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stateless ( band ) | https://en.wikipedia.org/wiki/Stateless_%28band%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11816737-3.html.csv | majority | for the band stateless , in 2007 , their label was always ! k7 . | {'scope': 'subset', 'col': '4', 'most_or_all': 'all', 'criterion': 'equal', 'value': '! k7', 'subset': {'col': '1', 'criterion': 'equal', 'value': '2007'}} | {'func': 'all_str_eq', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year', '2007'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; year ; 2007 }', 'tointer': 'select the rows whose year record is equal to 2007 .'}, 'label', '! k7'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose year record is equal to 2007 . for the label records of these rows , all of them fuzzily match to ! k7 .', 'tostr': 'all_eq { filter_eq { all_rows ; year ; 2007 } ; label ; ! k7 } = true'} | all_eq { filter_eq { all_rows ; year ; 2007 } ; label ; ! k7 } = true | select the rows whose year record is equal to 2007 . for the label records of these rows , all of them fuzzily match to ! k7 . | 2 | 2 | {'all_str_eq_1': 1, 'result_2': 2, 'filter_eq_0': 0, 'all_rows_3': 3, 'year_4': 4, '2007_5': 5, 'label_6': 6, '!k7_7': 7} | {'all_str_eq_1': 'all_str_eq', 'result_2': 'true', 'filter_eq_0': 'filter_eq', 'all_rows_3': 'all_rows', 'year_4': 'year', '2007_5': '2007', 'label_6': 'label', '!k7_7': '! k7'} | {'all_str_eq_1': [2], 'result_2': [], 'filter_eq_0': [1], 'all_rows_3': [0], 'year_4': [0], '2007_5': [0], 'label_6': [1], '!k7_7': [1]} | ['year', 'date', 'album', 'label', 'format ( s )'] | [['2004', '5 april', 'stateless', 'sony music', '7 , cds'], ['2007', '14 may', 'stateless', '! k7', '7'], ['2007', '30 july', 'stateless', '! k7', '12 , cds'], ['2007', '29 october', 'stateless', '! k7', '12 , cds'], ['2008', '20 july', 'single - only', 'first word excursions', '7'], ['2010', '22 november', 'matilda', 'ninja tune', 'digital'], ['2011', '14 february', 'matilda', 'ninja tune', 'digital']] |
2008 issf world cup final ( rifle and pistol ) | https://en.wikipedia.org/wiki/2008_ISSF_World_Cup_Final_%28rifle_and_pistol%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18191407-10.html.csv | superlative | in the 2008 issf world cup final ( rifle and pistol ) the shooter with the most rank points at the wc rio de janeiro event was leuris pupo with 10 points . | {'scope': 'subset', 'col_superlative': '3', 'row_superlative': '5', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '1,2', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'wc rio de janeiro'}} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'event', 'wc rio de janeiro'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; event ; wc rio de janeiro }', 'tointer': 'select the rows whose event record fuzzily matches to wc rio de janeiro .'}, 'rank points'], 'result': '10', 'ind': 1, 'tostr': 'max { filter_eq { all_rows ; event ; wc rio de janeiro } ; rank points }', 'tointer': 'select the rows whose event record fuzzily matches to wc rio de janeiro . the maximum rank points record of these rows is 10 .'}, '10'], 'result': True, 'ind': 2, 'tostr': 'eq { max { filter_eq { all_rows ; event ; wc rio de janeiro } ; rank points } ; 10 }', 'tointer': 'select the rows whose event record fuzzily matches to wc rio de janeiro . the maximum rank points record of these rows is 10 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'event', 'wc rio de janeiro'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; event ; wc rio de janeiro }', 'tointer': 'select the rows whose event record fuzzily matches to wc rio de janeiro .'}, 'rank points'], 'result': None, 'ind': 3, 'tostr': 'argmax { filter_eq { all_rows ; event ; wc rio de janeiro } ; rank points }'}, 'shooter'], 'result': 'leuris pupo ( cub )', 'ind': 4, 'tostr': 'hop { argmax { filter_eq { all_rows ; event ; wc rio de janeiro } ; rank points } ; shooter }'}, 'leuris pupo ( cub )'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { argmax { filter_eq { all_rows ; event ; wc rio de janeiro } ; rank points } ; shooter } ; leuris pupo ( cub ) }', 'tointer': 'the shooter record of the row with superlative rank points record is leuris pupo ( cub ) .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { max { filter_eq { all_rows ; event ; wc rio de janeiro } ; rank points } ; 10 } ; eq { hop { argmax { filter_eq { all_rows ; event ; wc rio de janeiro } ; rank points } ; shooter } ; leuris pupo ( cub ) } } = true', 'tointer': 'select the rows whose event record fuzzily matches to wc rio de janeiro . the maximum rank points record of these rows is 10 . the shooter record of the row with superlative rank points record is leuris pupo ( cub ) .'} | and { eq { max { filter_eq { all_rows ; event ; wc rio de janeiro } ; rank points } ; 10 } ; eq { hop { argmax { filter_eq { all_rows ; event ; wc rio de janeiro } ; rank points } ; shooter } ; leuris pupo ( cub ) } } = true | select the rows whose event record fuzzily matches to wc rio de janeiro . the maximum rank points record of these rows is 10 . the shooter record of the row with superlative rank points record is leuris pupo ( cub ) . | 8 | 7 | {'and_6': 6, 'result_7': 7, 'eq_2': 2, 'max_1': 1, 'filter_str_eq_0': 0, 'all_rows_8': 8, 'event_9': 9, 'wc rio de janeiro_10': 10, 'rank points_11': 11, '10_12': 12, 'str_eq_5': 5, 'str_hop_4': 4, 'argmax_3': 3, 'rank points_13': 13, 'shooter_14': 14, 'leuris pupo ( cub )_15': 15} | {'and_6': 'and', 'result_7': 'true', 'eq_2': 'eq', 'max_1': 'max', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_8': 'all_rows', 'event_9': 'event', 'wc rio de janeiro_10': 'wc rio de janeiro', 'rank points_11': 'rank points', '10_12': '10', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'argmax_3': 'argmax', 'rank points_13': 'rank points', 'shooter_14': 'shooter', 'leuris pupo ( cub )_15': 'leuris pupo ( cub )'} | {'and_6': [7], 'result_7': [], 'eq_2': [6], 'max_1': [2], 'filter_str_eq_0': [1, 3], 'all_rows_8': [0], 'event_9': [0], 'wc rio de janeiro_10': [0], 'rank points_11': [1], '10_12': [2], 'str_eq_5': [6], 'str_hop_4': [5], 'argmax_3': [4], 'rank points_13': [3], 'shooter_14': [4], 'leuris pupo ( cub )_15': [5]} | ['shooter', 'event', 'rank points', 'score points', 'total'] | [['ralf schumann ( ger )', 'wcf 2007', 'defending champion', 'defending champion', 'defending champion'], ['oleksandr petriv ( ukr )', 'og beijing', 'olympic gold medalist', 'olympic gold medalist', 'olympic gold medalist'], ['christian reitz ( ger )', 'og beijing', 'olympic bronze medalist', 'olympic bronze medalist', 'olympic bronze medalist'], ['sergei alifirenko ( rus )', 'wc beijing', '15', '10', '25'], ['leuris pupo ( cub )', 'wc rio de janeiro', '10', '11', '21'], ['iulian raicea ( rou )', 'wc rio de janeiro', '8', '9', '17'], ['ivan stoukachev ( rus )', 'wc milan', '8', '7', '15'], ['zhang penghui ( chn )', 'wc beijing', '8', '6', '14'], ['renã vogn ( den )', 'wc munich', '4', '7', '11'], ['jorge llames ( esp )', 'wc milan', '5', '6', '11'], ['josef fiala ( cze )', 'wc munich', '5', '6', '11']] |
1895 ahac season | https://en.wikipedia.org/wiki/1895_AHAC_Season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11756240-1.html.csv | aggregation | in the 1895 ahac season , the average number of games played was 7.6 . | {'scope': 'all', 'col': '2', 'type': 'average', 'result': '7.6', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'games played'], 'result': '7.6', 'ind': 0, 'tostr': 'avg { all_rows ; games played }'}, '7.6'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; games played } ; 7.6 } = true', 'tointer': 'the average of the games played record of all rows is 7.6 .'} | round_eq { avg { all_rows ; games played } ; 7.6 } = true | the average of the games played record of all rows is 7.6 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'games played_4': 4, '7.6_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'games played_4': 'games played', '7.6_5': '7.6'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'games played_4': [0], '7.6_5': [1]} | ['team', 'games played', 'wins', 'losses', 'ties', 'goals for', 'goals against'] | [['montreal victorias', '8', '6', '2', '0', '35', '20'], ['montreal hockey club', '8', '4', '4', '0', '33', '22'], ['ottawa', '8', '4', '4', '0', '25', '24'], ['montreal crystals', '7', '3', '4', '0', '21', '39'], ['quebec', '7', '2', '5', '0', '18', '27']] |
seattle supersonics all - time roster | https://en.wikipedia.org/wiki/Seattle_SuperSonics_all-time_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16772687-16.html.csv | count | united states has a total of 8 players in saettle supersonics all-time roster . | {'scope': 'all', 'criterion': 'equal', 'value': 'united states', 'result': '8', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to united states .', 'tostr': 'filter_eq { all_rows ; nationality ; united states }'}], 'result': '8', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; nationality ; united states } }', 'tointer': 'select the rows whose nationality record fuzzily matches to united states . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; nationality ; united states } } ; 8 } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to united states . the number of such rows is 8 .'} | eq { count { filter_eq { all_rows ; nationality ; united states } } ; 8 } = true | select the rows whose nationality record fuzzily matches to united states . the number of such rows is 8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'nationality_5': 5, 'united states_6': 6, '8_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'nationality_5': 'nationality', 'united states_6': 'united states', '8_7': '8'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'nationality_5': [0], 'united states_6': [0], '8_7': [2]} | ['player', 'nationality', 'jersey number ( s )', 'position', 'years', 'from'] | [['gerald paddio', 'united states', '21', 'sf / sg', '1992 - 1993', 'unlv'], ['ruben patterson', 'united states', '21', 'sf', '1999 - 2001', 'cincinnati'], ['gary payton', 'united states', '2 , 20', 'guard', '1990 - 2003', 'oregon state'], ['sam perkins', 'united states', '14', 'c', '1993 - 1998', 'north carolina'], ['chuck person', 'united states', '45', 'sf', '1999 - 2000', 'auburn'], ['johan petro', 'france', '27', 'c', '2005 - 2007', 'pau - orthez'], ['mike phelps', 'united states', '25', 'sg', '1986 - 1987', 'alcorn state'], ['ricky pierce', 'united states', '21 , 22', 'sg / sf', '1991 - 1994', 'rice'], ['olden polynice', 'haiti', '23 , 0', 'c', '1988 - 1991 1999', 'virginia'], ['david pope', 'united states', '51', 'f', '1986', 'norfolk state'], ['vitaly potapenko', 'ukraine', '9', 'c', '2003 - 2006', 'wright state']] |
