert green', 'united states', '69', '- 1'], ['t1', 'grier jones', 'united states', '69', '- 1'], ['t1', 'florentino molina', 'argentina', '69', '- 1'], ['t1', 'larry nelson', 'united states', '69', '- 1'], ['t1', 'tom purtzer', 'united states', '69', '- 1'], ['t8', 'sam adams', 'united states', '70', 'e'], ['t8', 'george burns', 'united states', '70', 'e'], ['t8', 'al geiberger', 'united states', '70', 'e'], ['t8', 'morris hatalsky', 'united states', '70', 'e'], ['t8', 'joe inman', 'united states', '70', 'e'], ['t8', 'steve melnyk', 'united states', '70', 'e'], ['t8', 'mike morley', 'united states', '70', 'e'], ['t8', 'don padgett', 'united states', '70', 'e'], ['t8', 'arnold palmer', 'united states', '70', 'e'], ['t8', 'bob e smith', 'united states', '70', 'e']] |
john garamendi | https://en.wikipedia.org/wiki/John_Garamendi | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1602620-1.html.csv | unique | the only time that john garamendi was elected as state assemblyman was in 1974 . | {'scope': 'all', 'row': '1', 'col': '1', 'col_other': '4', 'criterion': 'equal', 'value': 'state assemblyman', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'office', 'state assemblyman'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose office record fuzzily matches to state assemblyman .', 'tostr': 'filter_eq { all_rows ; office ; state assemblyman }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; office ; state assemblyman } }', 'tointer': 'select the rows whose office record fuzzily matches to state assemblyman . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'office', 'state assemblyman'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose office record fuzzily matches to state assemblyman .', 'tostr': 'filter_eq { all_rows ; office ; state assemblyman }'}, 'elected'], 'result': '1974', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; office ; state assemblyman } ; elected }'}, '1974'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; office ; state assemblyman } ; elected } ; 1974 }', 'tointer': 'the elected record of this unqiue row is 1974 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; office ; state assemblyman } } ; eq { hop { filter_eq { all_rows ; office ; state assemblyman } ; elected } ; 1974 } } = true', 'tointer': 'select the rows whose office record fuzzily matches to state assemblyman . there is only one such row in the table . the elected record of this unqiue row is 1974 .'} | and { only { filter_eq { all_rows ; office ; state assemblyman } } ; eq { hop { filter_eq { all_rows ; office ; state assemblyman } ; elected } ; 1974 } } = true | select the rows whose office record fuzzily matches to state assemblyman . there is only one such row in the table . the elected record of this unqiue row is 1974 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'office_7': 7, 'state assemblyman_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'elected_9': 9, '1974_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'office_7': 'office', 'state assemblyman_8': 'state assemblyman', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'elected_9': 'elected', '1974_10': '1974'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'office_7': [0], 'state assemblyman_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'elected_9': [2], '1974_10': [3]} | ['office', 'type', 'location', 'elected', 'term began', 'term ended'] | [['state assemblyman', 'legislature', 'sacramento', '1974', 'december 7 , 1974', 'december 2 , 1976'], ['state senator', 'legislature', 'sacramento', '1976', 'december 2 , 1976', 'december 8 , 1980'], ['state senator', 'legislature', 'sacramento', '1980', 'december 8 , 1980', 'december 3 , 1984'], ['state senator', 'legislature', 'sacramento', '1984', 'december 3 , 1984', 'december 5 , 1988'], ['state senator', 'legislature', 'sacramento', '1988', 'december 5 , 1988', 'december 3 , 1990'], ['insurance commissioner', 'executive', 'sacramento', '1990', 'january 7 , 1991', 'january 2 , 1995'], ['insurance commissioner', 'executive', 'sacramento', '2002', 'january 6 , 2003', 'january 8 , 2007'], ['lieutenant governor', 'executive', 'sacramento', '2006', 'january 8 , 2007', 'november 5 , 2009'], ['us representative', 'legislative', 'washington , dc', '2009', 'november 5 , 2009', 'january 3 , 2011']] |
list of highest - grossing bollywood films | https://en.wikipedia.org/wiki/List_of_highest-grossing_Bollywood_films | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11872185-11.html.csv | count | a total of three movies on the list of highest - grossing bollywood films were released in the year 2013 . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': '2013', 'result': '3', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '2013'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 2013 .', 'tostr': 'filter_eq { all_rows ; year ; 2013 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; year ; 2013 } }', 'tointer': 'select the rows whose year record fuzzily matches to 2013 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; year ; 2013 } } ; 3 } = true', 'tointer': 'select the rows whose year record fuzzily matches to 2013 . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; year ; 2013 } } ; 3 } = true | select the rows whose year record fuzzily matches to 2013 . 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, 'year_5': 5, '2013_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', 'year_5': 'year', '2013_6': '2013', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'year_5': [0], '2013_6': [0], '3_7': [2]} | ['rank', 'movie', 'year', 'studio ( s )', 'third week nett gross'] | [['1', '3 idiots', '2009', 'vinod chopra productions', '30 , 30 , 00000'], ['2', 'yeh jawaani hai deewani', '2013', 'dharma productions', '19 , 60 , 00000'], ['3', 'chennai express', '2013', 'red chillies entertainment', '18 , 31 , 00000'], ['4', 'dabangg', '2010', 'arbaaz khan productions', '17 , 21 , 00000'], ['5', 'barfi', '2012', 'utv motion pictures', '15 , 70 , 00000'], ['6', 'bhaag milkha bhaag', '2013', 'viacom 18', '15 , 49 , 00000'], ['7', 'rowdy rathore', '2012', 'utv motion pictures', '15 , 16 , 00000'], ['8', 'ghajini', '2008', 'reliance entertainment', '14 , 13 , 00000'], ['9', 'ready', '2010', 't - series', '13 , 61 , 00000'], ['10', 'omg ! oh my god', '2012', 'paresh rawal', '13 , 44 , 00000']] |
1941 vfl season | https://en.wikipedia.org/wiki/1941_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10807673-16.html.csv | ordinal | the mcg venue recorded the highest crowd participation during the 1941 vfl season . | {'row': '6', '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'] | [['fitzroy', '14.15 ( 99 )', 'richmond', '12.16 ( 88 )', 'brunswick street oval', '11000', '16 august 1941'], ['essendon', '19.17 ( 131 )', 'hawthorn', '14.9 ( 93 )', 'windy hill', '7000', '16 august 1941'], ['carlton', '20.17 ( 137 )', 'st kilda', '11.14 ( 80 )', 'princes park', '8000', '16 august 1941'], ['south melbourne', '10.10 ( 70 )', 'geelong', '10.9 ( 69 )', 'lake oval', '4000', '16 august 1941'], ['north melbourne', '15.14 ( 104 )', 'footscray', '11.22 ( 88 )', 'arden street oval', '8000', '16 august 1941'], ['melbourne', '17.8 ( 110 )', 'collingwood', '11.21 ( 87 )', 'mcg', '31000', '16 august 1941']] |
list of amusement park rankings | https://en.wikipedia.org/wiki/List_of_amusement_park_rankings | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16578883-7.html.csv | superlative | in the rankings for amusement parks , the water park with the best ranking is typhoon lagoon at walt disney world resort . | {'scope': 'all', 'col_superlative': '1', 'row_superlative': '1', 'value_mentioned': 'yes', 'max_or_min': 'min', 'other_col': '2', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'min', 'args': ['all_rows', 'rank'], 'result': '1', 'ind': 0, 'tostr': 'min { all_rows ; rank }', 'tointer': 'the minimum rank record of all rows is 1 .'}, '1'], 'result': True, 'ind': 1, 'tostr': 'eq { min { all_rows ; rank } ; 1 }', 'tointer': 'the minimum rank record of all rows is 1 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'rank'], 'result': None, 'ind': 2, 'tostr': 'argmin { all_rows ; rank }'}, 'water park'], 'result': 'typhoon lagoon at walt disney world resort', 'ind': 3, 'tostr': 'hop { argmin { all_rows ; rank } ; water park }'}, 'typhoon lagoon at walt disney world resort'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmin { all_rows ; rank } ; water park } ; typhoon lagoon at walt disney world resort }', 'tointer': 'the water park record of the row with superlative rank record is typhoon lagoon at walt disney world resort .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { min { all_rows ; rank } ; 1 } ; eq { hop { argmin { all_rows ; rank } ; water park } ; typhoon lagoon at walt disney world resort } } = true', 'tointer': 'the minimum rank record of all rows is 1 . the water park record of the row with superlative rank record is typhoon lagoon at walt disney world resort .'} | and { eq { min { all_rows ; rank } ; 1 } ; eq { hop { argmin { all_rows ; rank } ; water park } ; typhoon lagoon at walt disney world resort } } = true | the minimum rank record of all rows is 1 . the water park record of the row with superlative rank record is typhoon lagoon at walt disney world resort . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'min_0': 0, 'all_rows_7': 7, 'rank_8': 8, '1_9': 9, 'str_eq_4': 4, 'str_hop_3': 3, 'argmin_2': 2, 'all_rows_10': 10, 'rank_11': 11, 'water park_12': 12, 'typhoon lagoon at walt disney world resort_13': 13} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'min_0': 'min', 'all_rows_7': 'all_rows', 'rank_8': 'rank', '1_9': '1', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'argmin_2': 'argmin', 'all_rows_10': 'all_rows', 'rank_11': 'rank', 'water park_12': 'water park', 'typhoon lagoon at walt disney world resort_13': 'typhoon lagoon at walt disney world resort'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'min_0': [1], 'all_rows_7': [0], 'rank_8': [0], '1_9': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'argmin_2': [3], 'all_rows_10': [2], 'rank_11': [2], 'water park_12': [3], 'typhoon lagoon at walt disney world resort_13': [4]} | ['rank', 'water park', 'location', '2011', '2012'] | [['1', 'typhoon lagoon at walt disney world resort', 'lake buena vista , florida , usa', '2058000', '2100000'], ['2', 'chime - long water park', 'guangzhou , china', '1900000', '2021000'], ['3', 'blizzard beach at walt disney world resort', 'lake buena vista , florida , usa', '1891000', '1929000'], ['4', 'ocean world', 'gangwon - do , south korea', '1726000', '1720000'], ['5', 'aquatica', 'orlando , florida , usa', '1500000', '1538000'], ['6', 'caribbean bay at everland resort', 'gyeonggi - do , south korea', '1497000', '1508000'], ['7', 'aquaventure', 'dubai , united arab emirates', '1200000', '1300000'], ['8', "wet 'n wild orlando", 'orlando , florida , usa', '1223000', '1247000'], ['9', "wet 'n' wild water world", 'gold coast , queensland , australia', '1200000', '1200000'], ['10', 'sunway lagoon', 'kuala lumpur , malaysia', '1040000', '1200000'], ['11', 'resom spa castle', 'chungcheongnam - do , south korea', '1034000', '1158000'], ['12', 'schlitterbahn', 'new braunfels , texas , usa', '982000', '1017000'], ['13', 'atlantis water adventure', 'jakarta , indonesia', '950000', '1000000'], ['14', 'summerland', 'tokyo , japan', '850000', '990000'], ['15', 'happy magic water cube', 'beijing , china', '768000', '968000'], ['16', 'the jungle water adventure', 'bogor , indonesia', '871000', '951000'], ['17', 'wild wadi water park', 'dubai , united arab emirates', '890000', '860000'], ['18', 'siam water park', 'tenerife , spain', '800000', '800000'], ['19', 'ocean park water adventure', 'jakarta , indonesia', '600000', '750000'], ['20', 'water country usa', 'williamsburg , virginia , usa', '723000', '748000']] |
wang shi - ting | https://en.wikipedia.org/wiki/Wang_Shi-ting | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15340120-1.html.csv | unique | the tournament played in hong kong was the only tournament in which wang shi - ting faced marianne witmeyer in the final . | {'scope': 'all', 'row': '1', 'col': '4', 'col_other': '2', 'criterion': 'equal', 'value': 'marianne witmeyer', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent in the final', 'marianne witmeyer'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent in the final record fuzzily matches to marianne witmeyer .', 'tostr': 'filter_eq { all_rows ; opponent in the final ; marianne witmeyer }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; opponent in the final ; marianne witmeyer } }', 'tointer': 'select the rows whose opponent in the final record fuzzily matches to marianne witmeyer . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent in the final', 'marianne witmeyer'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent in the final record fuzzily matches to marianne witmeyer .', 'tostr': 'filter_eq { all_rows ; opponent in the final ; marianne witmeyer }'}, 'tournament'], 'result': 'hong kong', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent in the final ; marianne witmeyer } ; tournament }'}, 'hong kong'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; opponent in the final ; marianne witmeyer } ; tournament } ; hong kong }', 'tointer': 'the tournament record of this unqiue row is hong kong .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; opponent in the final ; marianne witmeyer } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; marianne witmeyer } ; tournament } ; hong kong } } = true', 'tointer': 'select the rows whose opponent in the final record fuzzily matches to marianne witmeyer . there is only one such row in the table . the tournament record of this unqiue row is hong kong .'} | and { only { filter_eq { all_rows ; opponent in the final ; marianne witmeyer } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; marianne witmeyer } ; tournament } ; hong kong } } = true | select the rows whose opponent in the final record fuzzily matches to marianne witmeyer . there is only one such row in the table . the tournament record of this unqiue row is hong kong . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opponent in the final_7': 7, 'marianne witmeyer_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'tournament_9': 9, 'hong kong_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opponent in the final_7': 'opponent in the final', 'marianne witmeyer_8': 'marianne witmeyer', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tournament_9': 'tournament', 'hong kong_10': 'hong kong'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'opponent in the final_7': [0], 'marianne witmeyer_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'tournament_9': [2], 'hong kong_10': [3]} | ['date', 'tournament', 'surface', 'opponent in the final', 'score'] | [['september 13 , 1993', 'hong kong', 'hard', 'marianne witmeyer', '6 - 4 , 3 - 6 , 7 - 5'], ['october 4 , 1993', 'taipei , taiwan', 'hard', 'linda wild', '6 - 1 , 7 - 6 ( 4 )'], ['november 14 , 1994', 'taipei , taiwan', 'hard', 'kyoko nagatsuka', '6 - 1 , 6 - 3'], ['october 2 , 1995', 'surabaya , indonesia', 'hard', 'yi jingqian', '6 - 1 , 6 - 1'], ['october 7 , 1996', 'surabaya , indonesia', 'hard', 'nana miyagi', '6 - 4 , 6 - 0'], ['october 14 , 1996', 'beijing , china', 'hard ( i )', 'chen li', '6 - 3 , 6 - 4']] |
2007 - 08 segunda división | https://en.wikipedia.org/wiki/2007%E2%80%9308_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11828307-4.html.csv | superlative | the goalkeeper for asier riesgo had the most matches played of any team . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '3', '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', 'matches'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; matches }'}, 'goalkeeper'], 'result': 'asier riesgo', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; matches } ; goalkeeper }'}, 'asier riesgo'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; matches } ; goalkeeper } ; asier riesgo } = true', 'tointer': 'select the row whose matches record of all rows is maximum . the goalkeeper record of this row is asier riesgo .'} | eq { hop { argmax { all_rows ; matches } ; goalkeeper } ; asier riesgo } = true | select the row whose matches record of all rows is maximum . the goalkeeper record of this row is asier riesgo . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'matches_5': 5, 'goalkeeper_6': 6, 'asier riesgo_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'matches_5': 'matches', 'goalkeeper_6': 'goalkeeper', 'asier riesgo_7': 'asier riesgo'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'matches_5': [0], 'goalkeeper_6': [1], 'asier riesgo_7': [2]} | ['goalkeeper', 'goals', 'matches', 'average', 'team'] | [['carlos sánchez', '27', '33', '0.82', 'cd castellón'], ['jacobo', '29', '32', '0.91', 'cd numancia'], ['asier riesgo', '39', '42', '0.93', 'real sociedad'], ['roberto', '39', '41', '0.95', 'sporting de gijón'], ['iñaki goitia', '41', '40', '1.02', 'málaga cf'], ['pedro contreras', '37', '36', '1.03', 'cádiz cf'], ['wilfredo caballero', '41', '38', '1.08', 'elche cf'], ['bernardo', '42', '38', '1.11', 'deportivo alavés'], ['javier varas', '46', '41', '1.12', 'sevilla atlético'], ['juan pablo', '33', '29', '1.14', 'cd tenerife']] |
primera división de fútbol profesional apertura 2002 | https://en.wikipedia.org/wiki/Primera_Divisi%C3%B3n_de_F%C3%BAtbol_Profesional_Apertura_2002 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13013383-1.html.csv | comparative | cd fas conceded more goals than cd arcense in the primera división de fútbol profesional apertura 2002 . | {'row_1': '1', 'row_2': '7', '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', 'team', 'cd fas'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to cd fas .', 'tostr': 'filter_eq { all_rows ; team ; cd fas }'}, 'goals conceded'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team ; cd fas } ; goals conceded }', 'tointer': 'select the rows whose team record fuzzily matches to cd fas . take the goals conceded record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'cd arcense'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose team record fuzzily matches to cd arcense .', 'tostr': 'filter_eq { all_rows ; team ; cd arcense }'}, 'goals conceded'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; team ; cd arcense } ; goals conceded }', 'tointer': 'select the rows whose team record fuzzily matches to cd arcense . take the goals conceded record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; team ; cd fas } ; goals conceded } ; hop { filter_eq { all_rows ; team ; cd arcense } ; goals conceded } } = true', 'tointer': 'select the rows whose team record fuzzily matches to cd fas . take the goals conceded record of this row . select the rows whose team record fuzzily matches to cd arcense . take the goals conceded record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; team ; cd fas } ; goals conceded } ; hop { filter_eq { all_rows ; team ; cd arcense } ; goals conceded } } = true | select the rows whose team record fuzzily matches to cd fas . take the goals conceded record of this row . select the rows whose team record fuzzily matches to cd arcense . take the goals conceded 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, 'team_7': 7, 'cd fas_8': 8, 'goals conceded_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'team_11': 11, 'cd arcense_12': 12, 'goals conceded_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', 'team_7': 'team', 'cd fas_8': 'cd fas', 'goals conceded_9': 'goals conceded', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'team_11': 'team', 'cd arcense_12': 'cd arcense', 'goals conceded_13': 'goals conceded'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'team_7': [0], 'cd fas_8': [0], 'goals conceded_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'team_11': [1], 'cd arcense_12': [1], 'goals conceded_13': [3]} | ['place', 'team', 'played', 'draw', 'lost', 'goals scored', 'goals conceded', 'points'] | [['1', 'cd fas', '18', '5', '3', '24', '20', '35'], ['2', 'municipal limeño', '18', '4', '5', '33', '19', '31'], ['3', 'san salvador fc', '18', '7', '4', '28', '21', '28'], ['4', 'cd águila', '18', '9', '3', '26', '20', '27'], ['5', 'cd luis ángel firpo', '18', '6', '5', '23', '24', '27'], ['6', 'ad isidro metapán', '18', '6', '7', '22', '24', '21'], ['7', 'cd arcense', '18', '6', '7', '15', '17', '21'], ['8', 'cd atlético balboa', '18', '5', '9', '21', '31', '17'], ['9', 'alianza fc', '18', '7', '8', '17', '22', '16'], ['10', 'cd dragón', '18', '7', '8', '20', '31', '16']] |
2002 in film | https://en.wikipedia.org/wiki/2002_in_film | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-167180-1.html.csv | superlative | the lord of the rings : the two towers was the highest grossing film of 2002 . | {'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', 'worldwide gross'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; worldwide gross }'}, 'title'], 'result': 'the lord of the rings : the two towers', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; worldwide gross } ; title }'}, 'the lord of the rings : the two towers'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; worldwide gross } ; title } ; the lord of the rings : the two towers } = true', 'tointer': 'select the row whose worldwide gross record of all rows is maximum . the title record of this row is the lord of the rings : the two towers .'} | eq { hop { argmax { all_rows ; worldwide gross } ; title } ; the lord of the rings : the two towers } = true | select the row whose worldwide gross record of all rows is maximum . the title record of this row is the lord of the rings : the two towers . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'worldwide gross_5': 5, 'title_6': 6, 'the lord of the rings : the two towers_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'worldwide gross_5': 'worldwide gross', 'title_6': 'title', 'the lord of the rings : the two towers_7': 'the lord of the rings : the two towers'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'worldwide gross_5': [0], 'title_6': [1], 'the lord of the rings : the two towers_7': [2]} | ['rank', 'title', 'studio', 'director ( s )', 'worldwide gross'] | [['1', 'the lord of the rings : the two towers', 'new line cinema', 'peter jackson', '925282504'], ['2', 'harry potter and the chamber of secrets', 'warner bros', 'chris columbus', '878643482'], ['3', 'spider - man', 'columbia pictures', 'sam raimi', '821708551'], ['4', 'star wars episode ii : attack of the clones', '20th century fox / lucasfilm', 'george lucas', '649398328'], ['5', 'men in black ii', 'columbia pictures', 'barry sonnenfeld', '441818803'], ['6', 'die another day', 'mgm', 'lee tamahori', '431971116'], ['7', 'signs', 'touchstone pictures / blinding edge', 'm night shyamalan', '408247917'], ['8', 'ice age', '20th century fox / blue sky studios', 'chris wedge', '383257136'], ['9', 'my big fat greek wedding', 'ifc films', 'joel zwick', '368744044'], ['10', 'minority report', 'dreamworks / 20th century fox', 'steven spielberg', '358372926'], ['11', 'catch me if you can', 'dreamworks / amblin entertainment', 'steven spielberg', '352114312'], ['12', 'chicago', 'miramax films', 'rob marshall', '306776732'], ['13', 'austin powers in goldmember', 'new line cinema', 'jay roach', '296655431'], ['14', 'xxx', 'columbia pictures', 'rob cohen', '277448382'], ['15', 'scooby - doo', 'warner bros', 'raja gosnell', '275650703'], ['16', 'spirited away', 'studio ghibli / walt disney pictures', 'hayao miyazaki', '274925095'], ['17', 'lilo & stitch', 'walt disney pictures', 'chris sanders and dean deblois', '273144151'], ['18', 'the ring', 'dreamworks', 'gore verbinski', '249348933'], ['19', '8 mile', 'universal studios / imagine entertainment', 'curtis hanson', '242875078'], ['20', 'the bourne identity', 'universal pictures', 'doug liman', '214034224']] |
2007 - 08 san antonio spurs season | https://en.wikipedia.org/wiki/2007%E2%80%9308_San_Antonio_Spurs_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11963601-12.html.csv | comparative | in the 2007-08 san antonio spurs season , duncan had 2 more rebounds on may 27th than on may 29th . | {'row_1': '4', 'row_2': '5', 'col': '6', 'col_other': '2', 'relation': 'diff', 'record_mentioned': 'yes', 'diff_result': {'diff_value': '2', 'bigger': 'row1'}} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'may 27'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to may 27 .', 'tostr': 'filter_eq { all_rows ; date ; may 27 }'}, 'high rebounds'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; may 27 } ; high rebounds }', 'tointer': 'select the rows whose date record fuzzily matches to may 27 . take the high rebounds record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'may 29'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to may 29 .', 'tostr': 'filter_eq { all_rows ; date ; may 29 }'}, 'high rebounds'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; may 29 } ; high rebounds }', 'tointer': 'select the rows whose date record fuzzily matches to may 29 . take the high rebounds record of this row .'}], 'result': '2', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; date ; may 27 } ; high rebounds } ; hop { filter_eq { all_rows ; date ; may 29 } ; high rebounds } }'}, '2'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; date ; may 27 } ; high rebounds } ; hop { filter_eq { all_rows ; date ; may 29 } ; high rebounds } } ; 2 }', 'tointer': 'select the rows whose date record fuzzily matches to may 27 . take the high rebounds record of this row . select the rows whose date record fuzzily matches to may 29 . take the high rebounds record of this row . the first record is 2 larger than the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'may 27'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to may 27 .', 'tostr': 'filter_eq { all_rows ; date ; may 27 }'}, 'high rebounds'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; may 27 } ; high rebounds }', 'tointer': 'select the rows whose date record fuzzily matches to may 27 . take the high rebounds record of this row .'}, 'duncan ( 17 )'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; may 27 } ; high rebounds } ; duncan ( 17 ) }', 'tointer': 'the high rebounds record of the first row is duncan ( 17 ) .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'may 29'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to may 29 .', 'tostr': 'filter_eq { all_rows ; date ; may 29 }'}, 'high rebounds'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; may 29 } ; high rebounds }', 'tointer': 'select the rows whose date record fuzzily matches to may 29 . take the high rebounds record of this row .'}, 'duncan ( 15 )'], 'result': True, 'ind': 7, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; may 29 } ; high rebounds } ; duncan ( 15 ) }', 'tointer': 'the high rebounds record of the second row is duncan ( 15 ) .'}], 'result': True, 'ind': 8, 'tostr': 'and { eq { hop { filter_eq { all_rows ; date ; may 27 } ; high rebounds } ; duncan ( 17 ) } ; eq { hop { filter_eq { all_rows ; date ; may 29 } ; high rebounds } ; duncan ( 15 ) } }', 'tointer': 'the high rebounds record of the first row is duncan ( 17 ) . the high rebounds record of the second row is duncan ( 15 ) .'}], 'result': True, 'ind': 9, 'tostr': 'and { eq { diff { hop { filter_eq { all_rows ; date ; may 27 } ; high rebounds } ; hop { filter_eq { all_rows ; date ; may 29 } ; high rebounds } } ; 2 } ; and { eq { hop { filter_eq { all_rows ; date ; may 27 } ; high rebounds } ; duncan ( 17 ) } ; eq { hop { filter_eq { all_rows ; date ; may 29 } ; high rebounds } ; duncan ( 15 ) } } } = true', 'tointer': 'select the rows whose date record fuzzily matches to may 27 . take the high rebounds record of this row . select the rows whose date record fuzzily matches to may 29 . take the high rebounds record of this row . the first record is 2 larger than the second record . the high rebounds record of the first row is duncan ( 17 ) . the high rebounds record of the second row is duncan ( 15 ) .'} | and { eq { diff { hop { filter_eq { all_rows ; date ; may 27 } ; high rebounds } ; hop { filter_eq { all_rows ; date ; may 29 } ; high rebounds } } ; 2 } ; and { eq { hop { filter_eq { all_rows ; date ; may 27 } ; high rebounds } ; duncan ( 17 ) } ; eq { hop { filter_eq { all_rows ; date ; may 29 } ; high rebounds } ; duncan ( 15 ) } } } = true | select the rows whose date record fuzzily matches to may 27 . take the high rebounds record of this row . select the rows whose date record fuzzily matches to may 29 . take the high rebounds record of this row . the first record is 2 larger than the second record . the high rebounds record of the first row is duncan ( 17 ) . the high rebounds record of the second row is duncan ( 15 ) . | 14 | 10 | {'and_9': 9, 'result_10': 10, 'eq_5': 5, 'diff_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_11': 11, 'date_12': 12, 'may 27_13': 13, 'high rebounds_14': 14, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_15': 15, 'date_16': 16, 'may 29_17': 17, 'high rebounds_18': 18, '2_19': 19, 'and_8': 8, 'str_eq_6': 6, 'duncan (17)_20': 20, 'str_eq_7': 7, 'duncan (15)_21': 21} | {'and_9': 'and', 'result_10': 'true', 'eq_5': 'eq', 'diff_4': 'diff', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_11': 'all_rows', 'date_12': 'date', 'may 27_13': 'may 27', 'high rebounds_14': 'high rebounds', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_15': 'all_rows', 'date_16': 'date', 'may 29_17': 'may 29', 'high rebounds_18': 'high rebounds', '2_19': '2', 'and_8': 'and', 'str_eq_6': 'str_eq', 'duncan (17)_20': 'duncan ( 17 )', 'str_eq_7': 'str_eq', 'duncan (15)_21': 'duncan ( 15 )'} | {'and_9': [10], 'result_10': [], 'eq_5': [9], 'diff_4': [5], 'str_hop_2': [4, 6], 'filter_str_eq_0': [2], 'all_rows_11': [0], 'date_12': [0], 'may 27_13': [0], 'high rebounds_14': [2], 'str_hop_3': [4, 7], 'filter_str_eq_1': [3], 'all_rows_15': [1], 'date_16': [1], 'may 29_17': [1], 'high rebounds_18': [3], '2_19': [5], 'and_8': [9], 'str_eq_6': [8], 'duncan (17)_20': [6], 'str_eq_7': [8], 'duncan (15)_21': [7]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'series'] | [['1', 'may 21', 'los angeles', '85 - 89', 'duncan ( 30 )', 'duncan ( 18 )', 'parker ( 6 )', 'staples center 18997', '0 - 1'], ['2', 'may 23', 'los angeles', '71 - 101', 'parker ( 13 )', 'duncan ( 16 )', 'duncan ( 4 )', 'staples center 18997', '0 - 2'], ['3', 'may 25', 'los angeles', '103 - 84', 'ginóbili ( 30 )', 'duncan ( 21 )', 'duncan , parker ( 5 )', 'at & t center 18797', '1 - 2'], ['4', 'may 27', 'los angeles', '91 - 93', 'duncan ( 29 )', 'duncan ( 17 )', 'parker ( 9 )', 'at & t center 18797', '1 - 3'], ['5', 'may 29', 'los angeles', '92 - 100', 'parker ( 23 )', 'duncan ( 15 )', 'duncan ( 10 )', 'staples center 18997', '1 - 4']] |
the new adventures of old christine ( season 1 ) | https://en.wikipedia.org/wiki/The_New_Adventures_of_Old_Christine_%28season_1%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27910411-1.html.csv | unique | only the episode " long days journey into stan " was written by danielle evenson . | {'scope': 'all', 'row': '7', 'col': '4', 'col_other': '2', 'criterion': 'equal', 'value': 'danielle evenson', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'written by', 'danielle evenson'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose written by record fuzzily matches to danielle evenson .', 'tostr': 'filter_eq { all_rows ; written by ; danielle evenson }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; written by ; danielle evenson } }', 'tointer': 'select the rows whose written by record fuzzily matches to danielle evenson . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'written by', 'danielle evenson'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose written by record fuzzily matches to danielle evenson .', 'tostr': 'filter_eq { all_rows ; written by ; danielle evenson }'}, 'title'], 'result': 'long days journey into stan', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; written by ; danielle evenson } ; title }'}, 'long days journey into stan'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; written by ; danielle evenson } ; title } ; long days journey into stan }', 'tointer': 'the title record of this unqiue row is long days journey into stan .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; written by ; danielle evenson } } ; eq { hop { filter_eq { all_rows ; written by ; danielle evenson } ; title } ; long days journey into stan } } = true', 'tointer': 'select the rows whose written by record fuzzily matches to danielle evenson . there is only one such row in the table . the title record of this unqiue row is long days journey into stan .'} | and { only { filter_eq { all_rows ; written by ; danielle evenson } } ; eq { hop { filter_eq { all_rows ; written by ; danielle evenson } ; title } ; long days journey into stan } } = true | select the rows whose written by record fuzzily matches to danielle evenson . there is only one such row in the table . the title record of this unqiue row is long days journey into stan . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'written by_7': 7, 'danielle evenson_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'title_9': 9, 'long days journey into stan_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'written by_7': 'written by', 'danielle evenson_8': 'danielle evenson', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'title_9': 'title', 'long days journey into stan_10': 'long days journey into stan'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'written by_7': [0], 'danielle evenson_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'title_9': [2], 'long days journey into stan_10': [3]} | ['no in series', 'title', 'directed by', 'written by', 'original air date', 'us viewers ( millions )'] | [['1', 'pilot', 'andy ackerman', 'kari lizer', 'march 13 , 2006', '12.36'], ['2', 'supertramp', 'andy ackerman', 'teleplay : jeff astrof story : kari lizer', 'march 13 , 2006', '15.09'], ['3', 'open water', 'andy ackerman', 'adam barr', 'march 20 , 2006', '15.13'], ['4', 'one toe over the line , sweet jesus', 'andy ackerman', 'adam barr', 'march 27 , 2006', '11.96'], ['5', "i 'll show you mine", 'andy ackerman', 'steve baldikoski & bryan behar', 'april 3 , 2006', '8.28'], ['6', 'the other f word', 'andy ackerman', 'jeff astrof', 'april 10 , 2006', '11.42'], ['7', 'long days journey into stan', 'andy ackerman', 'danielle evenson', 'april 17 , 2006', '11.38'], ['8', 'teach your children well', 'andy ackerman', 'katie palmer', 'april 24 , 2006', '11.81'], ['9', 'ritchie has two mommies', 'andy ackerman', 'kari lizer', 'may 1 , 2006', '11.92'], ['10', 'no fault divorce', 'andy ackerman', 'jeff astrof & adam barr and kari lizer', 'may 8 , 2006', '11.87']] |
lukáš melich | https://en.wikipedia.org/wiki/Luk%C3%A1%C5%A1_Melich | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12582968-1.html.csv | majority | the majority of the time lukas placed 15th or higher . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'less_than_eq', 'value': '15', 'subset': None} | {'func': 'most_less_eq', 'args': ['all_rows', 'position', '15'], 'result': True, 'ind': 0, 'tointer': 'for the position records of all rows , most of them are less than or equal to 15 .', 'tostr': 'most_less_eq { all_rows ; position ; 15 } = true'} | most_less_eq { all_rows ; position ; 15 } = true | for the position records of all rows , most of them are less than or equal to 15 . | 1 | 1 | {'most_less_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'position_3': 3, '15_4': 4} | {'most_less_eq_0': 'most_less_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'position_3': 'position', '15_4': '15'} | {'most_less_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'position_3': [0], '15_4': [0]} | ['year', 'competition', 'venue', 'position', 'notes'] | [['1998', 'world junior championships', 'annecy , france', '10th', '61.51 m'], ['1999', 'european junior championships', 'riga , latvia', '5th', '64.20 m'], ['2001', 'european u23 championships', 'amsterdam , netherlands', '11th', '66.41 m'], ['2003', 'universiade', 'daegu , south korea', '4th', '71.26 m'], ['2005', 'world championships', 'helsinki , finland', '14th', '74.53 m'], ['2006', 'european championships', 'gothenburg , sweden', '15th', '73.77 m'], ['2008', 'olympic games', 'beijing , pr china', '29th', '70.56 m'], ['2009', 'world championships', 'berlin , germany', '14th', '74.47 m'], ['2012', 'olympic games', 'london , great britain', '6th', '77.17 m'], ['2013', 'world championships', 'moscow , russia', '3rd', '79.36 m']] |
baie - comeau drakkar | https://en.wikipedia.org/wiki/Baie-Comeau_Drakkar | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1259985-1.html.csv | aggregation | baie - comeau drakkar played an average of 70 games from 1997 to 2012 per year . | {'scope': 'all', 'col': '2', 'type': 'average', 'result': '70', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'games'], 'result': '70', 'ind': 0, 'tostr': 'avg { all_rows ; games }'}, '70'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; games } ; 70 } = true', 'tointer': 'the average of the games record of all rows is 70 .'} | round_eq { avg { all_rows ; games } ; 70 } = true | the average of the games record of all rows is 70 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'games_4': 4, '70_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'games_4': 'games', '70_5': '70'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'games_4': [0], '70_5': [1]} | ['season', 'games', 'lost', 'points', 'goalsfor', 'goalsagainst'] | [['1997 - 98', '70', '47', '41', '215', '332'], ['1998 - 99', '70', '44', '44', '208', '297'], ['1999 - 2000', '72', '31', '72', '257', '285'], ['2000 - 01', '72', '23', '90', '283', '255'], ['2001 - 02', '72', '25', '85', '288', '231'], ['2002 - 03', '72', '14', '108', '319', '213'], ['2003 - 04', '70', '42', '49', '195', '285'], ['2004 - 05', '70', '37', '57', '208', '280'], ['2005 - 06', '70', '38', '62', '249', '285'], ['2006 - 07', '70', '26', '79', '304', '285'], ['2007 - 08', '70', '19', '96', '249', '210'], ['2008 - 09', '68', '37', '53', '206', '297'], ['2009 - 10', '68', '40', '49', '187', '297'], ['2010 - 11', '68', '46', '34', '151', '266'], ['2011 - 12', '68', '34', '63', '217', '241']] |
con todo | https://en.wikipedia.org/wiki/Con_Todo | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25614153-1.html.csv | ordinal | the second longest song on con todo is solo cristo . | {'row': '11', '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', 'duration', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; duration ; 2 }'}, 'song'], 'result': 'sólo cristo', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; duration ; 2 } ; song }'}, 'sólo cristo'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; duration ; 2 } ; song } ; sólo cristo } = true', 'tointer': 'select the row whose duration record of all rows is 2nd maximum . the song record of this row is sólo cristo .'} | eq { hop { nth_argmax { all_rows ; duration ; 2 } ; song } ; sólo cristo } = true | select the row whose duration record of all rows is 2nd maximum . the song record of this row is sólo cristo . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'duration_5': 5, '2_6': 6, 'song_7': 7, 'sólo cristo_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', 'duration_5': 'duration', '2_6': '2', 'song_7': 'song', 'sólo cristo_8': 'sólo cristo'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'duration_5': [0], '2_6': [0], 'song_7': [1], 'sólo cristo_8': [2]} | ['', 'song', 'english translation', 'original album', 'composer', 'worship leader', 'supporting vocal', 'duration'] | [['1', 'para exaltarte', 'your name high', 'this is our god', 'joel houston', 'joel houston', 'none', '4:02'], ['2', 'correré', 'run', 'this is our god', 'joel houston', 'toni romero', 'none', '3:22'], ['3', 'hosanna', 'hosanna', 'saviour king', 'brooke fraser', 'darlene zschech', 'none', '6:08'], ['4', 'desde mi interior', 'from the inside out', 'unidos permanecemos', 'joel houston', 'jad gillies', 'none', '6:13'], ['5', 'canción del desierto', 'desert song', 'this is our god', 'brooke fraser', 'annie garratt', 'none', '4:16'], ['6', 'en la cruz', 'the cross', 'mighty to save', 'darlene zschech & reuben morgan', 'darlene zschech', 'none', '6:20'], ['7', 'rey salvador', 'saviour king', 'saviour king', 'marty sampson & mia fields', 'dave ware', 'none', '8:02'], ['8', 'poderoso para salvar', 'mighty to save', 'mighty to save', 'reuben morgan & ben fielding', 'reuben morgan', 'darlene zschech', '5:34'], ['9', 'soy libre', 'break free', 'saviour king', 'joel houston , matt crocker & scott ligertwood', 'matt crocker', 'none', '3:59'], ['10', 'poderoso', 'stronger', 'this is our god', 'reuben morgan & ben fielding', 'jad gillies', 'darlene zschech', '4:37'], ['11', 'sólo cristo', 'none but jesus', 'unidos permanecemos', 'brooke fraser', 'brooke fraser', 'none', '7:07'], ['12', 'es nuestro dios', 'this is our god', 'this is our god', 'reuben morgan', 'reuben morgan & darlene zschech', 'none', '6:10'], ['13', 'eres mi fortaleza', 'you are my strength', 'saviour king', 'reuben morgan', 'reuben morgan', 'none', '4:53']] |
economy of south america | https://en.wikipedia.org/wiki/Economy_of_South_America | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1222653-11.html.csv | comparative | one us dollar is worth more argentine pesos than it is brazilian reals . | {'row_1': '1', 'row_2': '3', 'col': '4', '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', 'currency', 'argentine peso ( ars )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose currency record fuzzily matches to argentine peso ( ars ) .', 'tostr': 'filter_eq { all_rows ; currency ; argentine peso ( ars ) }'}, '1 usd ='], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; currency ; argentine peso ( ars ) } ; 1 usd = }', 'tointer': 'select the rows whose currency record fuzzily matches to argentine peso ( ars ) . take the 1 usd = record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'currency', 'brazilian real ( brl )'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose currency record fuzzily matches to brazilian real ( brl ) .', 'tostr': 'filter_eq { all_rows ; currency ; brazilian real ( brl ) }'}, '1 usd ='], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; currency ; brazilian real ( brl ) } ; 1 usd = }', 'tointer': 'select the rows whose currency record fuzzily matches to brazilian real ( brl ) . take the 1 usd = record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; currency ; argentine peso ( ars ) } ; 1 usd = } ; hop { filter_eq { all_rows ; currency ; brazilian real ( brl ) } ; 1 usd = } } = true', 'tointer': 'select the rows whose currency record fuzzily matches to argentine peso ( ars ) . take the 1 usd = record of this row . select the rows whose currency record fuzzily matches to brazilian real ( brl ) . take the 1 usd = record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; currency ; argentine peso ( ars ) } ; 1 usd = } ; hop { filter_eq { all_rows ; currency ; brazilian real ( brl ) } ; 1 usd = } } = true | select the rows whose currency record fuzzily matches to argentine peso ( ars ) . take the 1 usd = record of this row . select the rows whose currency record fuzzily matches to brazilian real ( brl ) . take the 1 usd = 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, 'currency_7': 7, 'argentine peso (ars)_8': 8, '1 usd =_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'currency_11': 11, 'brazilian real (brl)_12': 12, '1 usd =_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', 'currency_7': 'currency', 'argentine peso (ars)_8': 'argentine peso ( ars )', '1 usd =_9': '1 usd =', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'currency_11': 'currency', 'brazilian real (brl)_12': 'brazilian real ( brl )', '1 usd =_13': '1 usd ='} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'currency_7': [0], 'argentine peso (ars)_8': [0], '1 usd =_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'currency_11': [1], 'brazilian real (brl)_12': [1], '1 usd =_13': [3]} | ['country', 'currency', '1 euro =', '1 usd =', 'central bank'] | [['argentina', 'argentine peso ( ars )', '5.65', '4.20', 'central bank of argentina'], ['bolivia', 'bolivian boliviano ( bob )', '11.0985', '7.57080', 'central bank of bolivia'], ['brazil', 'brazilian real ( brl )', '2.58963', '1.76650', 'central bank of brazil'], ['chile', 'chilean peso ( clp )', '701.020', '507.580', 'central bank of chile'], ['colombia', 'colombian peso ( cop )', '2593.20', '1885.74', 'bank of the republic'], ['ecuador', 'us dollar ( usd )', '1.46611', '1', 'federal reserve'], ['guyana', 'guyanese dollar ( gyd )', '297.547', '202.950', 'bank of guyana'], ['paraguay', 'paraguayan guaraní ( pyg )', '6802.74', '4640.00', 'central bank of paraguay'], ['peru', 'peruvian nuevo sol ( pen )', '4.26966', '2.72440', 'central reserve bank of peru'], ['suriname', 'surinamese dollar ( srd )', '4.10543', '2.80000', 'central bank of suriname'], ['uruguay', 'uruguayan peso ( uyu )', '31.1529', '21.2470', 'central bank of uruguay'], ['venezuela', 'venezuelan bolívar fuerte ( vef )', '6.16331', '4.30000', 'central bank of venezuela']] |
platte valley conference | https://en.wikipedia.org/wiki/Platte_Valley_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13971270-1.html.csv | count | among the schools of platte valley conference with enrollment over 70 , 4 of them have football teams . | {'scope': 'subset', 'criterion': 'fuzzily_match', 'value': 'y', 'result': '4', 'col': '6', 'subset': {'col': '5', 'criterion': 'greater_than', 'value': '70'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'school enrollment ( 200810 )', '70'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; school enrollment ( 200810 ) ; 70 }', 'tointer': 'select the rows whose school enrollment ( 200810 ) record is greater than 70 .'}, 'football', 'y'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose school enrollment ( 200810 ) record is greater than 70 . among these rows , select the rows whose football record fuzzily matches to y .', 'tostr': 'filter_eq { filter_greater { all_rows ; school enrollment ( 200810 ) ; 70 } ; football ; y }'}], 'result': '4', 'ind': 2, 'tostr': 'count { filter_eq { filter_greater { all_rows ; school enrollment ( 200810 ) ; 70 } ; football ; y } }', 'tointer': 'select the rows whose school enrollment ( 200810 ) record is greater than 70 . among these rows , select the rows whose football record fuzzily matches to y . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_greater { all_rows ; school enrollment ( 200810 ) ; 70 } ; football ; y } } ; 4 } = true', 'tointer': 'select the rows whose school enrollment ( 200810 ) record is greater than 70 . among these rows , select the rows whose football record fuzzily matches to y . the number of such rows is 4 .'} | eq { count { filter_eq { filter_greater { all_rows ; school enrollment ( 200810 ) ; 70 } ; football ; y } } ; 4 } = true | select the rows whose school enrollment ( 200810 ) record is greater than 70 . among these rows , select the rows whose football record fuzzily matches to y . the number of such rows is 4 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, 'school enrollment (200810)_6': 6, '70_7': 7, 'football_8': 8, 'y_9': 9, '4_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', 'school enrollment (200810)_6': 'school enrollment ( 200810 )', '70_7': '70', 'football_8': 'football', 'y_9': 'y', '4_10': '4'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'school enrollment (200810)_6': [0], '70_7': [0], 'football_8': [1], 'y_9': [1], '4_10': [3]} | ['school', 'team name', 'town', 'county', 'school enrollment ( 200810 )', 'football'] | [['de kalb high school', 'tigers', 'de kalb', 'buchanan', '118', 'y'], ['jefferson conception junction high school', '( lady ) eagles', 'conception junction', 'nodaway', '50', 'n'], ['osborn high school', 'wildcats / lady cats', 'osborn', 'de kalb', '40', 'n'], ['north andrew high school', 'cardinals', 'rosendale', 'andrew', '105', 'y'], ['northeast nodaway high school', 'bluejays', 'ravenwood', 'nodaway', '73', 'n'], ['southwest livingston high school', 'wildcats', 'ludlow', 'livingston', '65', 'y ( football only )'], ['south nodaway high school', 'longhorns', 'barnard', 'nodaway', '71', 'y'], ['stewartsville high school', 'cardinals', 'stewartsville', 'de kalb', '88', 'y'], ['union star high school', 'trojans', 'union star', 'de kalb', '47', 'y']] |
1971 vfl season | https://en.wikipedia.org/wiki/1971_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10826072-22.html.csv | comparative | fitzroy had more away team points than north melbourne did . | {'row_1': '3', 'row_2': '5', 'col': '4', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'away team', 'fitzroy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose away team record fuzzily matches to fitzroy .', 'tostr': 'filter_eq { all_rows ; away team ; fitzroy }'}, 'away team score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; away team ; fitzroy } ; away team score }', 'tointer': 'select the rows whose away team record fuzzily matches to fitzroy . take the away team score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'away team', 'north melbourne'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose away team record fuzzily matches to north melbourne .', 'tostr': 'filter_eq { all_rows ; away team ; north melbourne }'}, 'away team score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; away team ; north melbourne } ; away team score }', 'tointer': 'select the rows whose away team record fuzzily matches to north melbourne . take the away team score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; away team ; fitzroy } ; away team score } ; hop { filter_eq { all_rows ; away team ; north melbourne } ; away team score } } = true', 'tointer': 'select the rows whose away team record fuzzily matches to fitzroy . take the away team score record of this row . select the rows whose away team record fuzzily matches to north melbourne . take the away team score record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; away team ; fitzroy } ; away team score } ; hop { filter_eq { all_rows ; away team ; north melbourne } ; away team score } } = true | select the rows whose away team record fuzzily matches to fitzroy . take the away team score record of this row . select the rows whose away team record fuzzily matches to north melbourne . take the away team 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, 'away team_7': 7, 'fitzroy_8': 8, 'away team score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'away team_11': 11, 'north melbourne_12': 12, 'away team 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', 'away team_7': 'away team', 'fitzroy_8': 'fitzroy', 'away team score_9': 'away team score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'away team_11': 'away team', 'north melbourne_12': 'north melbourne', 'away team score_13': 'away team score'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'away team_7': [0], 'fitzroy_8': [0], 'away team score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'away team_11': [1], 'north melbourne_12': [1], 'away team score_13': [3]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['hawthorn', '18.16 ( 124 )', 'melbourne', '8.17 ( 65 )', 'glenferrie oval', '14809', '28 august 1971'], ['footscray', '10.14 ( 74 )', 'st kilda', '12.18 ( 90 )', 'western oval', '16707', '28 august 1971'], ['essendon', '12.12 ( 84 )', 'fitzroy', '13.17 ( 95 )', 'windy hill', '12865', '28 august 1971'], ['carlton', '16.10 ( 106 )', 'collingwood', '13.9 ( 87 )', 'princes park', '32000', '28 august 1971'], ['south melbourne', '19.17 ( 131 )', 'north melbourne', '8.11 ( 59 )', 'lake oval', '9307', '28 august 1971'], ['richmond', '16.14 ( 110 )', 'geelong', '14.18 ( 102 )', 'mcg', '36423', '28 august 1971']] |