list of manila broadcasting company stations | https://en.wikipedia.org/wiki/List_of_Manila_Broadcasting_Company_stations | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28794440-1.html.csv | majority | most branding of the manila broadcasting company stations has the relay station type . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'relay', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'station type', 'relay'], 'result': True, 'ind': 0, 'tointer': 'for the station type records of all rows , most of them fuzzily match to relay .', 'tostr': 'most_eq { all_rows ; station type ; relay } = true'} | most_eq { all_rows ; station type ; relay } = true | for the station type records of all rows , most of them fuzzily match to relay . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'station type_3': 3, 'relay_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'station type_3': 'station type', 'relay_4': 'relay'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'station type_3': [0], 'relay_4': [0]} | ['branding', 'callsign', 'frequency', 'power ( kw )', 'station type', 'location'] | [['dzrh', 'dzrh', '666khz', '50 kw', 'originating', 'metro manila'], ['dzrh laoag', 'dzmt', '990khz', '5 kw', 'relay', 'laoag'], ['dzrh dagupan', 'dwdh', '1440khz', '10 kw', 'relay', 'dagupan'], ['dzrh tuguegarao', 'dwrh', '576khz', '5 kw', 'relay', 'tuguegarao'], ['dzrh isabela', 'dwrh', '828khz', '5 kw', 'relay', 'santiago , isabela'], ['dzrh lucena', 'dwsr', '1224khz', '5 kw', 'relay', 'lucena'], ['dzrh palawan', 'dyph', '693khz', '10 kw', 'relay', 'puerto princesa'], ['dzrh naga', 'dwmt', '981khz', '5 kw', 'relay', 'naga'], ['dzrh sorsogon', 'dzzh', '1287khz', '5 kw', 'relay', 'sorsogon'], ['dzrh kalibo', 'dykx', '693khz', '1 kw', 'relay', 'kalibo , aklan'], ['dzrh iloilo', 'dydh', '1485khz', '5 kw', 'relay', 'iloilo'], ['dzrh bacolod', 'dybh', '1080khz', '5 kw', 'relay', 'bacolod'], ['dzrh cebu', 'dyrh', '1395khz', '10 kw', 'relay', 'cebu'], ['dzrh tacloban', 'dyth', '990khz', '5 kw', 'relay', 'tacloban'], ['dzrh zamboanga', 'dxzh', '855khz', '5 kw', 'relay', 'zamboanga'], ['dzrh cagayan de oro', 'dxkh', '972khz', '5 kw', 'relay', 'cagayan de oro'], ['dzrh davao', 'dxrf', '1260khz', '10 kw', 'relay', 'davao'], ['dzrh general santos', 'dxgh', '531khz', '5 kw', 'relay', 'general santos'], ['dzrh bislig', 'dxrh', '1035khz', '1 kw', 'relay', 'bislig , surigao del sur']] |
elvis ' gold records volume 5 | https://en.wikipedia.org/wiki/Elvis%27_Gold_Records_Volume_5 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15582798-3.html.csv | superlative | if you talk in your sleep is the shortest track in the entire album , with a length of 2:34 . | {'scope': 'all', 'col_superlative': '7', 'row_superlative': '2', 'value_mentioned': 'yes', 'max_or_min': 'min', 'other_col': '5', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'min', 'args': ['all_rows', 'time'], 'result': '2:34', 'ind': 0, 'tostr': 'min { all_rows ; time }', 'tointer': 'the minimum time record of all rows is 2:34 .'}, '2:34'], 'result': True, 'ind': 1, 'tostr': 'eq { min { all_rows ; time } ; 2:34 }', 'tointer': 'the minimum time record of all rows is 2:34 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'time'], 'result': None, 'ind': 2, 'tostr': 'argmin { all_rows ; time }'}, 'song title'], 'result': 'if you talk in your sleep', 'ind': 3, 'tostr': 'hop { argmin { all_rows ; time } ; song title }'}, 'if you talk in your sleep'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmin { all_rows ; time } ; song title } ; if you talk in your sleep }', 'tointer': 'the song title record of the row with superlative time record is if you talk in your sleep .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { min { all_rows ; time } ; 2:34 } ; eq { hop { argmin { all_rows ; time } ; song title } ; if you talk in your sleep } } = true', 'tointer': 'the minimum time record of all rows is 2:34 . the song title record of the row with superlative time record is if you talk in your sleep .'} | and { eq { min { all_rows ; time } ; 2:34 } ; eq { hop { argmin { all_rows ; time } ; song title } ; if you talk in your sleep } } = true | the minimum time record of all rows is 2:34 . the song title record of the row with superlative time record is if you talk in your sleep . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'min_0': 0, 'all_rows_7': 7, 'time_8': 8, '2:34_9': 9, 'str_eq_4': 4, 'str_hop_3': 3, 'argmin_2': 2, 'all_rows_10': 10, 'time_11': 11, 'song title_12': 12, 'if you talk in your sleep_13': 13} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'min_0': 'min', 'all_rows_7': 'all_rows', 'time_8': 'time', '2:34_9': '2:34', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'argmin_2': 'argmin', 'all_rows_10': 'all_rows', 'time_11': 'time', 'song title_12': 'song title', 'if you talk in your sleep_13': 'if you talk in your sleep'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'min_0': [1], 'all_rows_7': [0], 'time_8': [0], '2:34_9': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'argmin_2': [3], 'all_rows_10': [2], 'time_11': [2], 'song title_12': [3], 'if you talk in your sleep_13': [4]} | ['track', 'recorded', 'catalogue', 'release date', 'song title', 'writer ( s )', 'time'] | [['1', '3 / 28 / 72', '74 - 0769', '8 / 1 / 72', 'burning love', 'dennis linde', '2:50'], ['2', '12 / 11 / 73', 'apbo 0280', '5 / 10 / 74', 'if you talk in your sleep', 'red west and johnny christopher', '2:34'], ['3', '2 / 5 / 76', 'pb 10601b', '3 / 12 / 76', 'for the heart', 'dennis linde', '3:22'], ['4', '2 / 4 / 76', 'pb 10857', '11 / 29 / 76', 'moody blue', 'mark james', '3:22'], ['5', '10 / 29 / 76', 'pb 10998', '6 / 6 / 77', 'way down', 'layng martine jr', '2:38']] |
euro convergence criteria | https://en.wikipedia.org/wiki/Euro_convergence_criteria | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1884378-1.html.csv | superlative | the danish krone has the highest central rate of the currencies listed for euro convergence . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '4', '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', 'central rate'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; central rate }'}, 'currency'], 'result': 'danish krone', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; central rate } ; currency }'}, 'danish krone'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; central rate } ; currency } ; danish krone } = true', 'tointer': 'select the row whose central rate record of all rows is maximum . the currency record of this row is danish krone .'} | eq { hop { argmax { all_rows ; central rate } ; currency } ; danish krone } = true | select the row whose central rate record of all rows is maximum . the currency record of this row is danish krone . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'central rate_5': 5, 'currency_6': 6, 'danish krone_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'central rate_5': 'central rate', 'currency_6': 'currency', 'danish krone_7': 'danish krone'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'central rate_5': [0], 'currency_6': [1], 'danish krone_7': [2]} | ['currency', 'code', 'entry erm ii', 'central rate', 'official target date'] | [['bulgarian lev', 'bgn', '-', '1.95583', '-'], ['croatian kuna', 'hrk', '-', '-', '-'], ['czech koruna', 'czk', '-', '-', '-'], ['danish krone', 'dkk', '1 january 1999', '7.46038', 'formal opt - out'], ['hungarian forint', 'huf', '-', '-', '-'], ['latvian lats', 'lvl', '2 may 2005', '0.702804', '1 january 2014'], ['lithuanian litas', 'ltl', '28 june 2004', '3.45280', '1 january 2015'], ['polish złoty', 'pln', '-', '-', '-'], ['romanian leu', 'ron', '-', '-', '-'], ['swedish krona', 'sek', 'not considered', '-', 'de facto opt - out'], ['british pound sterling gibraltar pound', 'gbp gip', 'not considered', '-', 'formal opt - out']] |
list of better off ted episodes | https://en.wikipedia.org/wiki/List_of_Better_Off_Ted_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21994729-2.html.csv | comparative | for the show better off ted , the episode titled " trust and consequence " aired 7 days before the episode , " father can you hear me . " . | {'row_1': '10', 'row_2': '11', 'col': '5', 'col_other': '2', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '7 days', 'bigger': 'row2'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'trust and consequence'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to trust and consequence .', 'tostr': 'filter_eq { all_rows ; title ; trust and consequence }'}, 'original air date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; title ; trust and consequence } ; original air date }', 'tointer': 'select the rows whose title record fuzzily matches to trust and consequence . take the original air date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'father , can you hair me'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose title record fuzzily matches to father , can you hair me .', 'tostr': 'filter_eq { all_rows ; title ; father , can you hair me }'}, 'original air date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; title ; father , can you hair me } ; original air date }', 'tointer': 'select the rows whose title record fuzzily matches to father , can you hair me . take the original air date record of this row .'}], 'result': '-7 days', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; title ; trust and consequence } ; original air date } ; hop { filter_eq { all_rows ; title ; father , can you hair me } ; original air date } }'}, '-7 days'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; title ; trust and consequence } ; original air date } ; hop { filter_eq { all_rows ; title ; father , can you hair me } ; original air date } } ; -7 days } = true', 'tointer': 'select the rows whose title record fuzzily matches to trust and consequence . take the original air date record of this row . select the rows whose title record fuzzily matches to father , can you hair me . take the original air date record of this row . the second record is 7 days larger than the first record .'} | eq { diff { hop { filter_eq { all_rows ; title ; trust and consequence } ; original air date } ; hop { filter_eq { all_rows ; title ; father , can you hair me } ; original air date } } ; -7 days } = true | select the rows whose title record fuzzily matches to trust and consequence . take the original air date record of this row . select the rows whose title record fuzzily matches to father , can you hair me . take the original air date record of this row . the second record is 7 days larger than the first record . | 6 | 6 | {'str_eq_5': 5, 'result_6': 6, 'diff_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'title_8': 8, 'trust and consequence_9': 9, 'original air date_10': 10, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'title_12': 12, 'father , can you hair me_13': 13, 'original air date_14': 14, '-7 days_15': 15} | {'str_eq_5': 'str_eq', 'result_6': 'true', 'diff_4': 'diff', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'title_8': 'title', 'trust and consequence_9': 'trust and consequence', 'original air date_10': 'original air date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'title_12': 'title', 'father , can you hair me_13': 'father , can you hair me', 'original air date_14': 'original air date', '-7 days_15': '-7 days'} | {'str_eq_5': [6], 'result_6': [], 'diff_4': [5], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'title_8': [0], 'trust and consequence_9': [0], 'original air date_10': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'title_12': [1], 'father , can you hair me_13': [1], 'original air date_14': [3], '-7 days_15': [5]} | ['series', 'title', 'directed by', 'written by', 'original air date', 'prod code'] | [['1', 'pilot', 'michael fresco', 'victor fresco', 'march 18 , 2009', '1apx79'], ['2', 'heroes', 'michael fresco', 'victor fresco', 'march 25 , 2009', '1apx01'], ['3', 'through rose - colored hazmat suits', 'michael fresco', 'justin adler', 'april 1 , 2009', '1apx02'], ['4', 'racial sensitivity', 'paul lazarus', 'michael glouberman', 'april 8 , 2009', '1apx03'], ['5', 'win some , dose some', 'michael fresco', 'elijah aron & jordan young', 'april 15 , 2009', '1apx07'], ['6', 'goodbye , mr chips', 'paul lazarus', 'becky mann & audra sieleff', 'april 22 , 2009', '1apx08'], ['7', 'get happy', 'gail mancuso', 'mike teverbaugh', 'may 5 , 2009', '1apx06'], ['8', 'you are the boss of me', 'michael spiller', "dan o ' shannon", 'june 23 , 2009', '1apx05'], ['9', 'bioshuffle', 'michael fresco', 'michael a ross', 'june 30 , 2009', '1apx09'], ['10', 'trust and consequence', 'lee shallat - chemel', 'mike teverbaugh', 'july 14 , 2009', '1apx10'], ['11', 'father , can you hair me', 'michael fresco', 'michael glouberman', 'july 21 , 2009', '1apx11'], ['12', 'jabberwocky', 'michael fresco', 'michael a ross', 'august 11 , 2009', '1apx04']] |