35th united states congress | https://en.wikipedia.org/wiki/35th_United_States_Congress | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1802760-3.html.csv | ordinal | in the 35th united states congress , the 2nd to last successors ' formal installation was when the successor was james chesnut jr . | {'row': '9', 'col': '5', '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', 'date of successors formal installation', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; date of successors formal installation ; 2 }'}, 'successor'], 'result': 'james chesnut , jr ( d )', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; date of successors formal installation ; 2 } ; successor }'}, 'james chesnut , jr ( d )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; date of successors formal installation ; 2 } ; successor } ; james chesnut , jr ( d ) } = true', 'tointer': 'select the row whose date of successors formal installation record of all rows is 2nd maximum . the successor record of this row is james chesnut , jr ( d ) .'} | eq { hop { nth_argmax { all_rows ; date of successors formal installation ; 2 } ; successor } ; james chesnut , jr ( d ) } = true | select the row whose date of successors formal installation record of all rows is 2nd maximum . the successor record of this row is james chesnut , jr ( d ) . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'date of successors formal installation_5': 5, '2_6': 6, 'successor_7': 7, 'james chesnut , jr ( d )_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', 'date of successors formal installation_5': 'date of successors formal installation', '2_6': '2', 'successor_7': 'successor', 'james chesnut , jr ( d )_8': 'james chesnut , jr ( d )'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'date of successors formal installation_5': [0], '2_6': [0], 'successor_7': [1], 'james chesnut , jr ( d )_8': [2]} | ['state ( class )', 'vacator', 'reason for change', 'successor', 'date of successors formal installation'] | [['tennessee ( 1 )', 'vacant', 'vacancy in term', 'andrew johnson ( d )', 'october 8 , 1857'], ['south carolina ( 3 )', 'andrew butler ( d )', 'died may 25 , 1857', 'james h hammond ( d )', 'december 7 , 1857'], ['new hampshire ( 3 )', 'james bell ( r )', 'died may 26 , 1857', 'daniel clark ( r )', 'june 27 , 1857'], ['texas ( 1 )', 'thomas j rusk ( d )', 'died july 29 , 1857', 'j pinckney henderson ( d )', 'november 9 , 1857'], ['south carolina ( 2 )', 'josiah j evans ( d )', 'died may 6 , 1858', 'arthur p hayne ( d )', 'may 11 , 1858'], ['minnesota ( 1 )', 'new seat', 'minnesota admitted to the union may 11 , 1858', 'henry m rice ( d )', 'may 11 , 1858'], ['minnesota ( 2 )', 'new seat', 'minnesota admitted to the union may 11 , 1858', 'james shields ( d )', 'may 11 , 1858'], ['texas ( 1 )', 'j pinckney henderson ( d )', 'died june 4 , 1858', 'matthias ward ( d )', 'september 27 , 1858'], ['south carolina ( 2 )', 'arthur p hayne ( d )', 'successor elected december 2 , 1858', 'james chesnut , jr ( d )', 'december 3 , 1858'], ['oregon ( 2 )', 'new seat', 'oregon admitted to the union february 14 , 1859', 'delazon smith ( d )', 'february 14 , 1859']] |
1956 syracuse orangemen football team | https://en.wikipedia.org/wiki/1956_Syracuse_Orangemen_football_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23346983-1.html.csv | aggregation | for the 1956 syracuse orangemen football team , the average number of points for the orangemen was 20.3 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '20.3', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'orangemen points'], 'result': '20.3', 'ind': 0, 'tostr': 'avg { all_rows ; orangemen points }'}, '20.3'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; orangemen points } ; 20.3 } = true', 'tointer': 'the average of the orangemen points record of all rows is 20.3 .'} | round_eq { avg { all_rows ; orangemen points } ; 20.3 } = true | the average of the orangemen points record of all rows is 20.3 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'orangemen points_4': 4, '20.3_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'orangemen points_4': 'orangemen points', '20.3_5': '20.3'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'orangemen points_4': [0], '20.3_5': [1]} | ['game', 'date', 'opponent', 'result', 'orangemen points', 'opponents', 'record'] | [['1', 'sept 22', 'maryland', 'win', '26', '12', '1 - 0'], ['2', 'sept 29', 'pittsburgh', 'loss', '7', '14', '1 - 1'], ['3', 'oct 13', 'west virginia', 'win', '27', '20', '2 - 1'], ['4', 'oct 20', 'army', 'win', '7', '0', '3 - 1'], ['5', 'oct 27', 'boston university', 'win', '21', '7', '4 - 1'], ['6', 'nov 3', 'penn state', 'win', '13', '9', '5 - 1'], ['7', 'nov 10', 'holy cross', 'win', '41', '20', '6 - 1']] |
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/2-17311759-8.html.csv | majority | joe johnson had the majority of high assists performances in the 2008 - 09 atlanta hawks season . | {'scope': 'all', 'col': '7', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'joe johnson', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'high assists', 'joe johnson'], 'result': True, 'ind': 0, 'tointer': 'for the high assists records of all rows , most of them fuzzily match to joe johnson .', 'tostr': 'most_eq { all_rows ; high assists ; joe johnson } = true'} | most_eq { all_rows ; high assists ; joe johnson } = true | for the high assists records of all rows , most of them fuzzily match to joe johnson . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'high assists_3': 3, 'joe johnson_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'high assists_3': 'high assists', 'joe johnson_4': 'joe johnson'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'high assists_3': [0], 'joe johnson_4': [0]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['59', 'march 1', 'cleveland', 'l 87 - 88 ( ot )', 'joe johnson ( 21 )', 'marvin williams , al horford ( 10 )', 'joe johnson ( 4 )', 'philips arena 19639', '33 - 26'], ['60', 'march 2', 'washington', 'w 98 - 89 ( ot )', 'marvin williams ( 28 )', 'al horford ( 8 )', 'joe johnson ( 13 )', 'verizon center 10189', '34 - 26'], ['61', 'march 4', 'new york', 'l 105 - 109 ( ot )', 'al horford ( 20 )', 'al horford ( 13 )', 'joe johnson ( 6 )', 'madison square garden 18931', '34 - 27'], ['62', 'march 6', 'charlotte', 'l 91 - 98 ( ot )', 'al horford ( 15 )', 'marvin williams ( 8 )', 'mike bibby ( 6 )', 'time warner cable arena 15058', '34 - 28'], ['63', 'march 7', 'detroit', 'w 87 - 83 ( ot )', 'josh smith ( 19 )', 'josh smith , al horford ( 12 )', 'joe johnson ( 6 )', 'philips arena 19101', '35 - 28'], ['64', 'march 9', 'new orleans', 'w 89 - 79 ( ot )', 'joe johnson ( 30 )', 'josh smith ( 13 )', 'mike bibby , josh smith , acie law ( 3 )', 'philips arena 14204', '36 - 28'], ['65', 'march 11', 'utah', 'w 100 - 93 ( ot )', 'joe johnson ( 31 )', 'al horford , josh smith ( 12 )', 'joe johnson ( 9 )', 'philips arena 13112', '37 - 28'], ['66', 'march 13', 'indiana', 'w 101 - 87 ( ot )', 'joe johnson ( 30 )', 'al horford ( 15 )', 'joe johnson , mike bibby ( 6 )', 'philips arena 14079', '38 - 28'], ['67', 'march 15', 'portland', 'w 98 - 80 ( ot )', 'joe johnson ( 35 )', 'josh smith ( 8 )', 'joe johnson ( 6 )', 'philips arena 14413', '39 - 28'], ['68', 'march 17', 'sacramento', 'w 119 - 97 ( ot )', 'al horford ( 23 )', 'al horford ( 12 )', 'mike bibby ( 7 )', 'philips arena 14226', '40 - 28'], ['69', 'march 19', 'dallas', 'w 95 - 87 ( ot )', 'joe johnson ( 24 )', 'josh smith ( 9 )', 'mike bibby ( 7 )', 'philips arena 17499', '41 - 28'], ['70', 'march 21', 'cleveland', 'l 96 - 102 ( ot )', 'joe johnson ( 24 )', 'al horford ( 11 )', 'al horford ( 6 )', 'quicken loans arena 20562', '41 - 29'], ['71', 'march 23', 'minnesota', 'w 109 - 97 ( ot )', 'ronald murray ( 30 )', 'al horford ( 13 )', 'mike bibby ( 9 )', 'philips arena 13425', '42 - 29'], ['72', 'march 25', 'san antonio', 'l 92 - 102 ( ot )', 'joe johnson ( 30 )', 'al horford ( 13 )', 'josh smith ( 5 )', 'philips arena 18529', '42 - 30'], ['73', 'march 27', 'boston', 'l 93 - 99 ( ot )', 'joe johnson , josh smith ( 22 )', 'al horford ( 14 )', 'joe johnson , josh smith ( 4 )', 'philips arena 20054', '42 - 31'], ['74', 'march 29', 'la lakers', 'w 86 - 76 ( ot )', 'mike bibby ( 21 )', 'zaza pachulia ( 13 )', 'joe johnson ( 8 )', 'philips arena 20148', '43 - 31'], ['75', 'march 31', 'philadelphia', 'l 85 - 98 ( ot )', 'josh smith ( 33 )', 'zaza pachulia , al horford ( 8 )', 'joe johnson ( 7 )', 'wachovia center 18256', '43 - 32']] |
1997 atp super 9 | https://en.wikipedia.org/wiki/1997_ATP_Super_9 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16381401-1.html.csv | count | a total of two tennis tournaments in the 1997 atp super 9 were played on a carpet surface . | {'scope': 'all', 'criterion': 'equal', 'value': 'carpet ( i )', 'result': '2', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'carpet ( i )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to carpet ( i ) .', 'tostr': 'filter_eq { all_rows ; surface ; carpet ( i ) }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; surface ; carpet ( i ) } }', 'tointer': 'select the rows whose surface record fuzzily matches to carpet ( i ) . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; surface ; carpet ( i ) } } ; 2 } = true', 'tointer': 'select the rows whose surface record fuzzily matches to carpet ( i ) . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; surface ; carpet ( i ) } } ; 2 } = true | select the rows whose surface record fuzzily matches to carpet ( i ) . 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, 'surface_5': 5, 'carpet (i)_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', 'surface_5': 'surface', 'carpet (i)_6': 'carpet ( i )', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'surface_5': [0], 'carpet (i)_6': [0], '2_7': [2]} | ['tournament', 'surface', 'week', 'winner and score', 'finalist', 'semifinalists'] | [['indian wells', 'hard', 'march 10', 'michael chang 4 - 6 , 6 - 3 , 6 - 4 , 6 - 3', 'bohdan ulihrach', 'jonas björkman thomas muster'], ['key biscane', 'hard', 'march 17', 'thomas muster 7 - 6 ( 6 ) , 6 - 3 , 6 - 1', 'sergi bruguera', 'pete sampras jim courier'], ['monte carlo', 'clay', 'april 21', 'marcelo ríos 6 - 4 , 6 - 3 , 6 - 3', 'àlex corretja', 'carlos moyá fabrice santoro'], ['hamburg', 'clay', 'may 5', 'andriy medvedev 6 - 0 , 6 - 4 , 6 - 2', 'félix mantilla', 'tommy haas yevgeny kafelnikov'], ['rome', 'clay', 'may 12', 'àlex corretja 7 - 5 , 7 - 5 , 6 - 3', 'marcelo ríos', 'alberto berasategui goran ivanišević'], ['montréal', 'hard', 'july 28', 'chris woodruff 7 - 5 , 4 - 6 , 6 - 3', 'gustavo kuerten', 'michael chang yevgeny kafelnikov'], ['cincinnati', 'hard', 'august 4', 'pete sampras 6 - 3 , 6 - 4', 'thomas muster', 'albert costa michael chang'], ['stuttgart', 'carpet ( i )', 'october 20', 'petr korda 7 - 6 ( 6 ) , 6 - 2 , 6 - 4', 'richard krajicek', 'jonas björkman patrick rafter'], ['paris', 'carpet ( i )', 'october 27', 'pete sampras 6 - 3 , 4 - 6 , 6 - 3 , 6 - 1', 'jonas björkman', 'yevgeny kafelnikov thomas enqvist'], ['hannover ( awc )', 'hard ( i )', 'november 10', 'pete sampras 6 - 3 , 6 - 2 , 6 - 2', 'yevgeny kafelnikov', 'carlos moyá jonas björkman']] |
tony kanaan | https://en.wikipedia.org/wiki/Tony_Kanaan | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1615758-3.html.csv | comparative | tony kanaan earned a higher rank in 2004 than he did in 2006 . | {'row_1': '3', 'row_2': '5', 'col': '5', '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', 'year', '2004'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 2004 .', 'tostr': 'filter_eq { all_rows ; year ; 2004 }'}, 'rank'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 2004 } ; rank }', 'tointer': 'select the rows whose year record fuzzily matches to 2004 . take the rank record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '2006'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 2006 .', 'tostr': 'filter_eq { all_rows ; year ; 2006 }'}, 'rank'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 2006 } ; rank }', 'tointer': 'select the rows whose year record fuzzily matches to 2006 . take the rank record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; year ; 2004 } ; rank } ; hop { filter_eq { all_rows ; year ; 2006 } ; rank } } = true', 'tointer': 'select the rows whose year record fuzzily matches to 2004 . take the rank record of this row . select the rows whose year record fuzzily matches to 2006 . take the rank record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; year ; 2004 } ; rank } ; hop { filter_eq { all_rows ; year ; 2006 } ; rank } } = true | select the rows whose year record fuzzily matches to 2004 . take the rank record of this row . select the rows whose year record fuzzily matches to 2006 . take the rank 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, 'year_7': 7, '2004_8': 8, 'rank_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'year_11': 11, '2006_12': 12, 'rank_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', 'year_7': 'year', '2004_8': '2004', 'rank_9': 'rank', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'year_11': 'year', '2006_12': '2006', 'rank_13': 'rank'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'year_7': [0], '2004_8': [0], 'rank_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'year_11': [1], '2006_12': [1], 'rank_13': [3]} | ['year', 'team', 'chassis', 'engine', 'rank', 'points'] | [['2002', 'mo nunn racing', 'g - force', 'chevrolet', '50th', '2'], ['2003', 'andretti green racing', 'dallara', 'honda', '4th', '476'], ['2004', 'andretti green racing', 'dallara', 'honda', '1st', '618'], ['2005', 'andretti green racing', 'dallara', 'honda', '2nd', '548'], ['2006', 'andretti green racing', 'dallara', 'honda', '6th', '384'], ['2007', 'andretti green racing', 'dallara', 'honda', '3rd', '576'], ['2008', 'andretti green racing', 'dallara', 'honda', '3rd', '513'], ['2009', 'andretti green racing', 'dallara', 'honda', '6th', '386'], ['2010', 'andretti autosport', 'dallara', 'honda', '6th', '453'], ['2011', 'kv racing technology', 'dallara', 'honda', '5th', '366'], ['2012', 'kv racing technology', 'dallara dw12', 'chevrolet', '9th', '351'], ['2013', 'kv racing technology', 'dallara dw12', 'chevrolet', '11th', '397']] |
duffy waldorf | https://en.wikipedia.org/wiki/Duffy_Waldorf | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1781343-3.html.csv | majority | duffy waldorf finished in the top 10 most tournaments he played . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '0', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'top - 10', '0'], 'result': True, 'ind': 0, 'tointer': 'for the top - 10 records of all rows , most of them are greater than 0 .', 'tostr': 'most_greater { all_rows ; top - 10 ; 0 } = true'} | most_greater { all_rows ; top - 10 ; 0 } = true | for the top - 10 records of all rows , most of them are greater than 0 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'top - 10_3': 3, '0_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'top - 10_3': 'top - 10', '0_4': '0'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'top - 10_3': [0], '0_4': [0]} | ['tournament', 'wins', 'top - 5', 'top - 10', 'top - 25', 'events', 'cuts made'] | [['masters tournament', '0', '1', '1', '2', '6', '5'], ['us open', '0', '0', '1', '2', '13', '7'], ['the open championship', '0', '0', '0', '1', '8', '7'], ['pga championship', '0', '0', '1', '2', '12', '7'], ['totals', '0', '1', '3', '7', '39', '26']] |
united states house of representatives elections , 1964 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1964 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341865-37.html.csv | ordinal | in the united states house of representatives elections , 1964 the result of being re - elected that happened most recently had the incumbent donald d clancy . | {'scope': 'subset', 'row': '2', 'col': '4', 'order': '1', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'subset': {'col': '5', 'criterion': 'equal', 'value': 're-elected'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 're-elected'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; result ; re-elected }', 'tointer': 'select the rows whose result record fuzzily matches to re-elected .'}, 'first elected', '1'], 'result': None, 'ind': 1, 'tostr': 'nth_argmax { filter_eq { all_rows ; result ; re-elected } ; first elected ; 1 }'}, 'incumbent'], 'result': 'donald d clancy', 'ind': 2, 'tostr': 'hop { nth_argmax { filter_eq { all_rows ; result ; re-elected } ; first elected ; 1 } ; incumbent }'}, 'donald d clancy'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmax { filter_eq { all_rows ; result ; re-elected } ; first elected ; 1 } ; incumbent } ; donald d clancy } = true', 'tointer': 'select the rows whose result record fuzzily matches to re-elected . select the row whose first elected record of these rows is 1st maximum . the incumbent record of this row is donald d clancy .'} | eq { hop { nth_argmax { filter_eq { all_rows ; result ; re-elected } ; first elected ; 1 } ; incumbent } ; donald d clancy } = true | select the rows whose result record fuzzily matches to re-elected . select the row whose first elected record of these rows is 1st maximum . the incumbent record of this row is donald d clancy . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmax_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'result_6': 6, 're-elected_7': 7, 'first elected_8': 8, '1_9': 9, 'incumbent_10': 10, 'donald d clancy_11': 11} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmax_1': 'nth_argmax', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'result_6': 'result', 're-elected_7': 're-elected', 'first elected_8': 'first elected', '1_9': '1', 'incumbent_10': 'incumbent', 'donald d clancy_11': 'donald d clancy'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmax_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'result_6': [0], 're-elected_7': [0], 'first elected_8': [1], '1_9': [1], 'incumbent_10': [2], 'donald d clancy_11': [3]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['ohio 1', 'carl w rich', 'republican', '1962', 'lost re - election democratic gain', 'john j gilligan ( d ) 51.9 % carl w rich ( r ) 48.1 %'], ['ohio 2', 'donald d clancy', 'republican', '1960', 're - elected', 'donald d clancy ( r ) 60.5 % h a sand ( d ) 39.5 %'], ['ohio 3', 'paul f schenck', 'republican', '1951', 'lost re - election democratic gain', 'rodney m love ( d ) 52.0 % paul f schenck ( r ) 48.0 %'], ['ohio 5', 'del latta', 'republican', '1958', 're - elected', 'del latta ( r ) 65.9 % milford landis ( d ) 34.1 %'], ['ohio 6', 'bill harsha', 'republican', '1960', 're - elected', 'bill harsha ( r ) 60.1 % frank e smith ( d ) 39.9 %'], ['ohio 16', 'frank t bow', 'republican', '1950', 're - elected', 'frank t bow ( r ) 52.2 % robert d freeman ( d ) 47.8 %'], ['ohio 19', 'michael j kirwan', 'democratic', '1936', 're - elected', 'michael j kirwan ( d ) 76.3 % albert james ( r ) 23.7 %']] |
list of world records in canoeing | https://en.wikipedia.org/wiki/List_of_world_records_in_canoeing | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14884844-2.html.csv | majority | the majority of records bob the list of world records in canoeing were set after 2000 . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '2000', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'year', '2000'], 'result': True, 'ind': 0, 'tointer': 'for the year records of all rows , most of them are greater than 2000 .', 'tostr': 'most_greater { all_rows ; year ; 2000 } = true'} | most_greater { all_rows ; year ; 2000 } = true | for the year records of all rows , most of them are greater than 2000 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'year_3': 3, '2000_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'year_3': 'year', '2000_4': '2000'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'year_3': [0], '2000_4': [0]} | ['distance', 'event', 'record', 'athletes', 'nationality', 'year', 'location'] | [['200 m', 'k1', '38.970 s', 'birgit fischer', 'germany', '1994', 'milano , italy'], ['200 m', 'k2', '36.320 s', 'fanny fischer , nicole reinhardt', 'germany', '2007', 'gerardmer , france'], ['500 m', 'k1', '1:46.906 s', 'bridgitte hartley', 'south africa', '2011', 'szeged , hungary'], ['500 m', 'k2', '1:37.071 s', 'yvonne schuring , viktoria schwarz', 'austria', '2011', 'szeged , hungary'], ['1000 m', 'k1', '3:52.983 s', 'elzbieta urbanczik', 'poland', '2001', 'sevilla , spain']] |
1911 michigan wolverines football team | https://en.wikipedia.org/wiki/1911_Michigan_Wolverines_football_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25730123-2.html.csv | comparative | for the 1911 michigan wolverines frederick l conklin scored more touchdowns than jimmy craig . | {'row_1': '2', 'row_2': '4', 'col': '2', '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', 'player', 'frederick l conklin'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to frederick l conklin .', 'tostr': 'filter_eq { all_rows ; player ; frederick l conklin }'}, 'touchdowns'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; frederick l conklin } ; touchdowns }', 'tointer': 'select the rows whose player record fuzzily matches to frederick l conklin . take the touchdowns record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'jimmy craig'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to jimmy craig .', 'tostr': 'filter_eq { all_rows ; player ; jimmy craig }'}, 'touchdowns'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; jimmy craig } ; touchdowns }', 'tointer': 'select the rows whose player record fuzzily matches to jimmy craig . take the touchdowns record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; player ; frederick l conklin } ; touchdowns } ; hop { filter_eq { all_rows ; player ; jimmy craig } ; touchdowns } } = true', 'tointer': 'select the rows whose player record fuzzily matches to frederick l conklin . take the touchdowns record of this row . select the rows whose player record fuzzily matches to jimmy craig . take the touchdowns record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; player ; frederick l conklin } ; touchdowns } ; hop { filter_eq { all_rows ; player ; jimmy craig } ; touchdowns } } = true | select the rows whose player record fuzzily matches to frederick l conklin . take the touchdowns record of this row . select the rows whose player record fuzzily matches to jimmy craig . take the touchdowns 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, 'player_7': 7, 'frederick l conklin_8': 8, 'touchdowns_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'jimmy craig_12': 12, 'touchdowns_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', 'player_7': 'player', 'frederick l conklin_8': 'frederick l conklin', 'touchdowns_9': 'touchdowns', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'jimmy craig_12': 'jimmy craig', 'touchdowns_13': 'touchdowns'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'frederick l conklin_8': [0], 'touchdowns_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'jimmy craig_12': [1], 'touchdowns_13': [3]} | ['player', 'touchdowns', 'extra points', 'field goals', 'points'] | [['george c thomson', '7', '0', '0', '35'], ['frederick l conklin', '2', '10', '2', '26'], ['stanfield wells', '4', '0', '0', '20'], ['jimmy craig', '1', '0', '0', '5'], ['thomas a bogle , jr', '0', '1', '1', '4']] |
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 | majority | most games of the amsterdam admirals ' in the 2004 season were played in the month of may . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'may', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'date', 'may'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , most of them fuzzily match to may .', 'tostr': 'most_eq { all_rows ; date ; may } = true'} | most_eq { all_rows ; date ; may } = true | for the date records of all rows , most of them fuzzily match to may . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, 'may_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', 'may_4': 'may'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], 'may_4': [0]} | ['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']] |
list of pokémon theme songs | https://en.wikipedia.org/wiki/List_of_Pok%C3%A9mon_theme_songs | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2144389-8.html.csv | count | grin was a vocalist on a total of 3 theme songs . | {'scope': 'all', 'criterion': 'equal', 'value': 'grin', 'result': '3', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'vocalist', 'grin'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose vocalist record fuzzily matches to grin .', 'tostr': 'filter_eq { all_rows ; vocalist ; grin }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; vocalist ; grin } }', 'tointer': 'select the rows whose vocalist record fuzzily matches to grin . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; vocalist ; grin } } ; 3 } = true', 'tointer': 'select the rows whose vocalist record fuzzily matches to grin . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; vocalist ; grin } } ; 3 } = true | select the rows whose vocalist record fuzzily matches to grin . 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, 'vocalist_5': 5, 'grin_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', 'vocalist_5': 'vocalist', 'grin_6': 'grin', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'vocalist_5': [0], 'grin_6': [0], '3_7': [2]} | ['', 'japanese title', 'rōmaji', 'japanese translation', 'vocalist', 'episodes used'] | [['1', '君のそばで ～ ヒカリのテーマ ～', 'kimi no soba de ~ hikari no tēma ~', "by your side ~ hikari 's theme ~", 'grin', 'dp001 - dp024'], ['2', '君のそばで ～ ヒカリのテーマ ～ ( popupversion )', 'kimi no soba de ~ hikari no tēma ~ ( popupversion )', "by your side ~ hikari 's theme ~ ( popupversion )", 'grin', 'dp025 - dp050'], ['3', '君のそばで ～ ヒカリのテーマ ～ ( winter version )', 'kimi no soba de ~ hikari no tēma ~ ( winter version )', "by your side ~ hikari 's theme ~ ( winter version )", 'grin', 'dp051 - dp061'], ['4', '風のメッセージ', 'kaze no messēji', 'message of the wind', 'mai mizuhashi', 'dp062 - dp072 dp084 - dp095'], ['5', '風のメッセージ ( pokapoka - version )', 'kaze no messēji ( pokapoka - version )', 'message of the wind ( pokapoka - version )', 'mai mizuhashi', 'dp073 - dp083'], ['6', 'あしたはきっと', 'ashita wa kitto', 'surely tomorrow', 'kanako', 'dp096 - dp120'], ['7', 'もえよギザみみピチュー !', 'moe yo giza mimi pichū !', 'get fired up , spiky - eared pichu !', 'shoko nakagawa', 'dp121 - dp145'], ['8', 'ドッチ ～ ニョ', 'dotchi ~ nyo', 'which one ~ is it', 'moomoo milk and araki - san', 'dp146 - dp182']] |
luster , norway | https://en.wikipedia.org/wiki/Luster%2C_Norway | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-178398-1.html.csv | ordinal | dale kyrkje is the second oldest church that can be found in luster . | {'row': '10', 'col': '4', 'order': '2', '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', 'year built', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; year built ; 2 }'}, 'church name'], 'result': 'dale kyrkje', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; year built ; 2 } ; church name }'}, 'dale kyrkje'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; year built ; 2 } ; church name } ; dale kyrkje } = true', 'tointer': 'select the row whose year built record of all rows is 2nd minimum . the church name record of this row is dale kyrkje .'} | eq { hop { nth_argmin { all_rows ; year built ; 2 } ; church name } ; dale kyrkje } = true | select the row whose year built record of all rows is 2nd minimum . the church name record of this row is dale kyrkje . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'year built_5': 5, '2_6': 6, 'church name_7': 7, 'dale kyrkje_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', 'year built_5': 'year built', '2_6': '2', 'church name_7': 'church name', 'dale kyrkje_8': 'dale kyrkje'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'year built_5': [0], '2_6': [0], 'church name_7': [1], 'dale kyrkje_8': [2]} | ['parish ( prestegjeld )', 'sub - parish ( sogn )', 'church name', 'year built', 'location of the church'] | [['hafslo parish', 'hafslo', 'hafslo kyrkje', '1878', 'hafslo'], ['hafslo parish', 'hafslo', 'veitastrond kapell', '1928', 'veitastrond'], ['hafslo parish', 'solvorn', 'solvorn kyrkje', '1883', 'solvorn'], ['hafslo parish', 'solvorn', 'urnes stavkyrkje', '1130', 'urnes'], ['jostedal parish', 'fet og joranger', 'fet kyrkje', '1894', 'fet'], ['jostedal parish', 'fet og joranger', 'joranger kyrkje', '1660', 'joranger'], ['jostedal parish', 'gaupne', 'gaupne kyrkje', '1908', 'gaupne'], ['jostedal parish', 'gaupne', 'gaupne gamle kyrkje', '1647', 'gaupne'], ['jostedal parish', 'jostedal', 'jostedal kyrkje', '1660', 'jostedal'], ['luster parish', 'dale', 'dale kyrkje', '1250', 'luster'], ['luster parish', 'fortun', 'fortun kyrkje', '1879', 'fortun'], ['luster parish', 'nes', 'nes kyrkje', '1909', 'nes']] |
2004 nba expansion draft | https://en.wikipedia.org/wiki/2004_NBA_Expansion_Draft | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15623086-3.html.csv | count | a total of 5 players from the 2004 expansion draft spent exactly 3 years in the nba . | {'scope': 'all', 'criterion': 'equal', 'value': '3', 'result': '5', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'nba years', '3'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nba years record is equal to 3 .', 'tostr': 'filter_eq { all_rows ; nba years ; 3 }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; nba years ; 3 } }', 'tointer': 'select the rows whose nba years record is equal to 3 . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; nba years ; 3 } } ; 5 } = true', 'tointer': 'select the rows whose nba years record is equal to 3 . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; nba years ; 3 } } ; 5 } = true | select the rows whose nba years record is equal to 3 . the number of such rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'nba years_5': 5, '3_6': 6, '5_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'nba years_5': 'nba years', '3_6': '3', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'nba years_5': [0], '3_6': [0], '5_7': [2]} | ['pos', 'nationality', 'previous team', 'nba years', 'career with the franchise'] | [['f', 'united states', 'washington wizards', '2', '2006'], ['g', 'bosnia and herzegovina', 'golden state warriors', '2', '-'], ['c', 'slovenia', 'indiana pacers', '3', '2004 - 2007'], ['g', 'united states', 'new orleans hornets', '1', '-'], ['c', 'montenegro', 'los angeles clippers', '3', '-'], ['g / f', 'united states', 'portland trail blazers', '1', '-'], ['f', 'united states', 'chicago bulls', '4', '-'], ['g', 'united states', 'seattle supersonics', '1', '-'], ['f', 'united states', 'boston celtics', '1', '-'], ['f', 'united states', 'cleveland cavaliers', '1', '2004 - 2005'], ['c', 'georgia', 'orlando magic', '1', '-'], ['g / f', 'serbia', 'utah jazz', '1', '-'], ['f / c', 'united states', 'los angeles lakers', '2', '2004 - 2005'], ['g', 'united states', 'new jersey nets', '2', '2004 - 2005'], ['f', 'united states', 'memphis grizzlies', '1', '2004 - 2005'], ['g', 'united states', 'denver nuggets', '3', '-'], ['f', 'united states', 'sacramento kings', '3', '2004 - 2011'], ['f / c', 'united states', 'phoenix suns', '6', '2004 - 2005'], ['f / c', 'united states', 'miami heat', '3', '-']] |
florida collegiate summer league | https://en.wikipedia.org/wiki/Florida_Collegiate_Summer_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18373863-2.html.csv | unique | only one player went lower than 6th in the draft . | {'scope': 'all', 'row': '1', 'col': '5', 'col_other': 'n/a', 'criterion': 'greater_than', 'value': '6', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'round', '6'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose round record is greater than 6 .', 'tostr': 'filter_greater { all_rows ; round ; 6 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; round ; 6 } } = true', 'tointer': 'select the rows whose round record is greater than 6 . there is only one such row in the table .'} | only { filter_greater { all_rows ; round ; 6 } } = true | select the rows whose round record is greater than 6 . there is only one such row in the table . | 2 | 2 | {'only_1': 1, 'result_2': 2, 'filter_greater_0': 0, 'all_rows_3': 3, 'round_4': 4, '6_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_greater_0': 'filter_greater', 'all_rows_3': 'all_rows', 'round_4': 'round', '6_5': '6'} | {'only_1': [2], 'result_2': [], 'filter_greater_0': [1], 'all_rows_3': [0], 'round_4': [0], '6_5': [0]} | ['player', 'fcsl team', 'years played', 'year drafted', 'round', 'mlb team'] | [['mike mcclendon', 'winter park', '2006', '2006', '10th', 'milwaukee brewers'], ['corey brown', 'orlando shockers', '2006', '2007', '1st', 'oakland athletics'], ['jonathan lucroy', 'sanford', '2005 06', '2007', '3rd', 'milwaukee brewers'], ['alan farina', 'orlando shockers', '2005', '2007', '3rd', 'toronto blue jays'], ['jonathan holt', 'leesburg', '2006 - 2007', '2007', '5th', 'cleveland indians'], ['dee gordon', 'belleview', '2008', '2008', '4th', 'los angeles dodgers'], ['mycal jones', 'leesburg', '2007 - 2008', '2009', '4th', 'atlanta braves'], ['kent matthes', 'winter pines', '2008', '2009', '4th', 'colorado rockies'], ['thomas berryhill', 'deland', '2008', '2009', '5th', 'atlanta braves'], ['jimmy nelson', 'deland', '2009', '2010', '2nd', 'milwaukee brewers'], ['dante bichette jr', 'winter park', '2011', '2011', '1st', 'new york yankees'], ["peter o'brien", 'deland', '2010', '2012', '2nd', 'new york yankees'], ['brandon thomas', 'sanford', '2010', '2012', '4th', 'pittsburgh pirates']] |
hull f.c | https://en.wikipedia.org/wiki/Hull_F.C. | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1095938-1.html.csv | superlative | for hull f.c. , when there are 27 games played , the only time the position was 12th was for super league xiv . | {'scope': 'subset', 'col_superlative': '5', 'row_superlative': '12', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '1,2', 'subset': {'col': '2', 'criterion': 'equal', 'value': '27'}} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'played', '27'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; played ; 27 }', 'tointer': 'select the rows whose played record is equal to 27 .'}, 'position'], 'result': '12th', 'ind': 1, 'tostr': 'max { filter_eq { all_rows ; played ; 27 } ; position }', 'tointer': 'select the rows whose played record is equal to 27 . the maximum position record of these rows is 12th .'}, '12th'], 'result': True, 'ind': 2, 'tostr': 'eq { max { filter_eq { all_rows ; played ; 27 } ; position } ; 12th }', 'tointer': 'select the rows whose played record is equal to 27 . the maximum position record of these rows is 12th .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'played', '27'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; played ; 27 }', 'tointer': 'select the rows whose played record is equal to 27 .'}, 'position'], 'result': None, 'ind': 3, 'tostr': 'argmax { filter_eq { all_rows ; played ; 27 } ; position }'}, 'competition'], 'result': 'super league xiv', 'ind': 4, 'tostr': 'hop { argmax { filter_eq { all_rows ; played ; 27 } ; position } ; competition }'}, 'super league xiv'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { argmax { filter_eq { all_rows ; played ; 27 } ; position } ; competition } ; super league xiv }', 'tointer': 'the competition record of the row with superlative position record is super league xiv .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { max { filter_eq { all_rows ; played ; 27 } ; position } ; 12th } ; eq { hop { argmax { filter_eq { all_rows ; played ; 27 } ; position } ; competition } ; super league xiv } } = true', 'tointer': 'select the rows whose played record is equal to 27 . the maximum position record of these rows is 12th . the competition record of the row with superlative position record is super league xiv .'} | and { eq { max { filter_eq { all_rows ; played ; 27 } ; position } ; 12th } ; eq { hop { argmax { filter_eq { all_rows ; played ; 27 } ; position } ; competition } ; super league xiv } } = true | select the rows whose played record is equal to 27 . the maximum position record of these rows is 12th . the competition record of the row with superlative position record is super league xiv . | 8 | 7 | {'and_6': 6, 'result_7': 7, 'eq_2': 2, 'max_1': 1, 'filter_eq_0': 0, 'all_rows_8': 8, 'played_9': 9, '27_10': 10, 'position_11': 11, '12th_12': 12, 'str_eq_5': 5, 'str_hop_4': 4, 'argmax_3': 3, 'position_13': 13, 'competition_14': 14, 'super league xiv_15': 15} | {'and_6': 'and', 'result_7': 'true', 'eq_2': 'eq', 'max_1': 'max', 'filter_eq_0': 'filter_eq', 'all_rows_8': 'all_rows', 'played_9': 'played', '27_10': '27', 'position_11': 'position', '12th_12': '12th', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'argmax_3': 'argmax', 'position_13': 'position', 'competition_14': 'competition', 'super league xiv_15': 'super league xiv'} | {'and_6': [7], 'result_7': [], 'eq_2': [6], 'max_1': [2], 'filter_eq_0': [1, 3], 'all_rows_8': [0], 'played_9': [0], '27_10': [0], 'position_11': [1], '12th_12': [2], 'str_eq_5': [6], 'str_hop_4': [5], 'argmax_3': [4], 'position_13': [3], 'competition_14': [4], 'super league xiv_15': [5]} | ['competition', 'played', 'drawn', 'lost', 'position'] | [['super league iii', '23', '0', '15', '9th'], ['super league iv', '30', '0', '25', '13th'], ['super league v', '28', '1', '15', '7th'], ['super league vi', '28', '2', '6', '3rd'], ['super league vii', '28', '0', '12', '5th'], ['super league viii', '28', '3', '12', '7th'], ['super league ix', '28', '2', '12', '3rd'], ['super league x', '28', '2', '11', '5th'], ['super league xi', '28', '0', '8', '2nd'], ['super league xii', '27', '2', '11', '5th'], ['super league xiii', '27', '1', '18', '11th'], ['super league xiv', '27', '0', '17', '12th'], ['super league xv', '27', '0', '11', '6th'], ['super league xvi', '27', '1', '13', '8th'], ['super league xvii', '27', '2', '10', '6th'], ['super league xviii', '27', '2', '12', '6th']] |
united states house of representatives elections in washington , 2008 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections_in_Washington%2C_2008 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16185956-1.html.csv | majority | most of the incumbents in washington districts , running in the 2008 united states house of representative elections were registered democrats . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'democrat', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'party', 'democrat'], 'result': True, 'ind': 0, 'tointer': 'for the party records of all rows , most of them fuzzily match to democrat .', 'tostr': 'most_eq { all_rows ; party ; democrat } = true'} | most_eq { all_rows ; party ; democrat } = true | for the party records of all rows , most of them fuzzily match to democrat . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'party_3': 3, 'democrat_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'party_3': 'party', 'democrat_4': 'democrat'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'party_3': [0], 'democrat_4': [0]} | ['district', 'incumbent', 'party', 'elected', 'status', '2008 candidates', 'results'] | [['washington 1', 'jay inslee', 'democrat', '1998', 'running', 'jay inslee ( d ) ( cw ) larry ishmael ( r ) ( cw )', '68 % 32 %'], ['washington 2', 'rick larsen', 'democrat', '2000', 'running', 'rick larsen ( d ) ( cw ) rick bart ( r ) ( cw )', '62 % 38 %'], ['washington 3', 'brian baird', 'democrat', '1998', 'running', 'brian baird ( d ) ( cw ) michael delavar ( r ) ( cw )', '64 % 36 %'], ['washington 4', 'doc hastings', 'republican', '1994', 'running', 'doc hastings ( r ) ( cw ) george fearing ( d ) ( cw )', '63 % 37 %'], ['washington 5', 'cathy mcmorris', 'republican', '2004', 'running', 'cathy mcmorris ( r ) ( cw ) mark mays ( d ) ( cw )', '65 % 35 %'], ['washington 6', 'norm dicks', 'democrat', '1976', 'running', 'norm dicks ( d ) ( cw ) doug cloud ( r ) ( cw )', '67 % 33 %'], ['washington 7', 'jim mcdermott', 'democrat', '1988', 'running', 'jim mcdermott ( d ) ( cw ) steve beren ( r ) ( cw )', '84 % 16 %'], ['washington 8', 'dave reichert', 'republican', '2004', 'running', 'dave reichert ( r ) ( cw ) darcy burner ( d ) ( cw )', '53 % 47 %'], ['washington 9', 'adam smith', 'democrat', '1996', 'running', 'adam smith ( d ) ( cw ) james postma ( r ) ( cw )', '65 % 35 %']] |
blood agent | https://en.wikipedia.org/wiki/Blood_agent | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1095283-1.html.csv | comparative | arsine is more effective as a blood agent than vinyl arsine is . | {'row_1': '5', 'row_2': '6', 'col': '3', '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', 'agent', 'arsine'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose agent record fuzzily matches to arsine .', 'tostr': 'filter_eq { all_rows ; agent ; arsine }'}, 'effectiveness as blood agent'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; agent ; arsine } ; effectiveness as blood agent }', 'tointer': 'select the rows whose agent record fuzzily matches to arsine . take the effectiveness as blood agent record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'agent', 'vinyl arsine'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose agent record fuzzily matches to vinyl arsine .', 'tostr': 'filter_eq { all_rows ; agent ; vinyl arsine }'}, 'effectiveness as blood agent'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; agent ; vinyl arsine } ; effectiveness as blood agent }', 'tointer': 'select the rows whose agent record fuzzily matches to vinyl arsine . take the effectiveness as blood agent record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; agent ; arsine } ; effectiveness as blood agent } ; hop { filter_eq { all_rows ; agent ; vinyl arsine } ; effectiveness as blood agent } } = true', 'tointer': 'select the rows whose agent record fuzzily matches to arsine . take the effectiveness as blood agent record of this row . select the rows whose agent record fuzzily matches to vinyl arsine . take the effectiveness as blood agent record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; agent ; arsine } ; effectiveness as blood agent } ; hop { filter_eq { all_rows ; agent ; vinyl arsine } ; effectiveness as blood agent } } = true | select the rows whose agent record fuzzily matches to arsine . take the effectiveness as blood agent record of this row . select the rows whose agent record fuzzily matches to vinyl arsine . take the effectiveness as blood agent 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, 'agent_7': 7, 'arsine_8': 8, 'effectiveness as blood agent_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'agent_11': 11, 'vinyl arsine_12': 12, 'effectiveness as blood agent_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', 'agent_7': 'agent', 'arsine_8': 'arsine', 'effectiveness as blood agent_9': 'effectiveness as blood agent', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'agent_11': 'agent', 'vinyl arsine_12': 'vinyl arsine', 'effectiveness as blood agent_13': 'effectiveness as blood agent'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'agent_7': [0], 'arsine_8': [0], 'effectiveness as blood agent_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'agent_11': [1], 'vinyl arsine_12': [1], 'effectiveness as blood agent_13': [3]} | ['agent', 'melting / boiling point', 'effectiveness as blood agent', 'persistence , open area', 'persistence , enclosed area', 'field stability', 'storage stability', 'toxicity as blood agent'] | [['hydrogen cyanide', '- 13 / 26 degree', '10', '2', '9', '10', '8', '10'], ['cyanogen', '- 28 / - 21 degree', '9', '2', '9', '8', '7', '9'], ['cyanogen chloride', '- 6 / 14 degree', '8', '3', '9', '9', '9', '8'], ['cyanogen bromide', '52 / 62 degree', '9', '5', '8', '5', '6', '8'], ['arsine', '- 117 / - 62 degree', '9', '3', '8', '5', '9', '9'], ['vinyl arsine', '124 degree ( boiling )', '7', '7', '9', '8', '9', '6'], ['phosgene', '- 118 / 8', '10', '6', '9', '5', '8', '6']] |
athletics at the 2008 summer olympics - men 's 200 metres | https://en.wikipedia.org/wiki/Athletics_at_the_2008_Summer_Olympics_%E2%80%93_Men%27s_200_metres | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18569011-3.html.csv | majority | the majority of athletes posted a time of 21 seconds . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': '21 .', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'time', '21 .'], 'result': True, 'ind': 0, 'tointer': 'for the time records of all rows , most of them fuzzily match to 21 . .', 'tostr': 'most_eq { all_rows ; time ; 21 . } = true'} | most_eq { all_rows ; time ; 21 . } = true | for the time records of all rows , most of them fuzzily match to 21 . . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'time_3': 3, '21._4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'time_3': 'time', '21._4': '21 .'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'time_3': [0], '21._4': [0]} | ['rank', 'lane', 'athlete', 'nationality', 'time', 'react'] | [['1', '4', 'shawn crawford', 'united states', '20.61', '0.216'], ['2', '6', 'marcin jędrusiński', 'poland', '20.64', '0.199'], ['3', '7', 'stephan buckland', 'mauritius', '20.98', '0.229'], ['4', '1', 'jiří vojtík', 'czech republic', '21.05', '0.165'], ['5', '9', 'fanuel kenosi', 'botswana', '21.09', '0.211'], ['6', '3', 'adam harris', 'guyana', '21.36', '0.163'], ['7', '5', 'khalil al - hanahneh', 'jordan', '21.55', '0.184'], ['8', '2', 'solomon bayoh', 'sierra leone', '22.16', '0.216']] |
2007 german motorcycle grand prix | https://en.wikipedia.org/wiki/2007_German_motorcycle_Grand_Prix | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12262589-1.html.csv | superlative | in the 2007 german motorcycle grand prix , dani pedrosa ranks the highest . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', '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', 'time / retired'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; time / retired }'}, 'rider'], 'result': 'dani pedrosa', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; time / retired } ; rider }'}, 'dani pedrosa'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; time / retired } ; rider } ; dani pedrosa } = true', 'tointer': 'select the row whose time / retired record of all rows is maximum . the rider record of this row is dani pedrosa .'} | eq { hop { argmax { all_rows ; time / retired } ; rider } ; dani pedrosa } = true | select the row whose time / retired record of all rows is maximum . the rider record of this row is dani pedrosa . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'time / retired_5': 5, 'rider_6': 6, 'dani pedrosa_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'time / retired_5': 'time / retired', 'rider_6': 'rider', 'dani pedrosa_7': 'dani pedrosa'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'time / retired_5': [0], 'rider_6': [1], 'dani pedrosa_7': [2]} | ['rider', 'manufacturer', 'laps', 'time / retired', 'grid'] | [['dani pedrosa', 'honda', '30', '41:53.196', '2'], ['loris capirossi', 'ducati', '30', '+ 13.166', '7'], ['nicky hayden', 'honda', '30', '+ 16.771', '14'], ['colin edwards', 'yamaha', '30', '+ 18.299', '13'], ['casey stoner', 'ducati', '30', '+ 31.426', '1'], ['marco melandri', 'honda', '30', '+ 31.917', '3'], ['john hopkins', 'suzuki', '30', '+ 33.395', '5'], ['anthony west', 'kawasaki', '30', '+ 41.194', '12'], ['alex hofmann', 'ducati', '30', '+ 43.214', '16'], ['michel fabrizio', 'honda', '30', '+ 44.459', '17'], ['chris vermeulen', 'suzuki', '30', '+ 1:01.894', '11'], ['kurtis roberts', 'kr212v', '30', '+ 1:10.721', '19'], ['makoto tamada', 'yamaha', '28', '+ 2 laps', '18'], ['carlos checa', 'honda', '27', '+ 3 laps', '15'], ['randy de puniet', 'kawasaki', '29', 'retirement', '4'], ['shinya nakano', 'honda', '19', 'retirement', '10'], ['alex barros', 'ducati', '9', 'accident', '8'], ['valentino rossi', 'yamaha', '5', 'accident', '6'], ['sylvain guintoli', 'ducati', '3', 'accident', '9']] |