euroleague 2007 - 08 individual statistics | https://en.wikipedia.org/wiki/Euroleague_2007%E2%80%9308_Individual_Statistics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16050349-4.html.csv | majority | all of the top scoring players in euroleague 2007 - 08 played in 6 games . | {'scope': 'all', 'col': '4', 'most_or_all': 'all', 'criterion': 'equal', 'value': '6', 'subset': None} | {'func': 'all_eq', 'args': ['all_rows', 'games', '6'], 'result': True, 'ind': 0, 'tointer': 'for the games records of all rows , all of them are equal to 6 .', 'tostr': 'all_eq { all_rows ; games ; 6 } = true'} | all_eq { all_rows ; games ; 6 } = true | for the games records of all rows , all of them are equal to 6 . | 1 | 1 | {'all_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'games_3': 3, '6_4': 4} | {'all_eq_0': 'all_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'games_3': 'games', '6_4': '6'} | {'all_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'games_3': [0], '6_4': [0]} | ['rank', 'name', 'team', 'games', 'points'] | [['1', 'will solomon', 'fenerbahçe', '6', '123'], ['2', 'jeremiah massey', 'aris thessaloniki', '6', '120'], ['3', 'lynn greer', 'olympiacos', '6', '113'], ['4', 'hollis price', 'lietuvos rytas vilnius', '6', '101'], ['4', 'kenan bajramović', 'lietuvos rytas vilnius', '6', '101']] |
meaghan francella | https://en.wikipedia.org/wiki/Meaghan_Francella | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10021158-3.html.csv | majority | meaghan francella 's had almost no wins throughout her 2005-2012 lpga career . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': '0', 'subset': None} | {'func': 'most_eq', 'args': ['all_rows', 'wins', '0'], 'result': True, 'ind': 0, 'tointer': 'for the wins records of all rows , most of them are equal to 0 .', 'tostr': 'most_eq { all_rows ; wins ; 0 } = true'} | most_eq { all_rows ; wins ; 0 } = true | for the wins records of all rows , most of them are equal to 0 . | 1 | 1 | {'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'wins_3': 3, '0_4': 4} | {'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'wins_3': 'wins', '0_4': '0'} | {'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'wins_3': [0], '0_4': [0]} | ['year', 'tournaments played', 'cuts made', 'wins', '2nd', 'top 10s', 'best finish', 'earnings', 'money list rank', 'scoring average', 'scoring rank'] | [['2005', '1', '1', '0', '0', '0', 't69', '2525', 'n / a', '75.00', 'n / a'], ['2006', '3', '1', '0', '0', '0', 't39', '55554', '183', '73.75', 'n / a'], ['2007', '25', '18', '1', '0', '4', '1', '507292', '29', '73.09', '66'], ['2008', '24', '11', '0', '0', '0', 't13', '117682', '88', '73.75', '131'], ['2009', '22', '16', '0', '0', '2', 't5', '292266', '48', '72.51', '63'], ['2010', '21', '17', '0', '0', '1', 't7', '168016', '57', '73.04', '77'], ['2011', '15', '8', '0', '0', '0', 't22', '66813', '84', '74.23', '117'], ['2012', '15', '4', '0', '0', '0', 't24', '28935', '116', '74.37', '121']] |
statistics relating to enlargement of the european union | https://en.wikipedia.org/wiki/Statistics_relating_to_enlargement_of_the_European_Union | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1307842-7.html.csv | ordinal | poland is the member country with the second lowest gdp per capita in the european union . | {'row': '8', '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', 'gdp per capita ( us )', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; gdp per capita ( us ) ; 2 }'}, 'member countries'], 'result': 'poland', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; gdp per capita ( us ) ; 2 } ; member countries }'}, 'poland'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; gdp per capita ( us ) ; 2 } ; member countries } ; poland } = true', 'tointer': 'select the row whose gdp per capita ( us ) record of all rows is 2nd minimum . the member countries record of this row is poland .'} | eq { hop { nth_argmin { all_rows ; gdp per capita ( us ) ; 2 } ; member countries } ; poland } = true | select the row whose gdp per capita ( us ) record of all rows is 2nd minimum . the member countries record of this row is poland . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'gdp per capita (us)_5': 5, '2_6': 6, 'member countries_7': 7, 'poland_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', 'gdp per capita (us)_5': 'gdp per capita ( us )', '2_6': '2', 'member countries_7': 'member countries', 'poland_8': 'poland'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'gdp per capita (us)_5': [0], '2_6': [0], 'member countries_7': [1], 'poland_8': [2]} | ['member countries', 'population', 'area ( km square )', 'gdp ( billion us )', 'gdp per capita ( us )'] | [['cyprus', '775927', '9250', '11.681', '15054'], ['czech republic', '10246178', '78866', '105.248', '10272'], ['estonia', '1341664', '45226', '22.384', '16684'], ['hungary', '10032375', '93030', '102183', '10185'], ['latvia', '2306306', '64589', '24.826', '10764'], ['lithuania', '3607899', '65200', '31.971', '8861'], ['malta', '396851', '316', '5.097', '12843'], ['poland', '38580445', '311904', '316.438', '8202'], ['slovakia', '5423567', '49036', '42.800', '7810'], ['slovenia', '2011473', '20273', '29.633', '14732'], ['accession countries', '74722685', '737690', '685.123', '9169'], ['existing members ( 2004 )', '381781620', '3367154', '7711.871', '20200']] |
jack brabham | https://en.wikipedia.org/wiki/Jack_Brabham | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16564-3.html.csv | comparative | jack brabham had a better ranking placement on 1961 ( 17 ) than in 1970 ( 22 ) . | {'row_1': '1', 'row_2': '4', 'col': '4', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1961'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 1961 .', 'tostr': 'filter_eq { all_rows ; year ; 1961 }'}, 'rank'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 1961 } ; rank }', 'tointer': 'select the rows whose year record fuzzily matches to 1961 . take the rank record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1970'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 1970 .', 'tostr': 'filter_eq { all_rows ; year ; 1970 }'}, 'rank'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 1970 } ; rank }', 'tointer': 'select the rows whose year record fuzzily matches to 1970 . take the rank record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; year ; 1961 } ; rank } ; hop { filter_eq { all_rows ; year ; 1970 } ; rank } }', 'tointer': 'select the rows whose year record fuzzily matches to 1961 . take the rank record of this row . select the rows whose year record fuzzily matches to 1970 . take the rank record of this row . the first record is less than the second record .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1961'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 1961 .', 'tostr': 'filter_eq { all_rows ; year ; 1961 }'}, 'rank'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 1961 } ; rank }', 'tointer': 'select the rows whose year record fuzzily matches to 1961 . take the rank record of this row .'}, '17'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; year ; 1961 } ; rank } ; 17 }', 'tointer': 'the rank record of the first row is 17 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1970'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 1970 .', 'tostr': 'filter_eq { all_rows ; year ; 1970 }'}, 'rank'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 1970 } ; rank }', 'tointer': 'select the rows whose year record fuzzily matches to 1970 . take the rank record of this row .'}, '22'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; year ; 1970 } ; rank } ; 22 }', 'tointer': 'the rank record of the second row is 22 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; year ; 1961 } ; rank } ; 17 } ; eq { hop { filter_eq { all_rows ; year ; 1970 } ; rank } ; 22 } }', 'tointer': 'the rank record of the first row is 17 . the rank record of the second row is 22 .'}], 'result': True, 'ind': 8, 'tostr': 'and { less { hop { filter_eq { all_rows ; year ; 1961 } ; rank } ; hop { filter_eq { all_rows ; year ; 1970 } ; rank } } ; and { eq { hop { filter_eq { all_rows ; year ; 1961 } ; rank } ; 17 } ; eq { hop { filter_eq { all_rows ; year ; 1970 } ; rank } ; 22 } } } = true', 'tointer': 'select the rows whose year record fuzzily matches to 1961 . take the rank record of this row . select the rows whose year record fuzzily matches to 1970 . take the rank record of this row . the first record is less than the second record . the rank record of the first row is 17 . the rank record of the second row is 22 .'} | and { less { hop { filter_eq { all_rows ; year ; 1961 } ; rank } ; hop { filter_eq { all_rows ; year ; 1970 } ; rank } } ; and { eq { hop { filter_eq { all_rows ; year ; 1961 } ; rank } ; 17 } ; eq { hop { filter_eq { all_rows ; year ; 1970 } ; rank } ; 22 } } } = true | select the rows whose year record fuzzily matches to 1961 . take the rank record of this row . select the rows whose year record fuzzily matches to 1970 . take the rank record of this row . the first record is less than the second record . the rank record of the first row is 17 . the rank record of the second row is 22 . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'less_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'year_11': 11, '1961_12': 12, 'rank_13': 13, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'year_15': 15, '1970_16': 16, 'rank_17': 17, 'and_7': 7, 'eq_5': 5, '17_18': 18, 'eq_6': 6, '22_19': 19} | {'and_8': 'and', 'result_9': 'true', 'less_4': 'less', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'year_11': 'year', '1961_12': '1961', 'rank_13': 'rank', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'year_15': 'year', '1970_16': '1970', 'rank_17': 'rank', 'and_7': 'and', 'eq_5': 'eq', '17_18': '17', 'eq_6': 'eq', '22_19': '22'} | {'and_8': [9], 'result_9': [], 'less_4': [8], 'num_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'year_11': [0], '1961_12': [0], 'rank_13': [2], 'num_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'year_15': [1], '1970_16': [1], 'rank_17': [3], 'and_7': [8], 'eq_5': [7], '17_18': [5], 'eq_6': [7], '22_19': [6]} | ['year', 'start', 'qual', 'rank', 'finish', 'laps'] | [['1961', '13', '145.144', '17', '9', '200'], ['1964', '25', '152.504', '15', '20', '77'], ['1969', '29', '163.875', '29', '24', '58'], ['1970', '26', '166.397', '22', '13', '175']] |
miss world | https://en.wikipedia.org/wiki/Miss_World | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-150343-1.html.csv | count | six of the miss world competitions took place in china . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'china', 'result': '6', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'china'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to china .', 'tostr': 'filter_eq { all_rows ; location ; china }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location ; china } }', 'tointer': 'select the rows whose location record fuzzily matches to china . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location ; china } } ; 6 } = true', 'tointer': 'select the rows whose location record fuzzily matches to china . the number of such rows is 6 .'} | eq { count { filter_eq { all_rows ; location ; china } } ; 6 } = true | select the rows whose location record fuzzily matches to china . the number of such rows is 6 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'location_5': 5, 'china_6': 6, '6_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', 'china_6': 'china', '6_7': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location_5': [0], 'china_6': [0], '6_7': [2]} | ['year', 'country / territory', 'miss world', 'national title', 'location'] | [['2013', 'philippines', 'megan young', 'miss world philippines', 'bali , indonesia'], ['2012', 'china pr', 'yu wenxia', 'miss china world', 'ordos city , china'], ['2011', 'venezuela', 'ivian sarcos', 'miss venezuela world', 'london , united kingdom'], ['2010', 'united states', 'alexandria mills', 'miss world united states', 'sanya , china'], ['2009', 'gibraltar', 'kaiane aldorino', 'miss gibraltar', 'johannesburg , south africa'], ['2008', 'russia', 'ksenia sukhinova', 'miss russia', 'johannesburg , south africa'], ['2007', 'china pr', 'zhang zilin', 'miss china world', 'sanya , china'], ['2006', 'czech republic', 'taťána kuchařová', 'miss czech republic', 'warsaw , poland'], ['2005', 'iceland', 'unnur birna vilhjálmsdóttir', 'ungfrú ísland', 'sanya , china'], ['2004', 'peru', 'maría julia mantilla', 'miss world perú', 'sanya , china'], ['2003', 'ireland', 'rosanna davison', 'miss ireland', 'sanya , china'], ['2002', 'turkey', 'azra akın', 'miss turkey', 'london , united kingdom'], ['2001', 'nigeria', 'agbani darego', 'most beautiful girl in nigeria', 'sun city , south africa'], ['2000', 'india', 'priyanka chopra', 'femina miss india', 'london , united kingdom']] |
2007 manx grand prix | https://en.wikipedia.org/wiki/2007_Manx_Grand_Prix | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11972799-2.html.csv | majority | the majority of riders in the 2007 manx grand prix drove with a suzuki bike . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'suzuki', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'team', 'suzuki'], 'result': True, 'ind': 0, 'tointer': 'for the team records of all rows , most of them fuzzily match to suzuki .', 'tostr': 'most_eq { all_rows ; team ; suzuki } = true'} | most_eq { all_rows ; team ; suzuki } = true | for the team records of all rows , most of them fuzzily match to suzuki . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'team_3': 3, 'suzuki_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'team_3': 'team', 'suzuki_4': 'suzuki'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'team_3': [0], 'suzuki_4': [0]} | ['rank', 'rider', 'team', 'speed', 'time'] | [['1', 'andrew brady', '750cc suzuki', '109.107 mph', '1:22.59.61'], ['2', 'russell mountford', '599cc yamaha', '107.888 mph', '1:20.22.88'], ['3', 'justin croft', '600cc yamaha', '107.678 mph', '1:24.05.72'], ['4', 'noel patterson', '750cc suzuki', '107.256 mph', '1:24.25.55'], ['5', 'paul gartland', '750cc suzuki', '104.660 mph', '1:26.31.20'], ['6', 'sergio romero', '599cc honda', '103.727 mph', '1:27.17.88'], ['7', 'ian gilder', '750cc suzuki', '103.637 mph', '1:27.22.47'], ['8', 'michael russell', '600cc suzuki', '103.455 mph', '1:27.31.69'], ['9', 'james mccann', '750cc suzuki', '103.346 mph', '1:27.37.22'], ['10', 'adrian louge', '749cc suzuki', '102.962 mph', '1:27.56.85']] |
list of people in playboy 1990 - 99 | https://en.wikipedia.org/wiki/List_of_people_in_Playboy_1990%E2%80%9399 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1566850-6.html.csv | ordinal | tim robbins was the second person to be interviewed by playboy magazine in 1995 . | {'row': '2', 'col': '1', 'order': '2', 'col_other': '4', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'scope': 'all', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_min', 'args': ['all_rows', 'date', '2'], 'result': '2 - 95', 'ind': 0, 'tostr': 'nth_min { all_rows ; date ; 2 }', 'tointer': 'the 2nd minimum date record of all rows is 2 - 95 .'}, '2 - 95'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; date ; 2 } ; 2 - 95 }', 'tointer': 'the 2nd minimum date record of all rows is 2 - 95 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'date', '2'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; date ; 2 }'}, 'interview subject'], 'result': 'tim robbins', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; date ; 2 } ; interview subject }'}, 'tim robbins'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; date ; 2 } ; interview subject } ; tim robbins }', 'tointer': 'the interview subject record of the row with 2nd minimum date record is tim robbins .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { nth_min { all_rows ; date ; 2 } ; 2 - 95 } ; eq { hop { nth_argmin { all_rows ; date ; 2 } ; interview subject } ; tim robbins } } = true', 'tointer': 'the 2nd minimum date record of all rows is 2 - 95 . the interview subject record of the row with 2nd minimum date record is tim robbins .'} | and { eq { nth_min { all_rows ; date ; 2 } ; 2 - 95 } ; eq { hop { nth_argmin { all_rows ; date ; 2 } ; interview subject } ; tim robbins } } = true | the 2nd minimum date record of all rows is 2 - 95 . the interview subject record of the row with 2nd minimum date record is tim robbins . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_7': 7, 'date_8': 8, '2_9': 9, '2 - 95_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_11': 11, 'date_12': 12, '2_13': 13, 'interview subject_14': 14, 'tim robbins_15': 15} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_7': 'all_rows', 'date_8': 'date', '2_9': '2', '2 - 95_10': '2 - 95', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_11': 'all_rows', 'date_12': 'date', '2_13': '2', 'interview subject_14': 'interview subject', 'tim robbins_15': 'tim robbins'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'nth_min_0': [1], 'all_rows_7': [0], 'date_8': [0], '2_9': [0], '2 - 95_10': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'nth_argmin_2': [3], 'all_rows_11': [2], 'date_12': [2], '2_13': [2], 'interview subject_14': [3], 'tim robbins_15': [4]} | ['date', 'cover model', 'centerfold model', 'interview subject', '20 questions'] | [['1 - 95', 'drew barrymore', 'melissa deanne holliday', 'jean - claude van damme', 'tom snyder'], ['2 - 95', 'victoria jacobs', 'lisa marie scott', 'tim robbins', 'david spade'], ['3 - 95', 'amber smith', 'stacy sanches', 'vladimir zhirinovsky', 'jon stewart'], ['4 - 95', 'shana hiatt', 'danelle folta', 'david mamet', 'samuel l jackson'], ['5 - 95', 'nancy sinatra', 'cynthia gwyn brown', 'camille paglia', 'david hasselhoff'], ['6 - 95', 'julie lynn cialini', 'rhonda adams', 'joycelyn elders', 'tom arnold'], ['7 - 95', 'sandra taylor', 'heidi mark', 'mel gibson', 'kurt loder'], ['8 - 95', 'shelly jones', 'rachel jeã ¡ n marteen', 'berry gordy', 'dawn steel'], ['9 - 95', 'kimberley conrad hefner', "donna d'errico", 'cindy crawford', 'sandra bullock'], ['10 - 95', 'lisa boyle', 'alicia rickter', 'snoop doggy dogg', 'bill maher'], ['11 - 95', 'tahnee welch', 'holly witt', 'harvey keitel', 'g gordon liddy'], ['12 - 95', 'farrah fawcett', 'samantha torres', 'george foreman', 'dominick dunne']] |
2008 hamilton tiger - cats season | https://en.wikipedia.org/wiki/2008_Hamilton_Tiger-Cats_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16912076-4.html.csv | unique | week 9 was the only week where the tiger-cats did not play a game . | {'scope': 'all', 'row': '9', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': '-', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'date', '-'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record is equal to - .', 'tostr': 'filter_eq { all_rows ; date ; - }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; date ; - } }', 'tointer': 'select the rows whose date record is equal to - . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'date', '-'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record is equal to - .', 'tostr': 'filter_eq { all_rows ; date ; - }'}, 'week'], 'result': '9', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; - } ; week }'}, '9'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; - } ; week } ; 9 }', 'tointer': 'the week record of this unqiue row is 9 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; date ; - } } ; eq { hop { filter_eq { all_rows ; date ; - } ; week } ; 9 } } = true', 'tointer': 'select the rows whose date record is equal to - . there is only one such row in the table . the week record of this unqiue row is 9 .'} | and { only { filter_eq { all_rows ; date ; - } } ; eq { hop { filter_eq { all_rows ; date ; - } ; week } ; 9 } } = true | select the rows whose date record is equal to - . there is only one such row in the table . the week record of this unqiue row is 9 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'date_7': 7, '-_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'week_9': 9, '9_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'date_7': 'date', '-_8': '-', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'week_9': 'week', '9_10': '9'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'date_7': [0], '-_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'week_9': [2], '9_10': [3]} | ['week', 'date', 'opponent', 'score', 'result', 'attendance', 'record'] | [['1', 'june 26', 'montreal alouettes', '33 - 10', 'loss', '20589', '0 - 1'], ['2', 'july 3', 'toronto argonauts', '32 - 13', 'win', '30822', '1 - 1'], ['3', 'july 12', 'saskatchewan roughriders', '33 - 28', 'loss', '20874', '1 - 2'], ['4', 'july 17', 'calgary stampeders', '43 - 16', 'loss', '31116', '1 - 3'], ['5', 'july 24', 'edmonton eskimos', '19 - 13', 'loss', '21402', '1 - 4'], ['6', 'july 31', 'montreal alouettes', '40 - 33', 'loss', '20202', '1 - 5'], ['7', 'aug 7', 'toronto argonauts', '45 - 21', 'win', '19423', '2 - 5'], ['8', 'aug 14', 'winnipeg blue bombers', '37 - 24', 'loss', '25484', '2 - 6'], ['9', '-', '-', '-', '-', '-', ''], ['10', 'sept 1', 'toronto argonauts', '34 - 31', 'loss', '25911', '2 - 7'], ['11', 'sept 6', 'bc lions', '35 - 12', 'loss', '18723', '2 - 8'], ['12', 'sept 13', 'edmonton eskimos', '38 - 33', 'loss', '37500', '2 - 9'], ['13', 'sept 19', 'winnipeg blue bombers', '25 - 23', 'loss', '19102', '2 - 10'], ['14', 'sept 27', 'bc lions', '40 - 10', 'loss', '31161', '2 - 11'], ['15', 'oct 4', 'montreal alouettes', '44 - 36', 'win', '20423', '3 - 11'], ['16', 'oct 13', 'montreal alouettes', '42 - 11', 'loss', '20202', '3 - 12'], ['17', 'oct 19', 'saskatchewan roughriders', '30 - 29', 'loss', '30945', '3 - 13'], ['18', 'oct 24', 'calgary stampeders', '28 - 17', 'loss', '20614', '3 - 14'], ['19', 'nov 1', 'winnipeg blue bombers', '44 - 30', 'loss', '24595', '3 - 15']] |