sean alvarez | https://en.wikipedia.org/wiki/Sean_Alvarez | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17443088-2.html.csv | count | sean alvarez won all 3 of his matches that took place in japan . | {'scope': 'subset', 'criterion': 'equal', 'value': 'win', 'result': '3', 'col': '1', 'subset': {'col': '8', 'criterion': 'equal', 'value': 'japan'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'japan'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; location ; japan }', 'tointer': 'select the rows whose location record fuzzily matches to japan .'}, 'res', 'win'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose location record fuzzily matches to japan . among these rows , select the rows whose res record fuzzily matches to win .', 'tostr': 'filter_eq { filter_eq { all_rows ; location ; japan } ; res ; win }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; location ; japan } ; res ; win } }', 'tointer': 'select the rows whose location record fuzzily matches to japan . among these rows , select the rows whose res record fuzzily matches to win . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; location ; japan } ; res ; win } } ; 3 } = true', 'tointer': 'select the rows whose location record fuzzily matches to japan . among these rows , select the rows whose res record fuzzily matches to win . the number of such rows is 3 .'} | eq { count { filter_eq { filter_eq { all_rows ; location ; japan } ; res ; win } } ; 3 } = true | select the rows whose location record fuzzily matches to japan . among these rows , select the rows whose res record fuzzily matches to win . 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, 'location_6': 6, 'japan_7': 7, 'res_8': 8, 'win_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', 'location_6': 'location', 'japan_7': 'japan', 'res_8': 'res', 'win_9': 'win', '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], 'location_6': [0], 'japan_7': [0], 'res_8': [1], 'win_9': [1], '3_10': [3]} | ['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location'] | [['loss', '4 - 3', 'wesley correira', 'tko ( knees and punches )', 'ufc 42', '2', '1:46', 'florida , united states'], ['win', '4 - 2', 'mike radnov', 'submission ( rear naked choke )', 'ucc 10 - battle for the belts 2002', '2', '2:02', 'quebec , canada'], ['loss', '3 - 2', 'eric pele', 'ko', 'kotc 9 - showtime', '3', '0:27', 'california , united states'], ['win', '3 - 1', 'wataru sakata', 'decision', 'rings : final capture', '3', '5:00', 'japan'], ['win', '2 - 1', 'willie peeters', 'n / a', 'rings - mega battle tournament 1997 semifinal', '1', '9:40', 'japan'], ['loss', '1 - 1', 'oleg taktarov', 'ko ( punches )', 'pentagon combat - pentagon combat', '1', '0:52', 'brazil'], ['win', '1 - 0', 'yoji anjo', 'submission ( punches )', 'u - japan', '1', '34:26', 'japan']] |
st. catharines black hawks | https://en.wikipedia.org/wiki/St._Catharines_Black_Hawks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1143966-1.html.csv | ordinal | from the 1962-63 season to the 1974-75 season , the st. catharines black hawks ' second highest number of goals was 343 . | {'row': '9', 'col': '8', 'order': '2', 'col_other': 'n/a', 'max_or_min': 'max_to_min', 'value_mentioned': 'yes', 'scope': 'all', 'subset': None} | {'func': 'eq', 'args': [{'func': 'nth_max', 'args': ['all_rows', 'goals for', '2'], 'result': '343', 'ind': 0, 'tostr': 'nth_max { all_rows ; goals for ; 2 }', 'tointer': 'the 2nd maximum goals for record of all rows is 343 .'}, '343'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_max { all_rows ; goals for ; 2 } ; 343 } = true', 'tointer': 'the 2nd maximum goals for record of all rows is 343 .'} | eq { nth_max { all_rows ; goals for ; 2 } ; 343 } = true | the 2nd maximum goals for record of all rows is 343 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'nth_max_0': 0, 'all_rows_3': 3, 'goals for_4': 4, '2_5': 5, '343_6': 6} | {'eq_1': 'eq', 'result_2': 'true', 'nth_max_0': 'nth_max', 'all_rows_3': 'all_rows', 'goals for_4': 'goals for', '2_5': '2', '343_6': '343'} | {'eq_1': [2], 'result_2': [], 'nth_max_0': [1], 'all_rows_3': [0], 'goals for_4': [0], '2_5': [0], '343_6': [1]} | ['season', 'games', 'won', 'lost', 'tied', 'points', 'pct %', 'goals for', 'goals against', 'standing'] | [['1962 - 63', '50', '15', '24', '11', '41', '0.410', '172', '224', '5th oha'], ['1963 - 64', '56', '29', '20', '7', '65', '0.580', '244', '215', '3rd oha'], ['1964 - 65', '56', '19', '28', '9', '41', '0.420', '236', '253', '7th oha'], ['1965 - 66', '48', '15', '26', '7', '37', '0.385', '182', '231', '8th oha'], ['1966 - 67', '48', '19', '20', '9', '47', '0.490', '175', '155', '5th oha'], ['1967 - 68', '54', '21', '30', '3', '45', '0.417', '200', '211', '6th oha'], ['1968 - 69', '54', '31', '11', '12', '74', '0.685', '296', '206', '2nd oha'], ['1969 - 70', '54', '30', '18', '6', '66', '0.611', '268', '210', '3rd oha'], ['1970 - 71', '62', '40', '17', '5', '85', '0.685', '343', '236', '2nd oha'], ['1971 - 72', '63', '25', '31', '7', '57', '0.452', '258', '311', '7th oha'], ['1972 - 73', '63', '24', '28', '11', '59', '0.468', '280', '318', '5th oha'], ['1973 - 74', '70', '41', '23', '6', '88', '0.629', '358', '278', '2nd oha'], ['1974 - 75', '70', '30', '33', '7', '67', '0.479', '284', '300', '6th oha']] |
2008 - 09 chicago bulls season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Chicago_Bulls_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17058151-8.html.csv | aggregation | during the 2008 - 2009 chicago bulls season , tyrus thomas had 58 rebounds . | {'scope': 'subset', 'col': '6', 'type': 'sum', 'result': '58', 'subset': {'col': '6', 'criterion': 'fuzzily_match', 'value': 'tyrus thomas'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high rebounds', 'tyrus thomas'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; high rebounds ; tyrus thomas }', 'tointer': 'select the rows whose high rebounds record fuzzily matches to tyrus thomas .'}, 'high rebounds'], 'result': '58', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; high rebounds ; tyrus thomas } ; high rebounds }'}, '58'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; high rebounds ; tyrus thomas } ; high rebounds } ; 58 } = true', 'tointer': 'select the rows whose high rebounds record fuzzily matches to tyrus thomas . the sum of the high rebounds record of these rows is 58 .'} | round_eq { sum { filter_eq { all_rows ; high rebounds ; tyrus thomas } ; high rebounds } ; 58 } = true | select the rows whose high rebounds record fuzzily matches to tyrus thomas . the sum of the high rebounds record of these rows is 58 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'high rebounds_5': 5, 'tyrus thomas_6': 6, 'high rebounds_7': 7, '58_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'high rebounds_5': 'high rebounds', 'tyrus thomas_6': 'tyrus thomas', 'high rebounds_7': 'high rebounds', '58_8': '58'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'high rebounds_5': [0], 'tyrus thomas_6': [0], 'high rebounds_7': [1], '58_8': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['49', 'february 3', 'houston', 'l 100 - 107 ( ot )', 'luol deng ( 28 )', 'tyrus thomas ( 13 )', 'derrick rose ( 7 )', 'toyota center 16653', '21 - 28'], ['50', 'february 4', 'new orleans', 'w 107 - 93 ( ot )', 'derrick rose ( 21 )', 'tyrus thomas ( 10 )', 'ben gordon ( 7 )', 'new orleans arena 16270', '22 - 28'], ['51', 'february 7', 'dallas', 'l 114 - 115 ( ot )', 'ben gordon ( 28 )', 'tyrus thomas ( 12 )', 'derrick rose ( 9 )', 'american airlines center 20349', '22 - 29'], ['52', 'february 10', 'detroit', 'w 107 - 102 ( ot )', 'ben gordon ( 24 )', 'joakim noah ( 16 )', 'kirk hinrich ( 5 )', 'united center 21896', '23 - 29'], ['53', 'february 12', 'miami', 'l 93 - 95 ( ot )', 'ben gordon ( 34 )', 'joakim noah ( 11 )', 'derrick rose ( 6 )', 'united center 21801', '23 - 30'], ['54', 'february 18', 'milwaukee', 'w 113 - 104 ( ot )', 'kirk hinrich ( 31 )', 'joakim noah ( 9 )', 'derrick rose ( 9 )', 'bradley center 15309', '24 - 30'], ['55', 'february 20', 'denver', 'w 116 - 99 ( ot )', 'ben gordon ( 37 )', 'luol deng , tyrus thomas ( 12 )', 'kirk hinrich ( 8 )', 'united center 21790', '25 - 30'], ['56', 'february 22', 'indiana', 'l 91 - 98 ( ot )', 'ben gordon ( 28 )', 'joakim noah ( 12 )', 'derrick rose ( 8 )', 'conseco fieldhouse 17083', '25 - 31'], ['57', 'february 24', 'orlando', 'w 120 - 102 ( ot )', 'derrick rose ( 22 )', 'joakim noah ( 8 )', 'derrick rose , brad miller ( 5 )', 'united center 21902', '26 - 31'], ['58', 'february 25', 'new jersey', 'l 99 - 111 ( ot )', 'ben gordon ( 17 )', 'tyrus thomas ( 11 )', 'kirk hinrich , derrick rose ( 5 )', 'izod center 14075', '26 - 32'], ['59', 'february 27', 'washington', 'l 90 - 113 ( ot )', 'john salmons ( 25 )', 'brad miller ( 11 )', 'luol deng , john salmons , derrick rose ( 3 )', 'verizon center 18114', '26 - 33']] |
list of ministers for the police force of luxembourg | https://en.wikipedia.org/wiki/List_of_Ministers_for_the_Police_Force_of_Luxembourg | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16620096-1.html.csv | unique | of the ministers for the police force of luxembourg , the only one from the csv party was marc fischbach . | {'scope': 'all', 'row': '4', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'csv', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party', 'csv'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose party record fuzzily matches to csv .', 'tostr': 'filter_eq { all_rows ; party ; csv }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; party ; csv } }', 'tointer': 'select the rows whose party record fuzzily matches to csv . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party', 'csv'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose party record fuzzily matches to csv .', 'tostr': 'filter_eq { all_rows ; party ; csv }'}, 'minister'], 'result': 'marc fischbach', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; party ; csv } ; minister }'}, 'marc fischbach'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; party ; csv } ; minister } ; marc fischbach }', 'tointer': 'the minister record of this unqiue row is marc fischbach .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; party ; csv } } ; eq { hop { filter_eq { all_rows ; party ; csv } ; minister } ; marc fischbach } } = true', 'tointer': 'select the rows whose party record fuzzily matches to csv . there is only one such row in the table . the minister record of this unqiue row is marc fischbach .'} | and { only { filter_eq { all_rows ; party ; csv } } ; eq { hop { filter_eq { all_rows ; party ; csv } ; minister } ; marc fischbach } } = true | select the rows whose party record fuzzily matches to csv . there is only one such row in the table . the minister record of this unqiue row is marc fischbach . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'party_7': 7, 'csv_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'minister_9': 9, 'marc fischbach_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'party_7': 'party', 'csv_8': 'csv', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'minister_9': 'minister', 'marc fischbach_10': 'marc fischbach'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'party_7': [0], 'csv_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'minister_9': [2], 'marc fischbach_10': [3]} | ['minister', 'party', 'start date', 'end date', 'prime minister'] | [['eugène schaus', 'dp', '6 february 1969', '15 june 1974', 'pierre werner'], ['émile krieps', 'dp', '15 june 1974', '16 july 1979', 'gaston thorn'], ['émile krieps', 'dp', '16 july 1979', '20 july 1984', 'pierre werner'], ['marc fischbach', 'csv', '20 july 1984', '14 july 1989', 'jacques santer'], ['jacques poos', 'lsap', '14 july 1989', '13 july 1994', 'jacques santer'], ['alex bodry', 'lsap', '13 july 1994', '26 january 1995', 'jacques santer'], ['alex bodry', 'lsap', '26 january 1995', '7 august 1999', 'jean - claude juncker']] |
list of interplanetary voyages | https://en.wikipedia.org/wiki/List_of_interplanetary_voyages | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13698001-7.html.csv | aggregation | for interplanetary voyages with venera spacecraft , the total amount of time elapsed was 205 days . | {'scope': 'subset', 'col': '5', 'type': 'sum', 'result': '205', 'subset': {'col': '1', 'criterion': 'fuzzily_match', 'value': 'venera'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'spacecraft', 'venera'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; spacecraft ; venera }', 'tointer': 'select the rows whose spacecraft record fuzzily matches to venera .'}, 'time elapsed'], 'result': '205', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; spacecraft ; venera } ; time elapsed }'}, '205'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; spacecraft ; venera } ; time elapsed } ; 205 } = true', 'tointer': 'select the rows whose spacecraft record fuzzily matches to venera . the sum of the time elapsed record of these rows is 205 .'} | round_eq { sum { filter_eq { all_rows ; spacecraft ; venera } ; time elapsed } ; 205 } = true | select the rows whose spacecraft record fuzzily matches to venera . the sum of the time elapsed record of these rows is 205 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'spacecraft_5': 5, 'venera_6': 6, 'time elapsed_7': 7, '205_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'spacecraft_5': 'spacecraft', 'venera_6': 'venera', 'time elapsed_7': 'time elapsed', '205_8': '205'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'spacecraft_5': [0], 'venera_6': [0], 'time elapsed_7': [1], '205_8': [2]} | ['spacecraft', 'destination', 'launched', 'closest approach', 'time elapsed'] | [['venera 1', 'venus', '12 february 1961', '19 may 1961', '97 days ( 3 months , 8 days )'], ['mariner 2', 'venus', '27 august 1962', '14 december 1962', '110 days ( 3 months , 18 days )'], ['mars 1', 'mars', '1 november 1962', '19 june 1963', '231 days ( 7 months , 19 days )'], ['zond 1', 'venus', '2 april 1964', '14 july 1964', '104 days ( 3 months , 13 days )'], ['mariner 4', 'mars', '28 november 1964', '15 july 1965', '230 days ( 7 months , 18 days )'], ['zond 2', 'mars', '30 november 1964', '6 august 1965', '250 days ( 8 months , 8 days )'], ['venera 2', 'venus', '12 november 1965', '27 february 1966', '108 days ( 3 months , 16 days )'], ['mariner 5', 'venus', '14 june 1967', '19 october 1967', '128 days ( 4 months , 6 days )'], ['mariner 6', 'mars', '24 february 1969', '31 july 1969', '158 days ( 5 months , 8 days )'], ['mariner 7', 'mars', '27 february 1969', '5 august 1969', '160 days ( 5 months , 10 days )']] |
larry mize | https://en.wikipedia.org/wiki/Larry_Mize | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1584996-5.html.csv | aggregation | in the two open championships , larry mize made a total of 17 cuts . | {'scope': 'subset', 'col': '7', 'type': 'sum', 'result': '17', 'subset': {'col': '1', 'criterion': 'equal', 'value': 'open'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'open'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; tournament ; open }', 'tointer': 'select the rows whose tournament record fuzzily matches to open .'}, 'cuts made'], 'result': '17', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; tournament ; open } ; cuts made }'}, '17'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; tournament ; open } ; cuts made } ; 17 } = true', 'tointer': 'select the rows whose tournament record fuzzily matches to open . the sum of the cuts made record of these rows is 17 .'} | round_eq { sum { filter_eq { all_rows ; tournament ; open } ; cuts made } ; 17 } = true | select the rows whose tournament record fuzzily matches to open . the sum of the cuts made record of these rows is 17 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'tournament_5': 5, 'open_6': 6, 'cuts made_7': 7, '17_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'tournament_5': 'tournament', 'open_6': 'open', 'cuts made_7': 'cuts made', '17_8': '17'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'tournament_5': [0], 'open_6': [0], 'cuts made_7': [1], '17_8': [2]} | ['tournament', 'wins', 'top - 5', 'top - 10', 'top - 25', 'events', 'cuts made'] | [['masters tournament', '1', '2', '3', '11', '30', '17'], ['us open', '0', '1', '1', '4', '18', '10'], ['the open championship', '0', '0', '0', '2', '12', '7'], ['pga championship', '0', '0', '2', '6', '16', '10'], ['totals', '1', '3', '6', '23', '76', '44']] |
indiana high school athletics conferences : mid - eastern - northwestern | https://en.wikipedia.org/wiki/Indiana_High_School_Athletics_Conferences%3A_Mid-Eastern_%E2%80%93_Northwestern | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18942405-13.html.csv | superlative | new prairie 1 is the indiana high school with the highest amount of students enrolled . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '7', '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', 'enrollment'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; enrollment }'}, 'school'], 'result': 'new prairie 1', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; enrollment } ; school }'}, 'new prairie 1'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; enrollment } ; school } ; new prairie 1 } = true', 'tointer': 'select the row whose enrollment record of all rows is maximum . the school record of this row is new prairie 1 .'} | eq { hop { argmax { all_rows ; enrollment } ; school } ; new prairie 1 } = true | select the row whose enrollment record of all rows is maximum . the school record of this row is new prairie 1 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'enrollment_5': 5, 'school_6': 6, 'new prairie 1_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'enrollment_5': 'enrollment', 'school_6': 'school', 'new prairie 1_7': 'new prairie 1'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'enrollment_5': [0], 'school_6': [1], 'new prairie 1_7': [2]} | ['school', 'location', 'mascot', 'enrollment', 'ihsaa class', 'ihsaa football class', 'county'] | [['bremen', 'bremen', 'lions', '495', 'aa', 'aa', '50 marshall'], ['culver community', 'culver', 'cavaliers', '287', 'a', 'a', '50 marshall'], ['glenn', 'walkerton', 'falcons', '605', 'aaa', 'aaa', '71 st joseph'], ['jimtown', 'elkhart', 'jimmies', '601', 'aaa', 'aaa', '20 elkhart'], ['knox community', 'knox', 'redskins', '620', 'aaa', 'aaa', '75 starke'], ['laville', 'lakeville', 'lancers', '379', 'aa', 'a', '71 st joseph'], ['new prairie 1', 'new carlisle', 'cougars', '852', 'aaa', 'aaaa', '46 laporte 71 st joseph'], ['triton', 'bourbon', 'trojans', '316', 'a', 'a', '50 marshall']] |
fai world grand prix 2008 | https://en.wikipedia.org/wiki/FAI_World_Grand_Prix_2008 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17277703-1.html.csv | majority | most of the pilots achieved over 10 points . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '10', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'points', '10'], 'result': True, 'ind': 0, 'tointer': 'for the points records of all rows , most of them are greater than 10 .', 'tostr': 'most_greater { all_rows ; points ; 10 } = true'} | most_greater { all_rows ; points ; 10 } = true | for the points records of all rows , most of them are greater than 10 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'points_3': 3, '10_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'points_3': 'points', '10_4': '10'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'points_3': [0], '10_4': [0]} | ['position', 'pilot', 'country', 'glider', 'points'] | [['1', 'sebastian kawa', 'poland', 'diana sailplanes - diana 2', '69'], ['2', 'carlos rocca vidal', 'chile', 'schempp - hirth flugzeugbau gmbh - ventus 2b', '55'], ['3', 'mario kiessling', 'germany', 'schempp - hirth flugzeugbau gmbh - ventus 2ax', '47'], ['4', 'uli schwenk', 'germany', 'schempp - hirth flugzeugbau gmbh - ventus 2ax', '40'], ['5', 'thomas gostner', 'italy', 'diana sailplanes - diana 2', '43'], ['6', 'tilo holighaus', 'germany', 'schempp - hirth flugzeugbau gmbh - ventus 2ax', '24'], ['7', 'wolfgang janowitsch', 'austria', 'schempp - hirth flugzeugbau gmbh - ventus 2cxa', '15'], ['8', 'rene vidal', 'chile', 'schempp - hirth flugzeugbau gmbh - ventus 2c', '14'], ['8', 'stanislaw wujczak', 'poland', 'alexander schleicher gmbh & co - asg 29', '14'], ['10', 'eduard supersperger', 'austria', 'schempp - hirth flugzeugbau gmbh - ventus 2b', '12'], ['11', 'heimo demmerer', 'austria', 'schempp - hirth flugzeugbau gmbh - ventus 2b', '11'], ['12', 'patrick puskeiler', 'germany', 'schempp - hirth flugzeugbau gmbh - discus 2ax', '8'], ['13', 'petr krejcirik', 'czech republic', 'schempp - hirth flugzeugbau gmbh - ventus 2ax', '4'], ['13', 'graham parker', 'australia', 'alexander schleicher gmbh & co - asg 29', '4'], ['15', 'olli teronen', 'finland', 'alexander schleicher gmbh & co - asg 29', '2']] |
catanduanes | https://en.wikipedia.org/wiki/Catanduanes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-255829-1.html.csv | aggregation | the average population in 2010 for the municipalities in catanduanes was 17935 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '17935', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'population ( 2010 )'], 'result': '17935', 'ind': 0, 'tostr': 'avg { all_rows ; population ( 2010 ) }'}, '17935'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; population ( 2010 ) } ; 17935 } = true', 'tointer': 'the average of the population ( 2010 ) record of all rows is 17935 .'} | round_eq { avg { all_rows ; population ( 2010 ) } ; 17935 } = true | the average of the population ( 2010 ) record of all rows is 17935 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'population (2010)_4': 4, '17935_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'population (2010)_4': 'population ( 2010 )', '17935_5': '17935'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'population (2010)_4': [0], '17935_5': [1]} | ['municipality', 'no of barangays', 'area ( hectares )', 'population ( 2007 )', 'population ( 2010 )', 'pop density ( per km 2 )'] | [['bagamanoc', '18', '8074', '10183', '11370', '140.8'], ['baras', '29', '10950', '11787', '12243', '111.8'], ['bato', '27', '4862', '18738', '19984', '411.0'], ['caramoran', '27', '26374', '25618', '28063', '106.4'], ['gigmoto', '9', '18182', '7569', '8003', '44.0'], ['pandan', '26', '11990', '19005', '19393', '161.7'], ['panganiban ( payo )', '23', '7996', '9290', '9738', '121.8'], ['san andres ( calolbon )', '38', '16731', '33781', '35779', '213.8'], ['san miguel', '24', '12994', '12966', '14107', '108.6'], ['viga', '31', '15823', '19266', '20669', '130.6']] |
united states house of representatives elections , 1828 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1828 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2668243-22.html.csv | count | five of the incumbents were re-elected in the 1828 election . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 're-elected', 'result': '5', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 're-elected'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to re-elected .', 'tostr': 'filter_eq { all_rows ; result ; re-elected }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; result ; re-elected } }', 'tointer': 'select the rows whose result record fuzzily matches to re-elected . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; result ; re-elected } } ; 5 } = true', 'tointer': 'select the rows whose result record fuzzily matches to re-elected . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; result ; re-elected } } ; 5 } = true | select the rows whose result record fuzzily matches to re-elected . the number of such rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'result_5': 5, 're-elected_6': 6, '5_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'result_5': 'result', 're-elected_6': 're-elected', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 're-elected_6': [0], '5_7': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['south carolina 1', 'william drayton', 'jacksonian', '1825 ( special )', 're - elected', 'william drayton ( j )'], ['south carolina 2', 'james hamilton , jr', 'jacksonian', '1822 ( special )', 'retired jacksonian hold', 'robert w barnwell ( j )'], ['south carolina 3', 'thomas r mitchell', 'jacksonian', '1820 1824', 'lost re - election jacksonian hold', 'john campbell ( j ) thomas r mitchell ( j )'], ['south carolina 4', 'william d martin', 'jacksonian', '1826', 're - elected', 'william d martin ( j )'], ['south carolina 5', 'george mcduffie', 'jacksonian', '1820', 're - elected', 'george mcduffie ( j )'], ['south carolina 6', 'warren r davis', 'jacksonian', '1826', 're - elected', 'warren r davis ( j ) 76.1 % cobb 23.9 %'], ['south carolina 7', 'william t nuckolls', 'jacksonian', '1826', 're - elected', 'william t nuckolls ( j )']] |