superliga de voleibol masculina | https://en.wikipedia.org/wiki/Superliga_de_Voleibol_Masculina | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17308611-2.html.csv | majority | for superliga de voleibol masculina , when the points - was over 1200 , most of the points + were over 1000 . | {'scope': 'subset', 'col': '4', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '1000', 'subset': {'col': '5', 'criterion': 'greater_than', 'value': '1200'}} | {'func': 'most_greater', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'points -', '1200'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; points - ; 1200 }', 'tointer': 'select the rows whose points - record is greater than 1200 .'}, 'points +', '1000'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose points - record is greater than 1200 . for the points + records of these rows , most of them are greater than 1000 .', 'tostr': 'most_greater { filter_greater { all_rows ; points - ; 1200 } ; points + ; 1000 } = true'} | most_greater { filter_greater { all_rows ; points - ; 1200 } ; points + ; 1000 } = true | select the rows whose points - record is greater than 1200 . for the points + records of these rows , most of them are greater than 1000 . | 2 | 2 | {'most_greater_1': 1, 'result_2': 2, 'filter_greater_0': 0, 'all_rows_3': 3, 'points -_4': 4, '1200_5': 5, 'points +_6': 6, '1000_7': 7} | {'most_greater_1': 'most_greater', 'result_2': 'true', 'filter_greater_0': 'filter_greater', 'all_rows_3': 'all_rows', 'points -_4': 'points -', '1200_5': '1200', 'points +_6': 'points +', '1000_7': '1000'} | {'most_greater_1': [2], 'result_2': [], 'filter_greater_0': [1], 'all_rows_3': [0], 'points -_4': [0], '1200_5': [0], 'points +_6': [1], '1000_7': [1]} | ['team', 'sets +', 'sets -', 'points +', 'points -'] | [['cai teruel', '51', '10', '1466', '1134'], ['unicaja almería', '50', '12', '1497', '1142'], ['cma soria', '42', '18', '1399', '1201'], ['cajasol juvasa', '41', '21', '1466', '1299'], ['ushuaïa ibiza voley', '39', '22', '1412', '1274'], ["l'illa - grau", '25', '38', '1340', '1418'], ['vecindario ace gran canaria', '23', '38', '1250', '1384'], ['andorra', '19', '43', '1228', '1435'], ['vigo', '12', '48', '1091', '1428'], ['zaragoza', '2', '54', '960', '1394']] |
list of sports teams in nebraska | https://en.wikipedia.org/wiki/List_of_sports_teams_in_Nebraska | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14115168-4.html.csv | aggregation | the teams in nebraska that are part of the national association of intercollegiate athletics have a combined total of 79 national titles . | {'scope': 'all', 'col': '4', 'type': 'sum', 'result': '79', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'national titles'], 'result': '79', 'ind': 0, 'tostr': 'sum { all_rows ; national titles }'}, '79'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; national titles } ; 79 } = true', 'tointer': 'the sum of the national titles record of all rows is 79 .'} | round_eq { sum { all_rows ; national titles } ; 79 } = true | the sum of the national titles record of all rows is 79 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'national titles_4': 4, '79_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'national titles_4': 'national titles', '79_5': '79'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'national titles_4': [0], '79_5': [1]} | ['school', 'mascot', 'conference', 'national titles', 'founded'] | [['bellevue university', 'bellevue bruins', 'midlands', '14', '1966'], ['college of saint mary', 'saint mary flames', 'midlands', '0', '1923'], ['concordia university', 'concordia bulldogs', 'great plains', '1', '1894'], ['doane college', 'doane tigers', 'great plains', '10', '1872'], ['hastings college', 'hastings broncos', 'great plains', '3', '1882'], ['midland university', 'midland warriors', 'great plains', '2', '1883'], ['nebraska wesleyan university', 'nw prairie wolves', 'great plains', '19', '1887'], ['peru state college', 'peru state bobcats', 'midlands', '2', '1865'], ['york college', 'york panthers', 'midlands', '28', '1890']] |
sport in queensland | https://en.wikipedia.org/wiki/Sport_in_Queensland | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10909383-1.html.csv | count | two teams from queensland play their matches at brisbane cricket ground . | {'scope': 'all', 'criterion': 'equal', 'value': 'brisbane cricket ground', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'brisbane cricket ground'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to brisbane cricket ground .', 'tostr': 'filter_eq { all_rows ; venue ; brisbane cricket ground }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; venue ; brisbane cricket ground } }', 'tointer': 'select the rows whose venue record fuzzily matches to brisbane cricket ground . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; venue ; brisbane cricket ground } } ; 2 } = true', 'tointer': 'select the rows whose venue record fuzzily matches to brisbane cricket ground . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; venue ; brisbane cricket ground } } ; 2 } = true | select the rows whose venue record fuzzily matches to brisbane cricket ground . 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, 'venue_5': 5, 'brisbane cricket ground_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', 'venue_5': 'venue', 'brisbane cricket ground_6': 'brisbane cricket ground', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'venue_5': [0], 'brisbane cricket ground_6': [0], '2_7': [2]} | ['club / team', 'league', 'venue', 'established', 'premierships'] | [['brisbane bandits', 'australian baseball league', 'brisbane exhibition ground', '1989', '1'], ['brisbane broncos', 'national rugby league', 'suncorp stadium', '1988', '6'], ['brisbane lions', 'australian football league', 'brisbane cricket ground', '1997', '3'], ['brisbane roar', 'a - league / w - league', 'suncorp stadium', '2004', '1 / 2'], ['queensland blades', 'australian hockey league', 'queensland state hockey centre', '1991', '5'], ['queensland bulls', 'pura cup / ford ranger cup', 'brisbane cricket ground', '1892', '13'], ['queensland firebirds', 'commonwealth bank trophy', 'chandler arena', '1997', 'nil'], ['queensland reds', 'super rugby', 'suncorp stadium', '1996', '1'], ['triple eight race engineering', 'international v8 supercars championship', 'queensland raceway', '2003', '4']] |
1986 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1986_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17231232-5.html.csv | majority | most of the players of the 1986 u.s. open ( golf ) tournament 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', 'greg norman', 'australia', '71 + 68 = 139', '1'], ['t2', 'lee trevino', 'united states', '74 + 68 = 142', '+ 2'], ['t2', 'denis watson', 'zimbabwe', '72 + 70 = 142', '+ 2'], ['t4', 'raymond floyd', 'united states', '75 + 68 = 143', '+ 3'], ['t4', 'bob tway', 'united states', '70 + 73 = 143', '+ 3'], ['t4', 'tom watson', 'united states', '72 + 71 = 143', '+ 3'], ['t7', 'david frost', 'south africa', '72 + 72 = 144', '+ 4'], ['t7', 'bernhard langer', 'west germany', '74 + 70 = 144', '+ 4'], ['t7', 'tsuneyuki nakajima', 'japan', '72 + 72 = 144', '+ 4'], ['t7', "mac o'grady", 'united states', '75 + 69 = 144', '+ 4'], ['t7', 'payne stewart', 'united states', '76 + 68 = 144', '+ 4'], ['t7', 'bobby wadkins', 'united states', '75 + 69 = 144', '+ 4'], ['t7', 'lanny wadkins', 'united states', '74 + 70 = 144', '+ 4']] |
longyan | https://en.wikipedia.org/wiki/Longyan | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1204998-2.html.csv | aggregation | the average population of districts and counties in longyan is 365649.3 . | {'scope': 'all', 'col': '7', 'type': 'average', 'result': '365649.3', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'population'], 'result': '365649.3', 'ind': 0, 'tostr': 'avg { all_rows ; population }'}, '365649.3'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; population } ; 365649.3 } = true', 'tointer': 'the average of the population record of all rows is 365649.3 .'} | round_eq { avg { all_rows ; population } ; 365649.3 } = true | the average of the population record of all rows is 365649.3 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'population_4': 4, '365649.3_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'population_4': 'population', '365649.3_5': '365649.3'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'population_4': [0], '365649.3_5': [1]} | ['english name', 'simplified', 'traditional', 'pinyin', 'hakka', 'area', 'population', 'density'] | [['xinluo district', '新罗区', '新羅區', 'xīnluó qū', 'sîn - lò - khî', '2685', '662429', '247'], ['zhangping city', '漳平市', '漳平市', 'zhāngpíng shì', 'chông - phìn - sṳ', '2975', '240194', '81'], ['changting county', '长汀县', '長汀縣', 'chángtīng xiàn', 'tshòng - tin - yen', '3099', '393390', '127'], ['yongding county', '永定县', '永定縣', 'yǒngdìng xiàn', 'yún - thin - yen', '2216', '362658', '164'], ['shanghang county', '上杭县', '上杭縣', 'shàngháng xiàn', 'sông - hông - yen', '2879', '374047', '130'], ['wuping county', '武平县', '武平縣', 'wǔpíng xiàn', 'vú - phìn - yen', '2630', '278182', '106'], ['liancheng county', '连城县', '連城縣', 'liánchéng xiàn', 'lièn - sàng - yen', '2596', '248645', '96']] |