economy of europe | https://en.wikipedia.org/wiki/Economy_of_Europe | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1069072-1.html.csv | comparative | there are less people in paris than there are living in london . | {'row_1': '1', 'row_2': '2', 'col': '5', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'city', 'paris'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose city record fuzzily matches to paris .', 'tostr': 'filter_eq { all_rows ; city ; paris }'}, 'population m ( luz )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; city ; paris } ; population m ( luz ) }', 'tointer': 'select the rows whose city record fuzzily matches to paris . take the population m ( luz ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'city', 'london'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose city record fuzzily matches to london .', 'tostr': 'filter_eq { all_rows ; city ; london }'}, 'population m ( luz )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; city ; london } ; population m ( luz ) }', 'tointer': 'select the rows whose city record fuzzily matches to london . take the population m ( luz ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; city ; paris } ; population m ( luz ) } ; hop { filter_eq { all_rows ; city ; london } ; population m ( luz ) } } = true', 'tointer': 'select the rows whose city record fuzzily matches to paris . take the population m ( luz ) record of this row . select the rows whose city record fuzzily matches to london . take the population m ( luz ) record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; city ; paris } ; population m ( luz ) } ; hop { filter_eq { all_rows ; city ; london } ; population m ( luz ) } } = true | select the rows whose city record fuzzily matches to paris . take the population m ( luz ) record of this row . select the rows whose city record fuzzily matches to london . take the population m ( luz ) 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, 'city_7': 7, 'paris_8': 8, 'population m (luz)_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'city_11': 11, 'london_12': 12, 'population m (luz)_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', 'city_7': 'city', 'paris_8': 'paris', 'population m (luz)_9': 'population m ( luz )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'city_11': 'city', 'london_12': 'london', 'population m (luz)_13': 'population m ( luz )'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'city_7': [0], 'paris_8': [0], 'population m (luz)_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'city_11': [1], 'london_12': [1], 'population m (luz)_13': [3]} | ['rank', 'city', 'state', 'gdp in id b', 'population m ( luz )', 'gdp per capita id k', 'eurozone'] | [['1', 'paris', 'france', '731', '11.5', '62.4', 'y'], ['2', 'london', 'united kingdom', '565', '11.9', '49.4', 'n'], ['3', 'moscow', 'russia', '321', '10.5', '30.6', 'n'], ['4', 'madrid', 'spain', '230', '5.80', '39.7', 'y'], ['5', 'istanbul', 'turkey', '187', '13.2', '14.2', 'n'], ['6', 'barcelona', 'spain', '177', '4.97', '35.6', 'y'], ['7', 'rome', 'italy', '144', '3.46', '41.6', 'y'], ['8', 'milan', 'italy', '136', '3.08', '44.2', 'y'], ['9', 'vienna', 'austria', '122', '2.18', '56.0', 'y'], ['10', 'lisbon', 'portugal', '98', '2.44', '40.2', 'y'], ['11', 'athens', 'greece', '96', '4.01', '23.9', 'y'], ['12', 'berlin', 'germany', '95', '4.97', '19.1', 'y']] |
1990 - 91 yugoslav cup | https://en.wikipedia.org/wiki/1990%E2%80%9391_Yugoslav_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19294812-2.html.csv | comparative | dinamo zagreb had a higher agg than borac banja luka in the 1990-91 yugoslav cup . | {'row_1': '3', 'row_2': '1', 'col': '3', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team 1', 'dinamo zagreb'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team 1 record fuzzily matches to dinamo zagreb .', 'tostr': 'filter_eq { all_rows ; team 1 ; dinamo zagreb }'}, 'agg'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team 1 ; dinamo zagreb } ; agg }', 'tointer': 'select the rows whose team 1 record fuzzily matches to dinamo zagreb . take the agg record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team 1', 'borac banja luka'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose team 1 record fuzzily matches to borac banja luka .', 'tostr': 'filter_eq { all_rows ; team 1 ; borac banja luka }'}, 'agg'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; team 1 ; borac banja luka } ; agg }', 'tointer': 'select the rows whose team 1 record fuzzily matches to borac banja luka . take the agg record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; team 1 ; dinamo zagreb } ; agg } ; hop { filter_eq { all_rows ; team 1 ; borac banja luka } ; agg } } = true', 'tointer': 'select the rows whose team 1 record fuzzily matches to dinamo zagreb . take the agg record of this row . select the rows whose team 1 record fuzzily matches to borac banja luka . take the agg record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; team 1 ; dinamo zagreb } ; agg } ; hop { filter_eq { all_rows ; team 1 ; borac banja luka } ; agg } } = true | select the rows whose team 1 record fuzzily matches to dinamo zagreb . take the agg record of this row . select the rows whose team 1 record fuzzily matches to borac banja luka . take the agg 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, 'team 1_7': 7, 'dinamo zagreb_8': 8, 'agg_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'team 1_11': 11, 'borac banja luka_12': 12, 'agg_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', 'team 1_7': 'team 1', 'dinamo zagreb_8': 'dinamo zagreb', 'agg_9': 'agg', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'team 1_11': 'team 1', 'borac banja luka_12': 'borac banja luka', 'agg_13': 'agg'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'team 1_7': [0], 'dinamo zagreb_8': [0], 'agg_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'team 1_11': [1], 'borac banja luka_12': [1], 'agg_13': [3]} | ['tie no', 'team 1', 'agg', 'team 2', '1st leg', '2nd leg'] | [['1', 'borac banja luka', '2 - 1', 'osijek', '2 - 0', '0 - 1'], ['2', 'budućnost titograd', '2 - 1', 'partizan', '2 - 0', '0 - 1'], ['3', 'dinamo zagreb', '5 - 1', 'sarajevo', '1 - 0', '4 - 1'], ['4', 'hajduk split', '3 - 3 ( a )', 'pelister bitola', '1 - 1', '2 - 2'], ['5', 'ofk belgrade', '3 - 2', 'željezničar sarajevo', '2 - 1', '1 - 1'], ['6', 'proleter zrenjanin', '2 - 0', 'koper', '2 - 0', '0 - 0'], ['7', 'sloboda tuzla', '3 - 4', 'rijeka', '2 - 0', '1 - 4']] |
1995 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1995_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17162214-2.html.csv | majority | most of the participating players were from the united states . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'united states', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': True, 'ind': 0, 'tointer': 'for the country records of all rows , most of them fuzzily match to united states .', 'tostr': 'most_eq { all_rows ; country ; united states } = true'} | most_eq { all_rows ; country ; united states } = true | for the country records of all rows , most of them fuzzily match to united states . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'country_3': 3, 'united states_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'country_3': 'country', 'united states_4': 'united states'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'country_3': [0], 'united states_4': [0]} | ['place', 'player', 'country', 'score', 'to par'] | [['1', 'nick price', 'zimbabwe', '66', '- 4'], ['2', 'scott simpson', 'united states', '67', '- 3'], ['t3', 'phil mickelson', 'united states', '68', '- 2'], ['t3', 'greg norman', 'australia', '68', '- 2'], ['t5', 'bill glasson', 'united states', '69', '- 1'], ['t5', 'steve lowery', 'united states', '69', '- 1'], ['t5', 'jeff maggert', 'united states', '69', '- 1'], ['t5', 'masashi ozaki', 'japan', '69', '- 1'], ['t5', 'bob tway', 'united states', '69', '- 1'], ['t5', 'fuzzy zoeller', 'united states', '69', '- 1']] |
christian vietoris | https://en.wikipedia.org/wiki/Christian_Vietoris | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10705060-1.html.csv | count | christian vietoris raced in the gp2 series in both years 2010 and 2011 . | {'scope': 'all', 'criterion': 'equal', 'value': 'gp2 series', 'result': '2', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'series', 'gp2 series'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose series record fuzzily matches to gp2 series .', 'tostr': 'filter_eq { all_rows ; series ; gp2 series }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; series ; gp2 series } }', 'tointer': 'select the rows whose series record fuzzily matches to gp2 series . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; series ; gp2 series } } ; 2 } = true', 'tointer': 'select the rows whose series record fuzzily matches to gp2 series . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; series ; gp2 series } } ; 2 } = true | select the rows whose series record fuzzily matches to gp2 series . 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, 'series_5': 5, 'gp2 series_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', 'series_5': 'series', 'gp2 series_6': 'gp2 series', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'series_5': [0], 'gp2 series_6': [0], '2_7': [2]} | ['season', 'series', 'team name', 'races', 'poles', 'wins', 'points', 'position'] | [['2005', 'formula bmw adac', 'eifelland racing', '19', '0', '0', '17', '16th'], ['2006', 'formula bmw adac', 'josef kaufmann racing', '18', '9', '9', '277', '1st'], ['2007', 'german formula three', 'josef kaufmann racing', '12', '2', '1', '62', '6th'], ['2008', 'formula 3 euro series', 'mücke motorsport', '20', '1', '1', '36', '6th'], ['2009', 'formula 3 euro series', 'mücke motorsport', '18', '0', '4', '75', '2nd'], ['2009 - 10', 'gp2 asia series', 'dams', '8', '0', '1', '9', '10th'], ['2010', 'gp2 series', 'racing engineering', '18', '0', '1', '29', '9th'], ['2011', 'gp2 series', 'racing engineering', '14', '1', '2', '35', '7th'], ['2011', 'deutsche tourenwagen masters', 'persson motorsport', '10', '0', '0', '4', '14th'], ['2012', 'deutsche tourenwagen masters', 'hwa team', '10', '0', '0', '25', '12th']] |
list of superlative academy award winners and nominees | https://en.wikipedia.org/wiki/List_of_superlative_Academy_Award_winners_and_nominees | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10966872-2.html.csv | ordinal | john ford 's academy award record is the second earliest record that was set . | {'row': '1', 'col': '4', 'order': '2', 'col_other': '2', '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', 'year', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; year ; 2 }'}, 'director'], 'result': 'john ford', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; year ; 2 } ; director }'}, 'john ford'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; year ; 2 } ; director } ; john ford } = true', 'tointer': 'select the row whose year record of all rows is 2nd minimum . the director record of this row is john ford .'} | eq { hop { nth_argmin { all_rows ; year ; 2 } ; director } ; john ford } = true | select the row whose year record of all rows is 2nd minimum . the director record of this row is john ford . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'year_5': 5, '2_6': 6, 'director_7': 7, 'john ford_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', 'year_5': 'year', '2_6': '2', 'director_7': 'director', 'john ford_8': 'john ford'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'year_5': [0], '2_6': [0], 'director_7': [1], 'john ford_8': [2]} | ['superlative', 'director', 'record set', 'year', 'notes'] | [['most awards', 'john ford', '4 awards', '1952', 'awards resulted from 5 nominations'], ['most nominations', 'william wyler', '12 nominations', '1965', 'nominations resulted in 3 awards'], ['oldest winner', 'clint eastwood', '74 years old', '2004', 'million dollar baby'], ['oldest nominee', 'john huston', '79 years old', '1985', "prizzi 's honor"], ['youngest winner', 'norman taurog', '32 years old', '1930 / 31', 'skippy'], ['youngest nominee', 'john singleton', '24 years old', '1991', 'boyz n the hood']] |
usa today all - usa high school baseball team | https://en.wikipedia.org/wiki/USA_Today_All-USA_high_school_baseball_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11677100-1.html.csv | comparative | todd van poppel was awarded with the usa today all award before doug million . | {'row_1': '2', 'row_2': '6', 'col': '1', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'todd van poppel'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to todd van poppel .', 'tostr': 'filter_eq { all_rows ; player ; todd van poppel }'}, 'year'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; todd van poppel } ; year }', 'tointer': 'select the rows whose player record fuzzily matches to todd van poppel . take the year record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'doug million'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to doug million .', 'tostr': 'filter_eq { all_rows ; player ; doug million }'}, 'year'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; doug million } ; year }', 'tointer': 'select the rows whose player record fuzzily matches to doug million . take the year record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; player ; todd van poppel } ; year } ; hop { filter_eq { all_rows ; player ; doug million } ; year } } = true', 'tointer': 'select the rows whose player record fuzzily matches to todd van poppel . take the year record of this row . select the rows whose player record fuzzily matches to doug million . take the year record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; player ; todd van poppel } ; year } ; hop { filter_eq { all_rows ; player ; doug million } ; year } } = true | select the rows whose player record fuzzily matches to todd van poppel . take the year record of this row . select the rows whose player record fuzzily matches to doug million . take the year 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, 'todd van poppel_8': 8, 'year_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'doug million_12': 12, 'year_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', 'todd van poppel_8': 'todd van poppel', 'year_9': 'year', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'doug million_12': 'doug million', 'year_13': 'year'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'todd van poppel_8': [0], 'year_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'doug million_12': [1], 'year_13': [3]} | ['year', 'player', 'position', 'high school', 'hometown', 'mlb draft'] | [['1989', 'tyler houston', 'catcher', 'valley high school', 'las vegas , nv', '1st round - 2nd pick of 1989 draft ( braves )'], ['1990', 'todd van poppel', 'pitcher', 'martin high school', 'arlington , tx', "1st round - 14th pick of 1990 draft ( a 's )"], ['1991', 'brien taylor', 'pitcher', 'east carteret high school', 'beaufort , nc', '1st round - 1st pick of 1991 draft ( yankees )'], ['1992', 'derek jeter', 'infielder', 'central high school', 'kalamazoo , mi', '1st round - 6th pick of 1992 draft ( yankees )'], ['1993', 'alex rodriguez', 'infielder', 'westminster christian school', 'miami , fl', '1st round - 1st pick of 1993 draft ( mariners )'], ['1994', 'doug million', 'pitcher', 'sarasota high school', 'sarasota , fl', '1st round - 7th pick of 1994 draft ( rockies )'], ['1995', 'ben davis', 'catcher', 'malvern prep', 'malvern , pa', '1st round - 2nd pick of 1995 draft ( padres )'], ['1996', 'matt white', 'pitcher', 'waynesboro high school', 'waynesboro , pa', '1st round - 7th pick of 1996 draft ( giants )'], ['1997', 'rick ankiel', 'pitcher', 'port st lucie high school', 'port st lucie , fl', '2nd round - 72nd pick of 1997 draft ( cardinals )']] |
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 | superlative | the longest the rqw women 's championship title was held was 700 days . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '7', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': 'n/a', 'subset': None} | {'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'days held'], 'result': '700', 'ind': 0, 'tostr': 'max { all_rows ; days held }', 'tointer': 'the maximum days held record of all rows is 700 .'}, '700'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; days held } ; 700 } = true', 'tointer': 'the maximum days held record of all rows is 700 .'} | eq { max { all_rows ; days held } ; 700 } = true | the maximum days held record of all rows is 700 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'max_0': 0, 'all_rows_3': 3, 'days held_4': 4, '700_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'max_0': 'max', 'all_rows_3': 'all_rows', 'days held_4': 'days held', '700_5': '700'} | {'eq_1': [2], 'result_2': [], 'max_0': [1], 'all_rows_3': [0], 'days held_4': [0], '700_5': [1]} | ['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']] |
john wayne filmography | https://en.wikipedia.org/wiki/John_Wayne_filmography | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12379832-9.html.csv | unique | the only movie that john wayne appeared in which the leading lady was alberta vaughn was randy rides alone . | {'scope': 'all', 'row': '5', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'alberta vaughn', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'leading lady', 'alberta vaughn'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose leading lady record fuzzily matches to alberta vaughn .', 'tostr': 'filter_eq { all_rows ; leading lady ; alberta vaughn }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; leading lady ; alberta vaughn } }', 'tointer': 'select the rows whose leading lady record fuzzily matches to alberta vaughn . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'leading lady', 'alberta vaughn'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose leading lady record fuzzily matches to alberta vaughn .', 'tostr': 'filter_eq { all_rows ; leading lady ; alberta vaughn }'}, 'title'], 'result': 'randy rides alone', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; leading lady ; alberta vaughn } ; title }'}, 'randy rides alone'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; leading lady ; alberta vaughn } ; title } ; randy rides alone }', 'tointer': 'the title record of this unqiue row is randy rides alone .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; leading lady ; alberta vaughn } } ; eq { hop { filter_eq { all_rows ; leading lady ; alberta vaughn } ; title } ; randy rides alone } } = true', 'tointer': 'select the rows whose leading lady record fuzzily matches to alberta vaughn . there is only one such row in the table . the title record of this unqiue row is randy rides alone .'} | and { only { filter_eq { all_rows ; leading lady ; alberta vaughn } } ; eq { hop { filter_eq { all_rows ; leading lady ; alberta vaughn } ; title } ; randy rides alone } } = true | select the rows whose leading lady record fuzzily matches to alberta vaughn . there is only one such row in the table . the title record of this unqiue row is randy rides alone . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'leading lady_7': 7, 'alberta vaughn_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'title_9': 9, 'randy rides alone_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'leading lady_7': 'leading lady', 'alberta vaughn_8': 'alberta vaughn', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'title_9': 'title', 'randy rides alone_10': 'randy rides alone'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'leading lady_7': [0], 'alberta vaughn_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'title_9': [2], 'randy rides alone_10': [3]} | ['title', 'studio', 'role', 'leading lady', 'director'] | [['the lucky texan', 'mono', 'jerry mason', 'barbara sheldon', 'rn bradbury'], ['west of the divide', 'mono', 'ted hayden', 'virginia browne faire', 'rn bradbury'], ['blue steel', 'mono', 'john carruthers', 'eleanor hunt', 'rn bradbury'], ['the man from utah', 'mono', 'john westen', 'polly ann young', 'rn bradbury'], ['randy rides alone', 'mono', 'randy bowers', 'alberta vaughn', 'harry l fraser'], ['the star packer', 'mono', 'john travers', 'verna hillie', 'rn bradbury'], ['the trail beyond', 'mono', 'rod drew', 'verna hillie', 'rn bradbury'], ['the lawless frontier', 'mono', 'john tobin', 'sheila terry', 'rn bradbury'], ["' neath the arizona skies", 'mono', 'chris morrell', 'sheila terry', 'harry fraser']] |
snowy mountains scheme | https://en.wikipedia.org/wiki/Snowy_Mountains_Scheme | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-177948-2.html.csv | majority | in the dams of the snowy mountains scheme listed , the majority of dams were completed before 1970 . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '1970', 'subset': None} | {'func': 'most_less', 'args': ['all_rows', 'year completed', '1970'], 'result': True, 'ind': 0, 'tointer': 'for the year completed records of all rows , most of them are less than 1970 .', 'tostr': 'most_less { all_rows ; year completed ; 1970 } = true'} | most_less { all_rows ; year completed ; 1970 } = true | for the year completed records of all rows , most of them are less than 1970 . | 1 | 1 | {'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'year completed_3': 3, '1970_4': 4} | {'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'year completed_3': 'year completed', '1970_4': '1970'} | {'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'year completed_3': [0], '1970_4': [0]} | ['dam constructed', 'year completed', 'impounded body of water', 'reservoir capacity', 'dam wall height', 'dam type'] | [['blowering dam', '1968', 'blowering reservoir', 'ml ( 10 6cuft )', '-', 'rockfill embankment'], ['deep creek dam', '1961', 'deep creek reservoir', 'ml ( 10 6cuft )', '-', 'concrete gravity'], ['eucumbene dam', '1958', 'lake eucumbene', 'ml ( 10 6cuft )', '-', 'earthfill embankment'], ['geehi dam', '1966', 'geehi reservoir', 'ml ( 10 6cuft )', '-', 'rockfill embankment'], ['guthega dam', '1955', 'guthega reservoir', 'ml ( 10 6cuft )', '-', 'concrete gravity'], ['happy jacks dam', '1959', 'happy jacks pondage', 'ml ( 10 6cuft )', '-', 'concrete gravity'], ['island bend dam', '1965', 'island bend pondage', 'ml ( 10 6cuft )', '-', 'concrete gravity'], ['jindabyne dam', '1967', 'lake jindabyne', 'ml ( 10 6cuft )', '-', 'rockfill embankment'], ['jounama dam', '1968', 'jounama pondage', 'ml ( 10 6cuft )', '-', 'rockfill embankment'], ['khancoban dam', '1966', 'khancoban reservoir', 'ml ( 10 6cuft )', '-', 'earthfill embankment'], ['murray two dam', '1968', 'murray two pondage', 'ml ( 10 6cuft )', '-', 'concrete arch'], ['talbingo dam', '1970', 'talbingo reservoir', 'ml ( 10 6cuft )', '-', 'rockfill embankment'], ['tantangara dam', '1960', 'tantangara reservoir', 'ml ( 10 6cuft )', '-', 'concrete gravity'], ['tooma dam', '1961', 'tooma reservoir', 'ml ( 10 6cuft )', '-', 'concrete embankment'], ['tumut pond dam', '1959', 'tumut pond reservoir', 'ml ( 10 6cuft )', '-', 'concrete arch'], ['tumut two dam', '1961', 'tumut two pondage', 'ml ( 10 6cuft )', '-', 'concrete gravity']] |
1931 grand prix season | https://en.wikipedia.org/wiki/1931_Grand_Prix_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10061118-1.html.csv | count | four of these 1931 grand prix drivers drove bugatti vehicles . | {'scope': 'all', 'criterion': 'equal', 'value': 'bugatti', 'result': '4', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winning constructor', 'bugatti'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winning constructor record fuzzily matches to bugatti .', 'tostr': 'filter_eq { all_rows ; winning constructor ; bugatti }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; winning constructor ; bugatti } }', 'tointer': 'select the rows whose winning constructor record fuzzily matches to bugatti . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; winning constructor ; bugatti } } ; 4 } = true', 'tointer': 'select the rows whose winning constructor record fuzzily matches to bugatti . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; winning constructor ; bugatti } } ; 4 } = true | select the rows whose winning constructor record fuzzily matches to bugatti . 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, 'winning constructor_5': 5, 'bugatti_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', 'winning constructor_5': 'winning constructor', 'bugatti_6': 'bugatti', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'winning constructor_5': [0], 'bugatti_6': [0], '4_7': [2]} | ['name', 'circuit', 'date', 'winning drivers', 'winning constructor', 'report'] | [['italian grand prix', 'monza', '24 may', 'giuseppe campari', 'alfa romeo', 'report'], ['italian grand prix', 'monza', '24 may', 'tazio nuvolari', 'alfa romeo', 'report'], ['french grand prix', 'montlhéry', '21 june', 'louis chiron', 'bugatti', 'report'], ['french grand prix', 'montlhéry', '21 june', 'achille varzi', 'bugatti', 'report'], ['belgian grand prix', 'spa - francorchamps', '12 july', 'william grover - williams', 'bugatti', 'report'], ['belgian grand prix', 'spa - francorchamps', '12 july', 'caberto conelli', 'bugatti', 'report']] |
1991 - 92 seattle supersonics season | https://en.wikipedia.org/wiki/1991%E2%80%9392_Seattle_SuperSonics_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27902171-5.html.csv | count | seattle supersonics played against 12 teams during the 1991 - 92 season . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '12', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'team'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record is arbitrary .', 'tostr': 'filter_all { all_rows ; team }'}], 'result': '12', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; team } }', 'tointer': 'select the rows whose team record is arbitrary . the number of such rows is 12 .'}, '12'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; team } } ; 12 } = true', 'tointer': 'select the rows whose team record is arbitrary . the number of such rows is 12 .'} | eq { count { filter_all { all_rows ; team } } ; 12 } = true | select the rows whose team record is arbitrary . the number of such rows is 12 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'team_5': 5, '12_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'team_5': 'team', '12_6': '12'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'team_5': [0], '12_6': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['16', 'december 3', 'washington bullets', 'w 91 - 90', 'r pierce ( 26 )', 's kemp ( 12 )', 'g payton ( 5 )', 'seattle center coliseum 10957', '9 - 7'], ['17', 'december 6', 'minnesota timberwolves', 'w 96 - 94', 'r pierce ( 29 )', 'm cage ( 23 )', 'g payton , r pierce ( 5 )', 'seattle center coliseum 9796', '10 - 7'], ['18', 'december 7', 'dallas mavericks', 'w 104 - 101', 'r pierce ( 27 )', 'm cage ( 14 )', 'n mcmillan ( 6 )', 'seattle center coliseum 12313', '11 - 7'], ['19', 'december 10', 'chicago bulls', 'l 103 - 108', 'r pierce ( 30 )', 'm cage ( 13 )', 's kemp , g payton ( 5 )', 'chicago stadium 18061', '11 - 8'], ['20', 'december 11', 'new york knicks', 'l 87 - 96', 'r pierce ( 25 )', 'b benjamin , s kemp ( 9 )', 'r pierce ( 7 )', 'madison square garden 14934', '11 - 9'], ['21', 'december 13', 'boston celtics', 'l 97 - 117', 'r pierce ( 21 )', 'b benjamin ( 8 )', 'n mcmillan ( 8 )', 'boston garden 14890', '11 - 10'], ['22', 'december 14', 'philadelphia 76ers', 'l 95 - 104', 'b benjamin ( 23 )', 'b benjamin ( 9 )', 'n mcmillan ( 8 )', 'the spectrum 12395', '11 - 11'], ['23', 'december 17', 'los angeles clippers', 'w 116 - 99', 'b benjamin ( 20 )', 'm cage ( 13 )', 'n mcmillan ( 6 )', 'seattle center coliseum 10357', '12 - 11'], ['24', 'december 19', 'denver nuggets', 'w 119 - 106', 'r pierce ( 29 )', 'm cage ( 15 )', 'd mckey , n mcmillan , g payton ( 4 )', 'seattle center coliseum 10663', '13 - 11'], ['25', 'december 21', 'golden state warriors', 'w 120 - 112', 'r pierce ( 34 )', 'g payton ( 11 )', 'g payton ( 12 )', 'seattle center coliseum 14180', '14 - 11'], ['26', 'december 22', 'portland trail blazers', 'l 87 - 96', 'b benjamin ( 18 )', 'm cage ( 9 )', 'b kofoed , g payton ( 5 )', 'memorial coliseum 12888', '14 - 12'], ['27', 'december 26', 'sacramento kings', 'w 115 - 106 ( ot )', 'r pierce ( 27 )', 'b benjamin ( 13 )', 'g payton ( 5 )', 'arco arena 17014', '15 - 12']] |
christian danner | https://en.wikipedia.org/wiki/Christian_Danner | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1219722-3.html.csv | unique | 1989 is the only year that christian danner drove for the rial racing team . | {'scope': 'all', 'row': '7', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'rial racing', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'rial racing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to rial racing .', 'tostr': 'filter_eq { all_rows ; team ; rial racing }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; team ; rial racing } }', 'tointer': 'select the rows whose team record fuzzily matches to rial racing . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'rial racing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to rial racing .', 'tostr': 'filter_eq { all_rows ; team ; rial racing }'}, 'year'], 'result': '1989', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team ; rial racing } ; year }'}, '1989'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; team ; rial racing } ; year } ; 1989 }', 'tointer': 'the year record of this unqiue row is 1989 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; team ; rial racing } } ; eq { hop { filter_eq { all_rows ; team ; rial racing } ; year } ; 1989 } } = true', 'tointer': 'select the rows whose team record fuzzily matches to rial racing . there is only one such row in the table . the year record of this unqiue row is 1989 .'} | and { only { filter_eq { all_rows ; team ; rial racing } } ; eq { hop { filter_eq { all_rows ; team ; rial racing } ; year } ; 1989 } } = true | select the rows whose team record fuzzily matches to rial racing . there is only one such row in the table . the year record of this unqiue row is 1989 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'team_7': 7, 'rial racing_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1989_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'team_7': 'team', 'rial racing_8': 'rial racing', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1989_10': '1989'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'team_7': [0], 'rial racing_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1989_10': [3]} | ['year', 'team', 'chassis', 'engine', 'points'] | [['1985', 'west zakspeed racing', 'zakspeed 841', 'zakspeed 1500 / 4 1.5 l4t', '0'], ['1986', 'osella squadra corse', 'osella fa1f', 'alfa romeo 890t 1.5 v8t', '1'], ['1986', 'barclay arrows bmw', 'arrows a8', 'bmw m12 / 13 1.5 l4t', '1'], ['1986', 'barclay arrows bmw', 'arrows a9', 'bmw m12 / 13 1.5 l4t', '1'], ['1987', 'west zakspeed racing', 'zakspeed 861', 'zakspeed 1500 / 4 1.5 l4t', '0'], ['1987', 'west zakspeed racing', 'zakspeed 871', 'zakspeed 1500 / 4 1.5 l4t', '0'], ['1989', 'rial racing', 'rial arc2', 'ford cosworth dfr ( mader ) 3.5 v8', '3']] |
driver deaths in motorsport | https://en.wikipedia.org/wiki/Driver_deaths_in_motorsport | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1632486-11.html.csv | unique | indianapolis raceway park is the only circuit with a death in a drag racing event . | {'scope': 'all', 'row': '3', 'col': '1', 'col_other': '3', 'criterion': 'equal', 'value': 'drag racing', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'discipline', 'drag racing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose discipline record fuzzily matches to drag racing .', 'tostr': 'filter_eq { all_rows ; discipline ; drag racing }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; discipline ; drag racing } }', 'tointer': 'select the rows whose discipline record fuzzily matches to drag racing . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'discipline', 'drag racing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose discipline record fuzzily matches to drag racing .', 'tostr': 'filter_eq { all_rows ; discipline ; drag racing }'}, 'circuit'], 'result': 'indianapolis raceway park', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; discipline ; drag racing } ; circuit }'}, 'indianapolis raceway park'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; discipline ; drag racing } ; circuit } ; indianapolis raceway park }', 'tointer': 'the circuit record of this unqiue row is indianapolis raceway park .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; discipline ; drag racing } } ; eq { hop { filter_eq { all_rows ; discipline ; drag racing } ; circuit } ; indianapolis raceway park } } = true', 'tointer': 'select the rows whose discipline record fuzzily matches to drag racing . there is only one such row in the table . the circuit record of this unqiue row is indianapolis raceway park .'} | and { only { filter_eq { all_rows ; discipline ; drag racing } } ; eq { hop { filter_eq { all_rows ; discipline ; drag racing } ; circuit } ; indianapolis raceway park } } = true | select the rows whose discipline record fuzzily matches to drag racing . there is only one such row in the table . the circuit record of this unqiue row is indianapolis raceway park . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'discipline_7': 7, 'drag racing_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'circuit_9': 9, 'indianapolis raceway park_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'discipline_7': 'discipline', 'drag racing_8': 'drag racing', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'circuit_9': 'circuit', 'indianapolis raceway park_10': 'indianapolis raceway park'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'discipline_7': [0], 'drag racing_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'circuit_9': [2], 'indianapolis raceway park_10': [3]} | ['discipline', 'championship', 'circuit', 'event', 'session'] | [['stock car', 'sprint cup series', 'daytona international speedway', 'uno twin 125 qualifiers', 'qualifying'], ['stock car', 'whelen modified tour', 'martinsville speedway', 'winn - dixie 500', 'race'], ['drag racing', 'nhra winston drag racing series', 'indianapolis raceway park', 'mac tools us nationals', 'qualifying'], ['stock car', 'arca series', 'daytona international speedway', 'daytona arca 200', 'race'], ['open wheel', 'usac national championship', 'williams grove speedway', 'indianapolis sweepstakes', 'race']] |
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 | unique | independents were the only party to win no new seats . | {'scope': 'all', 'row': '9', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': '0', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'total elected 2001', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose total elected 2001 record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; total elected 2001 ; 0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; total elected 2001 ; 0 } }', 'tointer': 'select the rows whose total elected 2001 record is equal to 0 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'total elected 2001', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose total elected 2001 record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; total elected 2001 ; 0 }'}, 'party'], 'result': 'independents', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; total elected 2001 ; 0 } ; party }'}, 'independents'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; total elected 2001 ; 0 } ; party } ; independents }', 'tointer': 'the party record of this unqiue row is independents .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; total elected 2001 ; 0 } } ; eq { hop { filter_eq { all_rows ; total elected 2001 ; 0 } ; party } ; independents } } = true', 'tointer': 'select the rows whose total elected 2001 record is equal to 0 . there is only one such row in the table . the party record of this unqiue row is independents .'} | and { only { filter_eq { all_rows ; total elected 2001 ; 0 } } ; eq { hop { filter_eq { all_rows ; total elected 2001 ; 0 } ; party } ; independents } } = true | select the rows whose total elected 2001 record is equal to 0 . there is only one such row in the table . the party record of this unqiue row is independents . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'total elected 2001_7': 7, '0_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'party_9': 9, 'independents_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'total elected 2001_7': 'total elected 2001', '0_8': '0', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'party_9': 'party', 'independents_10': 'independents'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'total elected 2001_7': [0], '0_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'party_9': [2], 'independents_10': [3]} | ['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']] |
bermuda national cricket team | https://en.wikipedia.org/wiki/Bermuda_national_cricket_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1829476-2.html.csv | comparative | lionel cann scored a higher amount of runs over his career than dean minors scored . | {'row_1': '3', 'row_2': '5', 'col': '3', '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', 'player', 'lionel cann'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to lionel cann .', 'tostr': 'filter_eq { all_rows ; player ; lionel cann }'}, 'runs'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; lionel cann } ; runs }', 'tointer': 'select the rows whose player record fuzzily matches to lionel cann . take the runs record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'dean minors'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to dean minors .', 'tostr': 'filter_eq { all_rows ; player ; dean minors }'}, 'runs'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; dean minors } ; runs }', 'tointer': 'select the rows whose player record fuzzily matches to dean minors . take the runs record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; player ; lionel cann } ; runs } ; hop { filter_eq { all_rows ; player ; dean minors } ; runs } } = true', 'tointer': 'select the rows whose player record fuzzily matches to lionel cann . take the runs record of this row . select the rows whose player record fuzzily matches to dean minors . take the runs record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; player ; lionel cann } ; runs } ; hop { filter_eq { all_rows ; player ; dean minors } ; runs } } = true | select the rows whose player record fuzzily matches to lionel cann . take the runs record of this row . select the rows whose player record fuzzily matches to dean minors . take the runs 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, 'player_7': 7, 'lionel cann_8': 8, 'runs_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'dean minors_12': 12, 'runs_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', 'player_7': 'player', 'lionel cann_8': 'lionel cann', 'runs_9': 'runs', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'dean minors_12': 'dean minors', 'runs_13': 'runs'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'lionel cann_8': [0], 'runs_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'dean minors_12': [1], 'runs_13': [3]} | ['rank', 'player', 'runs', 'average', 'career'] | [['1', 'irving romaine', '783', '25.25', '2006 - 2009'], ['2', 'david hemp', '641', '33.73', '2006 - 2009'], ['3', 'lionel cann', '590', '26.81', '2006 - 2009'], ['4', 'janeiro tucker', '496', '19.84', '2006 - 2009'], ['5', 'dean minors', '478', '26.55', '2006 - 2007'], ['6', 'steven outerbridge', '336', '14.60', '2006 - 2009']] |
1982 pga championship | https://en.wikipedia.org/wiki/1982_PGA_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18165870-2.html.csv | aggregation | in the 1982 pga championship , total scores averaged 147.4 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '147.4', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'total'], 'result': '147.4', 'ind': 0, 'tostr': 'avg { all_rows ; total }'}, '147.4'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; total } ; 147.4 } = true', 'tointer': 'the average of the total record of all rows is 147.4 .'} | round_eq { avg { all_rows ; total } ; 147.4 } = true | the average of the total record of all rows is 147.4 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'total_4': 4, '147.4_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'total_4': 'total', '147.4_5': '147.4'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'total_4': [0], '147.4_5': [1]} | ['player', 'country', 'year ( s ) won', 'total', 'to par'] | [['dave stockton', 'united states', '1970 , 1976', '146', '+ 6'], ['gary player', 'south africa', '1962 , 1972', '146', '+ 6'], ['don january', 'united states', '1967', '146', '+ 6'], ['larry nelson', 'united states', '1981', '149', '+ 9'], ['al geiberger', 'united states', '1966', '150', '+ 10']] |
2004 - 05 toronto raptors season | https://en.wikipedia.org/wiki/2004%E2%80%9305_Toronto_Raptors_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15872814-8.html.csv | comparative | jalen rose scored more points on april 19 than he did on april 20 . | {'row_1': '10', 'row_2': '11', 'col': '5', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'april 19'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to april 19 .', 'tostr': 'filter_eq { all_rows ; date ; april 19 }'}, 'high points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; april 19 } ; high points }', 'tointer': 'select the rows whose date record fuzzily matches to april 19 . take the high points record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'april 20'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to april 20 .', 'tostr': 'filter_eq { all_rows ; date ; april 20 }'}, 'high points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; april 20 } ; high points }', 'tointer': 'select the rows whose date record fuzzily matches to april 20 . take the high points record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; date ; april 19 } ; high points } ; hop { filter_eq { all_rows ; date ; april 20 } ; high points } } = true', 'tointer': 'select the rows whose date record fuzzily matches to april 19 . take the high points record of this row . select the rows whose date record fuzzily matches to april 20 . take the high points record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; date ; april 19 } ; high points } ; hop { filter_eq { all_rows ; date ; april 20 } ; high points } } = true | select the rows whose date record fuzzily matches to april 19 . take the high points record of this row . select the rows whose date record fuzzily matches to april 20 . take the high points 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, 'april 19_8': 8, 'high points_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'date_11': 11, 'april 20_12': 12, 'high points_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', 'april 19_8': 'april 19', 'high points_9': 'high points', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', 'april 20_12': 'april 20', 'high points_13': 'high points'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'date_7': [0], 'april 19_8': [0], 'high points_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'date_11': [1], 'april 20_12': [1], 'high points_13': [3]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['72', 'april 1', 'charlotte', 'w 119 - 107 ( ot )', 'chris bosh ( 27 )', 'donyell marshall ( 12 )', 'milt palacio , morris peterson , jalen rose ( 4 )', 'charlotte coliseum 13550', '30 - 42'], ['73', 'april 3', 'detroit', 'l 103 - 113 ( ot )', 'morris peterson , jalen rose ( 22 )', 'chris bosh ( 9 )', 'jalen rose ( 5 )', 'air canada centre 19800', '30 - 43'], ['74', 'april 6', 'memphis', 'l 74 - 104 ( ot )', 'jalen rose ( 19 )', 'chris bosh ( 10 )', 'matt bonner , jalen rose ( 3 )', 'air canada centre 14964', '30 - 44'], ['75', 'april 8', 'atlanta', 'w 109 - 101 ( ot )', 'jalen rose ( 30 )', 'morris peterson ( 14 )', 'rafer alston ( 8 )', 'air canada centre 14352', '31 - 44'], ['76', 'april 9', 'chicago', 'l 97 - 110 ( ot )', 'jalen rose ( 19 )', 'chris bosh ( 9 )', 'rafer alston ( 9 )', 'united center 22281', '31 - 45'], ['77', 'april 11', 'indiana', 'l 90 - 94 ( ot )', 'jalen rose ( 26 )', 'chris bosh ( 13 )', 'rafer alston ( 9 )', 'air canada centre 15104', '31 - 46'], ['78', 'april 12', 'new york', 'w 105 - 93 ( ot )', 'chris bosh ( 29 )', 'rafer alston ( 9 )', 'rafer alston ( 7 )', 'madison square garden 18907', '32 - 46'], ['79', 'april 15', 'new jersey', 'l 90 - 101 ( ot )', 'jalen rose ( 20 )', 'morris peterson ( 8 )', 'rafer alston ( 7 )', 'air canada centre 19800', '32 - 47'], ['80', 'april 17', 'boston', 'l 98 - 103 ( ot )', 'jalen rose ( 31 )', 'chris bosh , pape sow ( 7 )', 'rafer alston ( 6 )', 'air canada centre 18797', '32 - 48'], ['81', 'april 19', 'milwaukee', 'w 127 - 109 ( ot )', 'jalen rose ( 29 )', 'pape sow ( 9 )', 'omar cook ( 10 )', 'bradley center 13947', '33 - 48'], ['82', 'april 20', 'cleveland', 'l 95 - 104 ( ot )', 'jalen rose ( 25 )', 'morris peterson ( 9 )', 'omar cook ( 9 )', 'air canada centre 19800', '33 - 49']] |
usa today all - usa high school basketball team | https://en.wikipedia.org/wiki/USA_Today_All-USA_high_school_basketball_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11677760-19.html.csv | aggregation | the median height of usa today 's all-usa high school basketball team for boys ' in '07 for the third team is 6 ' 4 " . | {'scope': 'all', 'col': '2', 'type': 'average', 'result': '6 \' 4 "', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'height'], 'result': '6 \' 4 "', 'ind': 0, 'tostr': 'avg { all_rows ; height }'}, '6 \' 4 "'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; height } ; 6 \' 4 " } = true', 'tointer': 'the average of the height record of all rows is 6 \' 4 " .'} | round_eq { avg { all_rows ; height } ; 6 ' 4 " } = true | the average of the height record of all rows is 6 ' 4 " . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'height_4': 4, '6\' 4"_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'height_4': 'height', '6\' 4"_5': '6 \' 4 "'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'height_4': [0], '6\' 4"_5': [1]} | ['player', 'height', 'school', 'hometown', 'college'] | [['anthony randolph', '6 - 10', 'woodrow wilson high school', 'dallas , tx', 'lsu'], ['nolan smith', '6 - 3', 'oak hill academy', 'washington , dc', 'duke'], ['corey fisher', '6 - 0', 'st patrick high school', 'elizabeth , nj', 'villanova'], ['nick calathes', '6 - 4', 'lake howell high school', 'winter park , fl', 'florida'], ['austin freeman', '6 - 4', 'dematha catholic high school', 'hyattsville , md', 'georgetown']] |
list of metropolitan areas in sweden | https://en.wikipedia.org/wiki/List_of_metropolitan_areas_in_Sweden | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1245658-3.html.csv | superlative | malmo has the most population in the metropolitan areas of sweden . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '1', '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', 'population'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; population }'}, 'municipality'], 'result': 'malmö', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; population } ; municipality }'}, 'malmö'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; population } ; municipality } ; malmö } = true', 'tointer': 'select the row whose population record of all rows is maximum . the municipality record of this row is malmö .'} | eq { hop { argmax { all_rows ; population } ; municipality } ; malmö } = true | select the row whose population record of all rows is maximum . the municipality record of this row is malmö . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'population_5': 5, 'municipality_6': 6, 'malmö_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'population_5': 'population', 'municipality_6': 'municipality', 'malmö_7': 'malmö'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'population_5': [0], 'municipality_6': [1], 'malmö_7': [2]} | ['municipality', 'number', 'population', 'area', 'density square'] | [['malmö', '1', '309912', '335.14', '925'], ['vellinge', '2', '33725', '143.18', '236'], ['trelleborg', '3', '42744', '342.07', '125'], ['skurup', '4', '15000', '195.17', '77'], ['svedala', '5', '20039', '218.97', '92'], ['lund', '6', '112925', '430.27', '262'], ['staffanstorp', '7', '22572', '107.61', '210'], ['burlöv', '8', '17079', '18.84', '907'], ['lomma', '9', '22415', '55.64', '403'], ['kävlinge', '10', '29513', '153.83', '192'], ['eslöv', '11', '31761', '421.66', '75'], ['höör', '12', '15591', '292.96', '53'], ['total', '12', '673276', '2715.34', '247.95']] |
1999 belarusian premier league | https://en.wikipedia.org/wiki/1999_Belarusian_Premier_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14746581-1.html.csv | aggregation | the average stadium capacity of teams in the 1999 belarusian premier league was 8561 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '8561', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'capacity'], 'result': '8561', 'ind': 0, 'tostr': 'avg { all_rows ; capacity }'}, '8561'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; capacity } ; 8561 } = true', 'tointer': 'the average of the capacity record of all rows is 8561 .'} | round_eq { avg { all_rows ; capacity } ; 8561 } = true | the average of the capacity record of all rows is 8561 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'capacity_4': 4, '8561_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'capacity_4': 'capacity', '8561_5': '8561'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'capacity_4': [0], '8561_5': [1]} | ['team', 'location', 'venue', 'capacity', 'position in 1998'] | [['dnepr - transmash', 'mogilev', 'spartak , mogilev', '11200', '1'], ['bate', 'borisov', 'city stadium , borisov', '5500', '2'], ['belshina', 'bobruisk', 'spartak , bobruisk', '3550', '3'], ['lokomotiv - 96', 'vitebsk', 'central , vitebsk', '8300', '4'], ['gomel', 'gomel', 'central , gomel', '11800', '5'], ['slavia', 'mozyr', 'yunost , mozyr', '5500', '6'], ['torpedo - maz', 'minsk', 'torpedo , minsk', '5200', '7'], ['dinamo minsk', 'minsk', 'dinamo , minsk', '41040', '8'], ['dinamo brest', 'brest', 'dinamo , brest', '10080', '9'], ['neman - belcard', 'grodno', 'neman', '6300', '10'], ['shakhtyor', 'soligorsk', 'stroitel', '5000', '11'], ['torpedo - kadino', 'mogilev', 'torpedo , mogilev', '3500', '12'], ['naftan - devon', 'novopolotsk', 'atlant', '6500', '13'], ['molodechno', 'molodechno', 'city stadium , molodechno', '5500', '14'], ['lida', 'lida', 'city stadium , lida', '4000', 'first league , 1'], ['svisloch - krovlya', 'osipovichi', 'yunost , osipovichi', '4000', 'first league , 2']] |