united states house of representatives elections , 1908 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1908 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1365787-4.html.csv | majority | for the united states house of representatives elections in 1908 , of the candidates first elected in the 1900s , all of them were from the democratic party . | {'scope': 'subset', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'democratic', 'subset': {'col': '4', 'criterion': 'fuzzily_match', 'value': '19'}} | {'func': 'all_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'first elected', '19'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; first elected ; 19 }', 'tointer': 'select the rows whose first elected record fuzzily matches to 19 .'}, 'party', 'democratic'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose first elected record fuzzily matches to 19 . for the party records of these rows , all of them fuzzily match to democratic .', 'tostr': 'all_eq { filter_eq { all_rows ; first elected ; 19 } ; party ; democratic } = true'} | all_eq { filter_eq { all_rows ; first elected ; 19 } ; party ; democratic } = true | select the rows whose first elected record fuzzily matches to 19 . for the party records of these rows , all of them fuzzily match to democratic . | 2 | 2 | {'all_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'first elected_4': 4, '19_5': 5, 'party_6': 6, 'democratic_7': 7} | {'all_str_eq_1': 'all_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'first elected_4': 'first elected', '19_5': '19', 'party_6': 'party', 'democratic_7': 'democratic'} | {'all_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'first elected_4': [0], '19_5': [0], 'party_6': [1], 'democratic_7': [1]} | ['district', 'incumbent', 'party', 'first elected', 'result'] | [['south carolina 1', 'george swinton legarã', 'democratic', '1902', 're - elected'], ['south carolina 2', "james o ' h patterson", 'democratic', '1904', 're - elected'], ['south carolina 3', 'wyatt aiken', 'democratic', '1902', 're - elected'], ['south carolina 4', 'joseph t johnson', 'democratic', '1900', 're - elected'], ['south carolina 5', 'david e finley', 'democratic', '1898', 're - elected'], ['south carolina 6', 'j edwin ellerbe', 'democratic', '1904', 're - elected'], ['south carolina 7', 'asbury f lever', 'democratic', '1901 ( special )', 're - elected']] |
list of england national rugby union team results 1980 - 89 | https://en.wikipedia.org/wiki/List_of_England_national_rugby_union_team_results_1980%E2%80%9389 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18178608-8.html.csv | comparative | japan scored more points than the usa against england in the 1987 world cup . | {'row_1': '6', 'row_2': '7', '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', 'opposing teams', 'japan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opposing teams record fuzzily matches to japan .', 'tostr': 'filter_eq { all_rows ; opposing teams ; japan }'}, 'against'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opposing teams ; japan } ; against }', 'tointer': 'select the rows whose opposing teams record fuzzily matches to japan . take the against record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opposing teams', 'usa'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opposing teams record fuzzily matches to usa .', 'tostr': 'filter_eq { all_rows ; opposing teams ; usa }'}, 'against'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opposing teams ; usa } ; against }', 'tointer': 'select the rows whose opposing teams record fuzzily matches to usa . take the against record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; opposing teams ; japan } ; against } ; hop { filter_eq { all_rows ; opposing teams ; usa } ; against } } = true', 'tointer': 'select the rows whose opposing teams record fuzzily matches to japan . take the against record of this row . select the rows whose opposing teams record fuzzily matches to usa . take the against record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; opposing teams ; japan } ; against } ; hop { filter_eq { all_rows ; opposing teams ; usa } ; against } } = true | select the rows whose opposing teams record fuzzily matches to japan . take the against record of this row . select the rows whose opposing teams record fuzzily matches to usa . take the against 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, 'opposing teams_7': 7, 'japan_8': 8, 'against_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opposing teams_11': 11, 'usa_12': 12, 'against_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', 'opposing teams_7': 'opposing teams', 'japan_8': 'japan', 'against_9': 'against', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opposing teams_11': 'opposing teams', 'usa_12': 'usa', 'against_13': 'against'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opposing teams_7': [0], 'japan_8': [0], 'against_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opposing teams_11': [1], 'usa_12': [1], 'against_13': [3]} | ['opposing teams', 'against', 'date', 'venue', 'status'] | [['ireland', '17', '07 / 02 / 1987', 'lansdowne road , dublin', 'five nations'], ['france', '19', '21 / 02 / 1987', 'twickenham , london', 'five nations'], ['wales', '19', '07 / 03 / 1987', 'cardiff arms park , cardiff', 'five nations'], ['scotland', '12', '04 / 04 / 1987', 'twickenham , london', 'five nations'], ['australia', '19', '23 / 05 / 1987', 'concord oval , sydney', '1987 rugby world cup'], ['japan', '7', '30 / 05 / 1987', 'concord oval , sydney', '1987 rugby world cup'], ['usa', '6', '03 / 06 / 1987', 'concord oval , sydney', '1987 rugby world cup'], ['wales', '16', '08 / 06 / 1987', 'ballymore stadium , brisbane', '1987 rugby world cup']] |
primary schools in dacorum | https://en.wikipedia.org/wiki/Primary_schools_in_Dacorum | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15089329-1.html.csv | ordinal | the team that had the second highest dcsf number was st. albert the great . | {'row': '8', 'col': '4', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'dcsf number', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; dcsf number ; 2 }'}, 'name'], 'result': 'st albert the great', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; dcsf number ; 2 } ; name }'}, 'st albert the great'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; dcsf number ; 2 } ; name } ; st albert the great } = true', 'tointer': 'select the row whose dcsf number record of all rows is 2nd maximum . the name record of this row is st albert the great .'} | eq { hop { nth_argmax { all_rows ; dcsf number ; 2 } ; name } ; st albert the great } = true | select the row whose dcsf number record of all rows is 2nd maximum . the name record of this row is st albert the great . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'dcsf number_5': 5, '2_6': 6, 'name_7': 7, 'st albert the great_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', 'dcsf number_5': 'dcsf number', '2_6': '2', 'name_7': 'name', 'st albert the great_8': 'st albert the great'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'dcsf number_5': [0], '2_6': [0], 'name_7': [1], 'st albert the great_8': [2]} | ['name', 'faith', 'type', 'dcsf number', 'ofsted number'] | [['belswains', '-', 'primary', '2466', '117365'], ['chambersbury', '-', 'primary', '2210', '117214'], ['hobbs hill wood', '-', 'primary', '2469', '117368'], ['leverstock green', 'ce', 'primary', '3054', '117416'], ['lime walk', '-', 'primary', '2422', '117333'], ['nash mills', 'ce', 'primary', '3302', '117418'], ['reddings', '-', 'primary', '2251', '117234'], ['st albert the great', 'rc', 'primary', '3391', '117471'], ['tudor', '-', 'primary', '2045', '117109'], ['two waters', '-', 'primary', '2044', '117108'], ['woodfield', '-', 'special', '7025', '117682']] |
sports in charlotte , north carolina | https://en.wikipedia.org/wiki/Sports_in_Charlotte%2C_North_Carolina | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15720079-6.html.csv | comparative | the venue charlotte speedway closed earlier than the venue belk gymnasium did . | {'row_1': '5', 'row_2': '4', 'col': '4', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'charlotte speedway'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to charlotte speedway .', 'tostr': 'filter_eq { all_rows ; venue ; charlotte speedway }'}, 'closed'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; venue ; charlotte speedway } ; closed }', 'tointer': 'select the rows whose venue record fuzzily matches to charlotte speedway . take the closed record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'belk gymnasium'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose venue record fuzzily matches to belk gymnasium .', 'tostr': 'filter_eq { all_rows ; venue ; belk gymnasium }'}, 'closed'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; venue ; belk gymnasium } ; closed }', 'tointer': 'select the rows whose venue record fuzzily matches to belk gymnasium . take the closed record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; venue ; charlotte speedway } ; closed } ; hop { filter_eq { all_rows ; venue ; belk gymnasium } ; closed } } = true', 'tointer': 'select the rows whose venue record fuzzily matches to charlotte speedway . take the closed record of this row . select the rows whose venue record fuzzily matches to belk gymnasium . take the closed record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; venue ; charlotte speedway } ; closed } ; hop { filter_eq { all_rows ; venue ; belk gymnasium } ; closed } } = true | select the rows whose venue record fuzzily matches to charlotte speedway . take the closed record of this row . select the rows whose venue record fuzzily matches to belk gymnasium . take the closed 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, 'venue_7': 7, 'charlotte speedway_8': 8, 'closed_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'venue_11': 11, 'belk gymnasium_12': 12, 'closed_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', 'venue_7': 'venue', 'charlotte speedway_8': 'charlotte speedway', 'closed_9': 'closed', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'venue_11': 'venue', 'belk gymnasium_12': 'belk gymnasium', 'closed_13': 'closed'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'venue_7': [0], 'charlotte speedway_8': [0], 'closed_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'venue_11': [1], 'belk gymnasium_12': [1], 'closed_13': [3]} | ['venue', 'location', 'environment', 'closed', 'reason'] | [['charlotte coliseum', 'eagle lake , charlotte', 'indoor arena', '2005', 'replaced'], ['jim crockett park', 'dilworth , charlotte', 'open air , natural grass', '1985', 'arson'], ['metrolina speedway', 'metrolina fairgrounds , charlotte', 'open air , dirt', '1990s', 'abandoned'], ['belk gymnasium', 'university city , charlotte', 'indoor arena', '1996', 'converted'], ['charlotte speedway', 'charlotte', 'open air , dirt', '1957', 'closed']] |