list of cold feet episodes | https://en.wikipedia.org/wiki/List_of_Cold_Feet_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-12919003-2.html.csv | majority | mike bullen wrote all of the the first five episodes of cold feet . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'mike bullen', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'writer', 'mike bullen'], 'result': True, 'ind': 0, 'tointer': 'for the writer records of all rows , all of them fuzzily match to mike bullen .', 'tostr': 'all_eq { all_rows ; writer ; mike bullen } = true'} | all_eq { all_rows ; writer ; mike bullen } = true | for the writer records of all rows , all of them fuzzily match to mike bullen . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'writer_3': 3, 'mike bullen_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'writer_3': 'writer', 'mike bullen_4': 'mike bullen'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'writer_3': [0], 'mike bullen_4': [0]} | ['no', 'episode', 'writer', 'director', 'viewers ( millions )', 'original airdate'] | [['1', 'episode 1', 'mike bullen', 'declan lowney', '7.47', '15 november 1998'], ['2', 'episode 2', 'mike bullen', 'declan lowney', '7.33', '22 november 1998'], ['3', 'episode 3', 'mike bullen', 'mark mylod', '7.46', '29 november 1998'], ['4', 'episode 4', 'mike bullen', 'mark mylod', '7.44', '6 december 1998'], ['5', 'episode 5', 'mike bullen', 'nigel cole', '7.91', '13 december 1998']] |
list of state leaders in the 20th century bc | https://en.wikipedia.org/wiki/List_of_state_leaders_in_the_20th_century_BC | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17606888-1.html.csv | majority | all the state leaders in the 20th century bc were sovereign leaders . | {'scope': 'all', 'col': '1', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'sovereign', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'type', 'sovereign'], 'result': True, 'ind': 0, 'tointer': 'for the type records of all rows , all of them fuzzily match to sovereign .', 'tostr': 'all_eq { all_rows ; type ; sovereign } = true'} | all_eq { all_rows ; type ; sovereign } = true | for the type records of all rows , all of them fuzzily match to sovereign . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'type_3': 3, 'sovereign_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'type_3': 'type', 'sovereign_4': 'sovereign'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'type_3': [0], 'sovereign_4': [0]} | ['type', 'name', 'title', 'royal house', 'from'] | [['sovereign', 'mentuhotep ii', 'pharaoh', 'eleventh dynasty', '2010 bc'], ['sovereign', 'mentuhotep iv', 'pharaoh', 'eleventh dynasty', '1998 bc or 1997 bc'], ['sovereign', 'amenemhat i', 'pharaoh', 'twelfth dynasty', '1991 bc'], ['sovereign', 'senusret i', 'pharaoh', 'twelfth dynasty', '1971 bc'], ['sovereign', 'amenemhat ii', 'pharaoh', 'twelfth dynasty', '1929 bc']] |
1939 vfl season | https://en.wikipedia.org/wiki/1939_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10806852-6.html.csv | majority | most of the games played in round 6 of the 1939 vfl season hand an audience of over 14,000 . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '14,000', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'crowd', '14,000'], 'result': True, 'ind': 0, 'tointer': 'for the crowd records of all rows , most of them are greater than 14,000 .', 'tostr': 'most_greater { all_rows ; crowd ; 14,000 } = true'} | most_greater { all_rows ; crowd ; 14,000 } = true | for the crowd records of all rows , most of them are greater than 14,000 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'crowd_3': 3, '14,000_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'crowd_3': 'crowd', '14,000_4': '14,000'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'crowd_3': [0], '14,000_4': [0]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['melbourne', '19.23 ( 137 )', 'south melbourne', '3.12 ( 30 )', 'mcg', '16523', '27 may 1939'], ['collingwood', '14.14 ( 98 )', 'hawthorn', '12.7 ( 79 )', 'victoria park', '15000', '27 may 1939'], ['carlton', '8.13 ( 61 )', 'richmond', '9.14 ( 68 )', 'princes park', '34000', '27 may 1939'], ['st kilda', '16.18 ( 114 )', 'geelong', '10.16 ( 76 )', 'junction oval', '17000', '27 may 1939'], ['footscray', '11.13 ( 79 )', 'fitzroy', '15.10 ( 100 )', 'western oval', '13000', '27 may 1939'], ['north melbourne', '15.11 ( 101 )', 'essendon', '13.10 ( 88 )', 'arden street oval', '14500', '27 may 1939']] |
1955 washington redskins season | https://en.wikipedia.org/wiki/1955_Washington_Redskins_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15123196-1.html.csv | majority | in the 1955 washington redskins season they lost the majority of games in october . | {'scope': 'subset', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'l', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'october'}} | {'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'october'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; october }', 'tointer': 'select the rows whose date record fuzzily matches to october .'}, 'result', 'l'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to october . for the result records of these rows , most of them fuzzily match to l .', 'tostr': 'most_eq { filter_eq { all_rows ; date ; october } ; result ; l } = true'} | most_eq { filter_eq { all_rows ; date ; october } ; result ; l } = true | select the rows whose date record fuzzily matches to october . for the result records of these rows , most of them fuzzily match to l . | 2 | 2 | {'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'date_4': 4, 'october_5': 5, 'result_6': 6, 'l_7': 7} | {'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'date_4': 'date', 'october_5': 'october', 'result_6': 'result', 'l_7': 'l'} | {'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'date_4': [0], 'october_5': [0], 'result_6': [1], 'l_7': [1]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 25 , 1955', 'cleveland browns', 'w 27 - 17', '30041'], ['2', 'october 1 , 1955', 'philadelphia eagles', 'w 31 - 30', '31891'], ['3', 'october 9 , 1955', 'chicago cardinals', 'l 24 - 10', '26337'], ['4', 'october 16 , 1955', 'cleveland browns', 'l 24 - 14', '29168'], ['5', 'october 23 , 1955', 'baltimore colts', 'w 14 - 13', '51387'], ['6', 'october 30 , 1955', 'new york giants', 'l 35 - 7', '17402'], ['7', 'november 6 , 1955', 'philadelphia eagles', 'w 34 - 21', '25741'], ['8', 'november 13 , 1955', 'san francisco 49ers', 'w 7 - 0', '25112'], ['9', 'november 20 , 1955', 'chicago cardinals', 'w 31 - 0', '16901'], ['10', 'november 27 , 1955', 'pittsburgh steelers', 'w 23 - 14', '21760'], ['11', 'december 4 , 1955', 'new york giants', 'l 27 - 20', '28556'], ['12', 'december 11 , 1955', 'pittsburgh steelers', 'w 28 - 17', '20547']] |
2010 fei world equestrian games | https://en.wikipedia.org/wiki/2010_FEI_World_Equestrian_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11661065-10.html.csv | superlative | the great britain had the most gold in the fei world equestrian games of 2010 . | {'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': 'great britain', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; gold } ; nation }'}, 'great britain'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; gold } ; nation } ; great britain } = true', 'tointer': 'select the row whose gold record of all rows is maximum . the nation record of this row is great britain .'} | eq { hop { argmax { all_rows ; gold } ; nation } ; great britain } = true | select the row whose gold record of all rows is maximum . the nation record of this row is great britain . | 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, 'great britain_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', 'great britain_7': 'great britain'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'gold_5': [0], 'nation_6': [1], 'great britain_7': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'great britain', '9', '7', '3', '19'], ['2', 'germany', '5', '5', '4', '14'], ['3', 'netherlands', '5', '3', '1', '9'], ['4', 'united states of america', '3', '2', '3', '8'], ['5', 'belgium', '1', '2', '1', '4'], ['6', 'united arab emirates', '1', '1', '1', '3'], ['7', 'australia', '1', '0', '2', '3'], ['8', 'spain', '1', '0', '0', '1'], ['8', 'switzerland', '1', '0', '0', '1'], ['10', 'denmark', '0', '3', '3', '6'], ['11', 'france', '0', '2', '1', '3'], ['12', 'canada', '0', '1', '2', '3'], ['13', 'saudi arabia', '0', '1', '0', '1'], ['14', 'new zealand', '0', '0', '2', '2'], ['15', 'austria', '0', '0', '1', '1'], ['15', 'finland', '0', '0', '1', '1'], ['15', 'italy', '0', '0', '1', '1'], ['15', 'norway', '0', '0', '1', '1'], ['total', 'total', '27', '27', '27', '81']] |
zina garrison | https://en.wikipedia.org/wiki/Zina_Garrison | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1028356-3.html.csv | majority | in most of the championships , zina garrison 's partner was sherwood stewart . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'sherwood stewart', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'partner', 'sherwood stewart'], 'result': True, 'ind': 0, 'tointer': 'for the partner records of all rows , most of them fuzzily match to sherwood stewart .', 'tostr': 'most_eq { all_rows ; partner ; sherwood stewart } = true'} | most_eq { all_rows ; partner ; sherwood stewart } = true | for the partner records of all rows , most of them fuzzily match to sherwood stewart . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'partner_3': 3, 'sherwood stewart_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'partner_3': 'partner', 'sherwood stewart_4': 'sherwood stewart'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'partner_3': [0], 'sherwood stewart_4': [0]} | ['outcome', 'year', 'championship', 'surface', 'partner', 'opponents', 'score'] | [['winner', '1987', 'australian open', 'grass', 'sherwood stewart', 'anne hobbs andrew castle', '3 - 6 , 7 - 6 ( 5 ) , 6 - 3'], ['winner', '1988', 'wimbledon', 'grass', 'sherwood stewart', 'gretchen magers kelly jones', '6 - 1 , 7 - 6 ( 3 )'], ['runner - up', '1989', 'australian open', 'hard', 'sherwood stewart', 'jana novotná jim pugh', '6 - 3 , 6 - 4'], ['runner - up', '1990', 'australian open', 'hard', 'jim pugh', 'natasha zvereva andrew castle', '4 - 6 , 6 - 2 , 6 - 3'], ['winner', '1990', 'wimbledon ( 2 )', 'grass', 'rick leach', 'elizabeth smylie john fitzgerald', '7 - 5 , 6 - 2']] |
jorge lozano | https://en.wikipedia.org/wiki/Jorge_Lozano | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11435084-1.html.csv | majority | jorge lozano partnered with todd witsken for the majority of his tournaments . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'todd witsken', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'partnering', 'todd witsken'], 'result': True, 'ind': 0, 'tointer': 'for the partnering records of all rows , most of them fuzzily match to todd witsken .', 'tostr': 'most_eq { all_rows ; partnering ; todd witsken } = true'} | most_eq { all_rows ; partnering ; todd witsken } = true | for the partnering records of all rows , most of them fuzzily match to todd witsken . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'partnering_3': 3, 'todd witsken_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'partnering_3': 'partnering', 'todd witsken_4': 'todd witsken'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'partnering_3': [0], 'todd witsken_4': [0]} | ['date', 'tournament', 'surface', 'partnering', 'opponents in the final', 'score'] | [['2 may 1988', 'forest hills , new york , united states', 'clay', 'todd witsken', 'pieter aldrich danie visser', '6 - 3 , 7 - 6'], ['9 may 1988', 'rome , italy', 'clay', 'todd witsken', 'anders järryd tomáš šmíd', '6 - 3 , 6 - 3'], ['4 july 1988', 'boston , massachusetts , united states', 'clay', 'todd witsken', 'bruno orešar jaime yzaga', '6 - 2 , 7 - 5'], ['25 july 1988', 'stratton mountain , vermont , united states', 'hard', 'todd witsken', 'pieter aldrich danie visser', '6 - 3 , 7 - 6'], ['10 april 1989', 'rio de janeiro , brazil', 'carpet', 'todd witsken', 'patrick mcenroe tim wilkison', '2 - 6 , 6 - 4 , 6 - 4'], ['6 november 1989', 'stockholm , sweden', 'carpet', 'todd witsken', 'rick leach jim pugh', '6 - 3 , 5 - 7 , 6 - 3'], ['26 february 1990', 'rotterdam , netherlands', 'carpet', 'leonardo lavalle', 'diego nargiso nicolás pereira', '6 - 3 , 7 - 6'], ['16 march 1992', 'casablanca , morocco', 'clay', 'horacio de la peña', 'ģirts dzelde t j middleton', '2 - 6 , 6 - 4 , 7 - 6'], ['4 october 1993', 'athens , greece', 'clay', 'horacio de la peña', 'royce deppe john sullivan', '3 - 6 , 6 - 1 , 6 - 2']] |
ana jovanović | https://en.wikipedia.org/wiki/Ana_Jovanovi%C4%87 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12326046-2.html.csv | ordinal | the match against aurelija miseviciute was ana jovanović 's earliest career tournament game . | {'row': '1', 'col': '2', 'order': '1', 'col_other': '5', '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 }'}, 'opponent'], 'result': 'aurelija miseviciute', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; date ; 1 } ; opponent }'}, 'aurelija miseviciute'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; date ; 1 } ; opponent } ; aurelija miseviciute } = true', 'tointer': 'select the row whose date record of all rows is 1st minimum . the opponent record of this row is aurelija miseviciute .'} | eq { hop { nth_argmin { all_rows ; date ; 1 } ; opponent } ; aurelija miseviciute } = true | select the row whose date record of all rows is 1st minimum . the opponent record of this row is aurelija miseviciute . | 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, 'opponent_7': 7, 'aurelija miseviciute_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', 'opponent_7': 'opponent', 'aurelija miseviciute_8': 'aurelija miseviciute'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], '1_6': [0], 'opponent_7': [1], 'aurelija miseviciute_8': [2]} | ['outcome', 'date', 'tournament', 'surface', 'opponent', 'score'] | [['winner', '13 october 2002', 'ain alsouknha', 'clay', 'aurelija miseviciute', '6 - 4 , 6 - 1'], ['winner', '27 october 2002', 'al mansoura', 'clay', 'ema janašková', '4 - 6 , 6 - 3 , 6 - 2'], ['winner', '4 july 2004', 'bibione', 'clay', 'sabrina jolk', '6 - 3 , 6 - 3'], ['ru', '27 march 2005', 'rome', 'clay', 'romina oprandi', '4 - 6 , 6 ( 4 ) - 7'], ['ru', '24 july 2005', 'palić', 'clay', 'miljana adanko', '5 - 7 , 1 - 6'], ['not played', '30 april 2006', 'herceg novi', 'clay', 'zorica petrov', 'np'], ['winner', '14 may 2006', 'mostar', 'clay', 'ani mijačika', '6 - 2 , 6 - 4'], ['winner', '25 march 2007', 'athens', 'hard', 'neuza silva', '6 - 3 , 4 - 6 , 6 - 3'], ['winner', '24 june 2007', 'sarajevo', 'clay', 'davinia lobbinger', '6 - 4 , 6 - 4'], ['winner', '5 august 2007', 'bad saulgau', 'clay', 'kathrin wörle', '7 - 5 , 4 - 6 , 7 - 5'], ['ru', '7 june 2009', 'sarajevo', 'clay', 'ivana lisjak', '0 - 6 , 6 ( 10 ) - 7'], ['ru', '2 august 2009', 'bad saulgau', 'clay', 'andrea hlaváčková', '4 - 6 , 4 - 6'], ['ru', '22 november 2009', 'opole', 'carpet ( i )', 'sandra záhlavová', '0 - 6 , 2 - 6']] |
list of schools in the bay of plenty region | https://en.wikipedia.org/wiki/List_of_schools_in_the_Bay_of_Plenty_Region | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12174210-5.html.csv | majority | all of the schools are under the authority of the state . | {'scope': 'all', 'col': '5', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'state', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'authority', 'state'], 'result': True, 'ind': 0, 'tointer': 'for the authority records of all rows , all of them fuzzily match to state .', 'tostr': 'all_eq { all_rows ; authority ; state } = true'} | all_eq { all_rows ; authority ; state } = true | for the authority records of all rows , all of them fuzzily match to state . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'authority_3': 3, 'state_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'authority_3': 'authority', 'state_4': 'state'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'authority_3': [0], 'state_4': [0]} | ['name', 'years', 'gender', 'area', 'authority', 'decile'] | [['kawerau putauaki school', '1 - 8', 'coed', 'kawerau', 'state', '1'], ['kawerau south school', '1 - 6', 'coed', 'kawerau', 'state', '1'], ['kawerau teen parent unit', '-', '-', 'kawerau', 'state', '1'], ['tarawera high school', '7 - 13', 'coed', 'kawerau', 'state', '1'], ['te whata tau o putauaki', '1 - 8', 'coed', 'kawerau', 'state', '1']] |
1940 vfl season | https://en.wikipedia.org/wiki/1940_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10807253-5.html.csv | superlative | corio oval was the first venue to be used during the 1940 vfl season . | {'scope': 'all', 'col_superlative': '7', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '5', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'date'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; date }'}, 'venue'], 'result': 'corio oval', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; date } ; venue }'}, 'corio oval'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; date } ; venue } ; corio oval } = true', 'tointer': 'select the row whose date record of all rows is minimum . the venue record of this row is corio oval .'} | eq { hop { argmin { all_rows ; date } ; venue } ; corio oval } = true | select the row whose date record of all rows is minimum . the venue record of this row is corio oval . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, 'venue_6': 6, 'corio oval_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'date_5': 'date', 'venue_6': 'venue', 'corio oval_7': 'corio oval'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], 'venue_6': [1], 'corio oval_7': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['geelong', '12.21 ( 93 )', 'south melbourne', '10.12 ( 72 )', 'corio oval', '5000', '25 may 1940'], ['fitzroy', '8.14 ( 62 )', 'richmond', '12.11 ( 83 )', 'brunswick street oval', '14000', '25 may 1940'], ['essendon', '12.18 ( 90 )', 'hawthorn', '9.19 ( 73 )', 'windy hill', '12000', '25 may 1940'], ['north melbourne', '7.11 ( 53 )', 'footscray', '9.12 ( 66 )', 'arden street oval', '13000', '25 may 1940'], ['melbourne', '17.12 ( 114 )', 'collingwood', '13.20 ( 98 )', 'mcg', '20043', '25 may 1940'], ['st kilda', '6.18 ( 54 )', 'carlton', '11.19 ( 85 )', 'junction oval', '21000', '25 may 1940']] |
list of cities in the far east by population | https://en.wikipedia.org/wiki/List_of_cities_in_the_Far_East_by_population | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16478687-2.html.csv | comparative | the population of jakarta is larger than the population of beijing . | {'row_1': '4', 'row_2': '7', 'col': '4', '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', 'metropolitan area', 'jakarta'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose metropolitan area record fuzzily matches to jakarta .', 'tostr': 'filter_eq { all_rows ; metropolitan area ; jakarta }'}, 'population'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; metropolitan area ; jakarta } ; population }', 'tointer': 'select the rows whose metropolitan area record fuzzily matches to jakarta . take the population record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'metropolitan area', 'beijing'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose metropolitan area record fuzzily matches to beijing .', 'tostr': 'filter_eq { all_rows ; metropolitan area ; beijing }'}, 'population'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; metropolitan area ; beijing } ; population }', 'tointer': 'select the rows whose metropolitan area record fuzzily matches to beijing . take the population record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; metropolitan area ; jakarta } ; population } ; hop { filter_eq { all_rows ; metropolitan area ; beijing } ; population } } = true', 'tointer': 'select the rows whose metropolitan area record fuzzily matches to jakarta . take the population record of this row . select the rows whose metropolitan area record fuzzily matches to beijing . take the population record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; metropolitan area ; jakarta } ; population } ; hop { filter_eq { all_rows ; metropolitan area ; beijing } ; population } } = true | select the rows whose metropolitan area record fuzzily matches to jakarta . take the population record of this row . select the rows whose metropolitan area record fuzzily matches to beijing . take the population 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, 'metropolitan area_7': 7, 'jakarta_8': 8, 'population_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'metropolitan area_11': 11, 'beijing_12': 12, 'population_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', 'metropolitan area_7': 'metropolitan area', 'jakarta_8': 'jakarta', 'population_9': 'population', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'metropolitan area_11': 'metropolitan area', 'beijing_12': 'beijing', 'population_13': 'population'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'metropolitan area_7': [0], 'jakarta_8': [0], 'population_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'metropolitan area_11': [1], 'beijing_12': [1], 'population_13': [3]} | ['rank', 'metropolitan area', 'country', 'population', 'area ( km square )', 'population density ( people / km square )'] | [['1', 'tokyo', 'japan', '32450000', '8014', '4049'], ['2', 'seoul', 'south korea', '20550000', '5076', '4048'], ['3', 'mumbai ( bombay )', 'india', '20900000', '8100', '7706'], ['4', 'jakarta', 'indonesia', '18900000', '5100', '3706'], ['5', 'shanghai', 'china', '16650000', '5177', '3216'], ['7', 'hong kong - shenzhen', 'hong kong china', '15800000', '3051', '5179'], ['8', 'beijing', 'china', '12500000', '6562', '1905']] |
rousimar palhares | https://en.wikipedia.org/wiki/Rousimar_Palhares | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17440284-2.html.csv | count | seven of the matches took place in the united states . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'united states', 'result': '7', 'col': '8', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to united states .', 'tostr': 'filter_eq { all_rows ; location ; united states }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location ; united states } }', 'tointer': 'select the rows whose location record fuzzily matches to united states . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location ; united states } } ; 7 } = true', 'tointer': 'select the rows whose location record fuzzily matches to united states . the number of such rows is 7 .'} | eq { count { filter_eq { all_rows ; location ; united states } } ; 7 } = true | select the rows whose location record fuzzily matches to united states . the number of such rows is 7 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'location_5': 5, 'united states_6': 6, '7_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', 'united states_6': 'united states', '7_7': '7'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location_5': [0], 'united states_6': [0], '7_7': [2]} | ['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location'] | [['win', '15 - 5', 'mike pierce', 'submission ( heel hook )', 'ufc fight night : maia vs shields', '1', '0:31', 'barueri , são paulo , brazil'], ['loss', '14 - 5', 'hector lombard', 'ko ( punches )', 'ufc on fx : sotiropoulos vs pearson', '1', '3:38', 'gold coast , queensland , australia'], ['loss', '14 - 4', 'alan belcher', 'tko ( punches & elbows )', 'ufc on fox : diaz vs miller', '1', '4:18', 'east rutherford , new jersey , united states'], ['win', '14 - 3', 'mike massenzio', 'submission ( heel hook )', 'ufc 142', '1', '1:03', 'rio de janeiro , rio de janeiro , brazil'], ['win', '13 - 3', 'dan miller', 'decision ( unanimous )', 'ufc 134', '3', '5:00', 'rio de janeiro , rio de janeiro , brazil'], ['win', '12 - 3', 'david branch', 'submission ( kneebar )', 'ufc live : sanchez vs kampmann', '2', '1:44', 'louisville , kentucky , united states'], ['loss', '11 - 3', 'nate marquardt', 'tko ( punches )', 'ufc fight night : marquardt vs palhares', '1', '3:28', 'austin , texas , united states'], ['win', '11 - 2', 'tomasz drwal', 'submission ( heel hook )', 'ufc 111', '1', '0:45', 'newark , new jersey , united states'], ['win', '10 - 2', 'lucio linhares', 'submission ( heel hook )', 'ufc 107', '2', '3:21', 'memphis , tennessee , united states'], ['win', '9 - 2', 'jeremy horn', 'decision ( unanimous )', 'ufc 93', '3', '5:00', 'dublin , ireland'], ['loss', '8 - 2', 'dan henderson', 'decision ( unanimous )', 'ufc 88', '3', '5:00', 'atlanta , georgia , united states'], ['win', '8 - 1', 'ivan salaverry', 'submission ( armbar )', 'ufc 84', '1', '2:36', 'las vegas , nevada , united states'], ['win', '7 - 1', 'daniel acacio', 'submission ( heel hook )', 'fury fc 5 : final conflict', '1', '1:22', 'são paulo , brazil'], ['win', '6 - 1', 'fabio nascimento', 'submission ( heel hook )', 'fury fc 5 : final conflict', '1', '2:45', 'são paulo , brazil'], ['win', '5 - 1', 'flavio luiz moura', 'submission ( heel hook )', 'fury fc 4 : high voltage', '1', '1:21', 'teresopolis , brazil'], ['win', '4 - 1', 'helio dipp', 'submission ( rear naked choke )', 'floripa fight 3', '1', '1:40', 'florianópolis , brazil'], ['win', '3 - 1', 'claudio mattos', 'tko ( injury )', 'storm samurai 12', '1', '4:58', 'curitiba , brazil'], ['loss', '2 - 1', 'arthur cesar jacintho', 'decision ( split )', 'rio mma challenger 2', '3', '5:00', 'rio de janeiro , brazil'], ['win', '2 - 0', 'renan moraes', 'submission ( armbar )', 'gold fighters championship 1', '1', 'n / a', 'rio de janeiro , brazil'], ['win', '1 - 0', 'bruno bastos', 'decision ( split )', 'floripa fight 2', '3', '5:00', 'florianópolis , brazil']] |
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