uruguayan air force | https://en.wikipedia.org/wiki/Uruguayan_Air_Force | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1015521-1.html.csv | superlative | the bell uh - 1 iroquois is the aircraft which the uruguayan air force has most in service . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '16', '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', 'in service'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; in service }'}, 'aircraft'], 'result': 'bell uh - 1 iroquois', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; in service } ; aircraft }'}, 'bell uh - 1 iroquois'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; in service } ; aircraft } ; bell uh - 1 iroquois } = true', 'tointer': 'select the row whose in service record of all rows is maximum . the aircraft record of this row is bell uh - 1 iroquois .'} | eq { hop { argmax { all_rows ; in service } ; aircraft } ; bell uh - 1 iroquois } = true | select the row whose in service record of all rows is maximum . the aircraft record of this row is bell uh - 1 iroquois . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'in service_5': 5, 'aircraft_6': 6, 'bell uh - 1 iroquois_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'in service_5': 'in service', 'aircraft_6': 'aircraft', 'bell uh - 1 iroquois_7': 'bell uh - 1 iroquois'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'in service_5': [0], 'aircraft_6': [1], 'bell uh - 1 iroquois_7': [2]} | ['aircraft', 'origin', 'type', 'versions', 'in service'] | [['cessna a - 37 dragonfly', 'united states', 'attack / fighter', 'a - 37b', '12 ( 16 delivered )'], ['fma ia 58 pucarã ¡', 'argentina', 'attack', 'a - 58', '5 ( 6 delivered )'], ['lockheed c - 130 hercules', 'united states', 'transport / utility', 'c - 130b', '2'], ['embraer emb 110 bandeirante', 'brazil', 'transport / utility', 'c - 95', '3'], ['beechcraft twin bonanza', 'united states', 'transport / utility', 'd50', '1'], ['casa c - 212 aviocar', 'spain', 'transport', 'c - 212 - 200', '2'], ['embraer emb 120 brasilia', 'brazil', 'transport', 'emb 120', '1'], ['cessna 206 stationair', 'united states', 'utility / liaison', 'u206h', '10'], ['beechcraft b58 baron', 'united states', 'trainer / liaison', 'b - 58', '2'], ['british aerospace 125', 'united kingdom', 'vip transport', '700a 600a', '2'], ['aermacchi sf260', 'italy', 'trainer', 't - 260 eu', '12'], ['pilatus pc - 7 turbo trainer', 'switzerland', 'trainer', '- 92', '5 ( 6 delivered )'], ['cessna t - 41 mescalero', 'united states', 'trainer', 't - 41d', '7'], ['aerospatiale as 365 dauphin', 'france', 'liaison / transport', 'as 365', '1'], ['bell 212 twin huey', 'united states', 'transport / utility', 'bell 212', '4'], ['bell uh - 1 iroquois', 'united states', 'transport / utility', 'uh - 1h', '13']] |
2008 - 09 tampa bay lightning season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Tampa_Bay_Lightning_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17360840-9.html.csv | unique | only one of the matches had an attendance of over 20,000 . | {'scope': 'all', 'row': '12', 'col': '6', 'col_other': 'n/a', 'criterion': 'greater_than', 'value': '20000', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'attendance', '20000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose attendance record is greater than 20000 .', 'tostr': 'filter_greater { all_rows ; attendance ; 20000 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; attendance ; 20000 } } = true', 'tointer': 'select the rows whose attendance record is greater than 20000 . there is only one such row in the table .'} | only { filter_greater { all_rows ; attendance ; 20000 } } = true | select the rows whose attendance record is greater than 20000 . 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, 'attendance_4': 4, '20000_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_greater_0': 'filter_greater', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '20000_5': '20000'} | {'only_1': [2], 'result_2': [], 'filter_greater_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '20000_5': [0]} | ['game', 'date', 'opponent', 'score', 'location', 'attendance', 'record', 'points'] | [['63', 'march 1', 'calgary flames', '8 - 6', 'pengrowth saddledome', '19289', '21 - 30 - 12', '54'], ['64', 'march 3', 'pittsburgh penguins', '1 - 3', 'st pete times forum', '19908', '21 - 31 - 12', '54'], ['65', 'march 6', 'st louis blues', '3 - 4 ot', 'st pete times forum', '13831', '21 - 31 - 13', '55'], ['66', 'march 7', 'carolina hurricanes', '3 - 9', 'st pete times forum', '15692', '21 - 32 - 13', '55'], ['67', 'march 11', 'ottawa senators', '2 - 3 ot', 'scotiabank place', '19231', '21 - 32 - 14', '56'], ['68', 'march 12', 'toronto maple leafs', '4 - 1', 'air canada centre', '19209', '22 - 32 - 14', '58'], ['69', 'march 14', 'florida panthers', '4 - 3 so', 'bankatlantic center', '17734', '23 - 32 - 14', '60'], ['70', 'march 17', 'toronto maple leafs', '3 - 4 so', 'st pete times forum', '18793', '23 - 32 - 15', '61'], ['71', 'march 19', 'washington capitals', '2 - 5', 'st pete times forum', '16541', '23 - 33 - 15', '61'], ['72', 'march 21', 'atlanta thrashers', '3 - 4 so', 'st pete times forum', '15391', '23 - 33 - 16', '62'], ['73', 'march 24', 'columbus blue jackets', '2 - 1 ot', 'st pete times forum', '14454', '24 - 33 - 16', '64'], ['74', 'march 26', 'montreal canadiens', '2 - 3 ot', 'bell centre', '21273', '24 - 33 - 17', '65'], ['75', 'march 27', 'washington capitals', '3 - 5', 'verizon center', '18277', '24 - 34 - 17', '65'], ['76', 'march 29', 'ottawa senators', '0 - 3', 'st pete times forum', '16427', '24 - 35 - 17', '65'], ['77', 'march 31', 'boston bruins', '1 - 3', 'td banknorth garden', '16996', '24 - 36 - 17', '65']] |
1946 vfl season | https://en.wikipedia.org/wiki/1946_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809368-18.html.csv | aggregation | the average crowd attendance for games in the 1946 vfl season was 16000 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '16000', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '16000', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '16000'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 16000 } = true', 'tointer': 'the average of the crowd record of all rows is 16000 .'} | round_eq { avg { all_rows ; crowd } ; 16000 } = true | the average of the crowd record of all rows is 16000 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '16000_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '16000_5': '16000'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '16000_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['north melbourne', '11.8 ( 74 )', 'south melbourne', '15.19 ( 109 )', 'arden street oval', '7000', '24 august 1946'], ['footscray', '23.27 ( 165 )', 'hawthorn', '8.5 ( 53 )', 'western oval', '12000', '24 august 1946'], ['fitzroy', '9.11 ( 65 )', 'melbourne', '10.9 ( 69 )', 'brunswick street oval', '19000', '24 august 1946'], ['collingwood', '17.26 ( 128 )', 'geelong', '7.11 ( 53 )', 'victoria park', '11000', '24 august 1946'], ['st kilda', '11.9 ( 75 )', 'essendon', '16.20 ( 116 )', 'junction oval', '9000', '24 august 1946'], ['richmond', '16.29 ( 125 )', 'carlton', '13.15 ( 93 )', 'punt road oval', '38000', '24 august 1946']] |
hit 'n run tour | https://en.wikipedia.org/wiki/Hit_%27n_Run_Tour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12946465-1.html.csv | majority | most of the hit 'n run tour concert cities were in 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]} | ['date', 'city', 'country', 'venue', 'attendance'] | [['july 20 , 2007', 'sault ste marie , michigan', 'united states', 'kewadin casino', '10000'], ['july 21 , 2007', 'cadott , wisconsin', 'united states', 'cadott rock fest', '35000'], ['july 25 , 2007', 'anaheim , california', 'united states', 'cisco customer appreciation event', '1000'], ['july 27 , 2007', 'san jacinto , california', 'united states', 'soboba casino arena', '3500'], ['september 15 , 2007', 'whistler , british columbia', 'canada', 'blackcomb mountain', 'canceled'], ['october 26 , 2007', 'paradise , nevada', 'united states', 'mandalay bay resort', '1500']] |
10k run | https://en.wikipedia.org/wiki/10K_run | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17370134-2.html.csv | unique | only one of the 10k run recordholders is not from kenya . | {'scope': 'all', 'row': '3', 'col': '4', 'col_other': 'n/a', 'criterion': 'not_equal', 'value': 'kenya', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_str_not_eq', 'args': ['all_rows', 'nation', 'kenya'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nation record does not match to kenya .', 'tostr': 'filter_not_eq { all_rows ; nation ; kenya }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_not_eq { all_rows ; nation ; kenya } } = true', 'tointer': 'select the rows whose nation record does not match to kenya . there is only one such row in the table .'} | only { filter_not_eq { all_rows ; nation ; kenya } } = true | select the rows whose nation record does not match to kenya . there is only one such row in the table . | 2 | 2 | {'only_1': 1, 'result_2': 2, 'filter_str_not_eq_0': 0, 'all_rows_3': 3, 'nation_4': 4, 'kenya_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_str_not_eq_0': 'filter_str_not_eq', 'all_rows_3': 'all_rows', 'nation_4': 'nation', 'kenya_5': 'kenya'} | {'only_1': [2], 'result_2': [], 'filter_str_not_eq_0': [1], 'all_rows_3': [0], 'nation_4': [0], 'kenya_5': [0]} | ['rank', 'time', 'athlete', 'nation', 'date', 'race'] | [['1', '26:44', 'leonard patrick komon', 'kenya', '26 september 2010', 'singelloop utrecht'], ['2', '27:01', 'micah kipkemboi kogo', 'kenya', '29 march 2009', 'parelloop brunssum'], ['3', '27:02', 'haile gebrselassie', 'ethiopia', '11 december 2002', 'doha , qatar'], ['4 =', '27:04', 'joseph kimani', 'kenya', '4 july 1996', 'peachtree road race'], ['4 =', '27:04', 'josphat kiprono menjo', 'kenya', '18 april 2010', 'cursa de bombers'], ['6', '27:09', 'peter kamais lotagor', 'kenya', '6 september 2009', 'tilburg 10k'], ['7 =', '27:11', 'sammy kipketer', 'kenya', '30 march 2002', 'crescent city classic'], ['7 =', '27:11', 'sammy kirop kitwara', 'kenya', '26 september 2010', 'singelloop utrecht'], ['9 =', '27:19', 'moses ndiema masai', 'kenya', '28 february 2010', "world 's best 10k"], ['9 =', '27:19', 'geoffrey kiprono mutai', 'kenya', '26 june 2011', 'boston 10k']] |
port de pailhères | https://en.wikipedia.org/wiki/Port_de_Pailh%C3%A8res | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12095103-1.html.csv | superlative | the year that the port de pailheres had the least amount of stages was in 2013 . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'stage'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; stage }'}, 'year'], 'result': '2013', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; stage } ; year }'}, '2013'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; stage } ; year } ; 2013 } = true', 'tointer': 'select the row whose stage record of all rows is minimum . the year record of this row is 2013 .'} | eq { hop { argmin { all_rows ; stage } ; year } ; 2013 } = true | select the row whose stage record of all rows is minimum . the year record of this row is 2013 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'stage_5': 5, 'year_6': 6, '2013_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'stage_5': 'stage', 'year_6': 'year', '2013_7': '2013'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'stage_5': [0], 'year_6': [1], '2013_7': [2]} | ['year', 'stage', 'category', 'start', 'finish', 'leader at the summit'] | [['2013', '8', 'hc', 'castres', 'ax - 3 domaines', 'nairo quintana ( col )'], ['2010', '14', 'hc', 'revel', 'ax - 3 domaines', 'christophe riblon ( fra )'], ['2007', '14', 'hc', 'mazamet', 'plateau - de - beille', 'rubén pérez ( esp )'], ['2005', '14', 'hc', 'agde', 'ax - 3 domaines', 'georg totschnig ( aut )'], ['2003', '13', '1', 'toulouse', 'ax - 3 domaines', 'juan miguel mercado ( esp )']] |
mobile telephony in africa | https://en.wikipedia.org/wiki/Mobile_telephony_in_Africa | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29395291-2.html.csv | ordinal | south africa has the second highest number of overall subscribers to a provider . | {'row': '2', 'col': '4', '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', 'subscribers ( 2006 ) ( thousands )', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; subscribers ( 2006 ) ( thousands ) ; 2 }'}, 'country'], 'result': 'south africa', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; subscribers ( 2006 ) ( thousands ) ; 2 } ; country }'}, 'south africa'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; subscribers ( 2006 ) ( thousands ) ; 2 } ; country } ; south africa } = true', 'tointer': 'select the row whose subscribers ( 2006 ) ( thousands ) record of all rows is 2nd maximum . the country record of this row is south africa .'} | eq { hop { nth_argmax { all_rows ; subscribers ( 2006 ) ( thousands ) ; 2 } ; country } ; south africa } = true | select the row whose subscribers ( 2006 ) ( thousands ) record of all rows is 2nd maximum . the country record of this row is south africa . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'subscribers (2006) (thousands)_5': 5, '2_6': 6, 'country_7': 7, 'south africa_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', 'subscribers (2006) (thousands)_5': 'subscribers ( 2006 ) ( thousands )', '2_6': '2', 'country_7': 'country', 'south africa_8': 'south africa'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'subscribers (2006) (thousands)_5': [0], '2_6': [0], 'country_7': [1], 'south africa_8': [2]} | ['provider', 'country', 'subscribers ( 2005 ) ( thousands )', 'subscribers ( 2006 ) ( thousands )', 'growth %'] | [['airtel', 'kenya , uganda', '37600', '31800', '54.9'], ['vodacom', 'south africa', '17600', '21800', '23.9'], ['mtn', 'south africa', '10235', '12483', '22'], ['mtn', 'nigeria', '8370', '12281', '47'], ['glo mobile', 'nigeria', '9000', '11000', '22'], ['maroc', 'morocco', '8237', '10707', '30'], ['djezzy', 'algeria', '7109', '10531', '48'], ['mobinil', 'egypt', '66960', '9267', '38'], ['vodafone', 'egypt', '6125', '8704', '42'], ['mobilis', 'algeria', '4908', '7476', '52']] |
helena suková | https://en.wikipedia.org/wiki/Helena_Sukov%C3%A1 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1920271-3.html.csv | unique | the 1992 us open was the only tennis tournament that helena suková played with tom nijssen as her partner . | {'scope': 'all', 'row': '2', 'col': '5', 'col_other': '2,3', 'criterion': 'equal', 'value': 'tom nijssen', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'partner', 'tom nijssen'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose partner record fuzzily matches to tom nijssen .', 'tostr': 'filter_eq { all_rows ; partner ; tom nijssen }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; partner ; tom nijssen } }', 'tointer': 'select the rows whose partner record fuzzily matches to tom nijssen . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'partner', 'tom nijssen'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose partner record fuzzily matches to tom nijssen .', 'tostr': 'filter_eq { all_rows ; partner ; tom nijssen }'}, 'year'], 'result': '1992', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; partner ; tom nijssen } ; year }'}, '1992'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; partner ; tom nijssen } ; year } ; 1992 }', 'tointer': 'the year record of this unqiue row is 1992 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'partner', 'tom nijssen'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose partner record fuzzily matches to tom nijssen .', 'tostr': 'filter_eq { all_rows ; partner ; tom nijssen }'}, 'championship'], 'result': 'us open', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; partner ; tom nijssen } ; championship }'}, 'us open'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; partner ; tom nijssen } ; championship } ; us open }', 'tointer': 'the championship record of this unqiue row is us open .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; partner ; tom nijssen } ; year } ; 1992 } ; eq { hop { filter_eq { all_rows ; partner ; tom nijssen } ; championship } ; us open } }', 'tointer': 'the year record of this unqiue row is 1992 . the championship record of this unqiue row is us open .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; partner ; tom nijssen } } ; and { eq { hop { filter_eq { all_rows ; partner ; tom nijssen } ; year } ; 1992 } ; eq { hop { filter_eq { all_rows ; partner ; tom nijssen } ; championship } ; us open } } } = true', 'tointer': 'select the rows whose partner record fuzzily matches to tom nijssen . there is only one such row in the table . the year record of this unqiue row is 1992 . the championship record of this unqiue row is us open .'} | and { only { filter_eq { all_rows ; partner ; tom nijssen } } ; and { eq { hop { filter_eq { all_rows ; partner ; tom nijssen } ; year } ; 1992 } ; eq { hop { filter_eq { all_rows ; partner ; tom nijssen } ; championship } ; us open } } } = true | select the rows whose partner record fuzzily matches to tom nijssen . there is only one such row in the table . the year record of this unqiue row is 1992 . the championship record of this unqiue row is us open . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'partner_10': 10, 'tom nijssen_11': 11, 'and_6': 6, 'eq_3': 3, 'num_hop_2': 2, 'year_12': 12, '1992_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'championship_14': 14, 'us open_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', 'partner_10': 'partner', 'tom nijssen_11': 'tom nijssen', 'and_6': 'and', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_12': 'year', '1992_13': '1992', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'championship_14': 'championship', 'us open_15': 'us open'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'partner_10': [0], 'tom nijssen_11': [0], 'and_6': [7], 'eq_3': [6], 'num_hop_2': [3], 'year_12': [2], '1992_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'championship_14': [4], 'us open_15': [5]} | ['outcome', 'year', 'championship', 'surface', 'partner', 'opponents', 'score'] | [['winner', '1991', 'french open', 'clay', 'cyril suk', 'caroline vis paul haarhuis', '3 - 6 , 6 - 4 , 6 - 1'], ['runner - up', '1992', 'us open', 'hard', 'tom nijssen', 'nicole provis mark woodforde', '4 - 6 , 6 - 3 , 6 - 3'], ['winner', '1993', 'us open', 'hard', 'todd woodbridge', 'martina navratilova mark woodforde', '6 - 3 , 7 - 6 ( 6 )'], ['runner - up', '1994', 'australian open', 'hard', 'todd woodbridge', 'larisa neiland andrei olhovskiy', '7 - 5 , 6 - 7 ( 0 ) , 6 - 2'], ['winner', '1994', 'wimbledon', 'grass', 'todd woodbridge', 'lori mcneil t j middleton', '3 - 6 , 7 - 5 , 6 - 3'], ['winner', '1996', 'wimbledon', 'grass', 'cyril suk', 'larisa neiland mark woodforde', '1 - 6 , 6 - 3 , 6 - 2'], ['winner', '1997', 'wimbledon', 'grass', 'cyril suk', 'larisa neiland andrei olhovskiy', '4 - 6 , 6 - 3 , 6 - 4']] |
wheelchair basketball at the 2000 summer paralympics | https://en.wikipedia.org/wiki/Wheelchair_basketball_at_the_2000_Summer_Paralympics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18781865-4.html.csv | superlative | canada won the most gold medals in wheelchair basketball at the 2000 summer paralympics . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'gold'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; gold }'}, 'nation'], 'result': 'canada ( can )', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; gold } ; nation }'}, 'canada ( can )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; gold } ; nation } ; canada ( can ) } = true', 'tointer': 'select the row whose gold record of all rows is maximum . the nation record of this row is canada ( can ) .'} | eq { hop { argmax { all_rows ; gold } ; nation } ; canada ( can ) } = true | select the row whose gold record of all rows is maximum . the nation record of this row is canada ( can ) . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'gold_5': 5, 'nation_6': 6, 'canada (can)_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'gold_5': 'gold', 'nation_6': 'nation', 'canada (can)_7': 'canada ( can )'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'gold_5': [0], 'nation_6': [1], 'canada (can)_7': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'canada ( can )', '2', '0', '0', '2'], ['2', 'australia ( aus )', '0', '1', '0', '1'], ['2', 'netherlands ( ned )', '0', '1', '0', '1'], ['4', 'united states ( usa )', '0', '0', '1', '1'], ['4', 'japan ( jpn )', '0', '0', '1', '1']] |
rajah broadcasting network | https://en.wikipedia.org/wiki/Rajah_Broadcasting_Network | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12379297-4.html.csv | comparative | the callsign dyrj - fm on the rajah broadcasting network operates on a higher frequency than the callsign dwrj - fm . | {'row_1': '4', 'row_2': '2', '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', 'callsign', 'dyjr - fm'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose callsign record fuzzily matches to dyjr - fm .', 'tostr': 'filter_eq { all_rows ; callsign ; dyjr - fm }'}, 'frequency'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; callsign ; dyjr - fm } ; frequency }', 'tointer': 'select the rows whose callsign record fuzzily matches to dyjr - fm . take the frequency record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'callsign', 'dwdj - fm'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose callsign record fuzzily matches to dwdj - fm .', 'tostr': 'filter_eq { all_rows ; callsign ; dwdj - fm }'}, 'frequency'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; callsign ; dwdj - fm } ; frequency }', 'tointer': 'select the rows whose callsign record fuzzily matches to dwdj - fm . take the frequency record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; callsign ; dyjr - fm } ; frequency } ; hop { filter_eq { all_rows ; callsign ; dwdj - fm } ; frequency } } = true', 'tointer': 'select the rows whose callsign record fuzzily matches to dyjr - fm . take the frequency record of this row . select the rows whose callsign record fuzzily matches to dwdj - fm . take the frequency record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; callsign ; dyjr - fm } ; frequency } ; hop { filter_eq { all_rows ; callsign ; dwdj - fm } ; frequency } } = true | select the rows whose callsign record fuzzily matches to dyjr - fm . take the frequency record of this row . select the rows whose callsign record fuzzily matches to dwdj - fm . take the frequency 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, 'callsign_7': 7, 'dyjr - fm_8': 8, 'frequency_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'callsign_11': 11, 'dwdj - fm_12': 12, 'frequency_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', 'callsign_7': 'callsign', 'dyjr - fm_8': 'dyjr - fm', 'frequency_9': 'frequency', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'callsign_11': 'callsign', 'dwdj - fm_12': 'dwdj - fm', 'frequency_13': 'frequency'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'callsign_7': [0], 'dyjr - fm_8': [0], 'frequency_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'callsign_11': [1], 'dwdj - fm_12': [1], 'frequency_13': [3]} | ['branding', 'callsign', 'frequency', 'power ( kw )', 'station type', 'location'] | [['rjfm 100.3', 'dzrj - fm', '100.3 mhz', '25 kw', 'originating', 'metro manila'], ['rjfm 91.1 baguio', 'dwdj - fm', '91.1 mhz', '5 kw', 'relay', 'baguio cordillera region'], ['rjfm 96.5 tuguegarao', 'dwrj - fm', '96.5 mhz', '5 kw', 'relay', 'tuguegarao northern luzon region'], ['rjfm 99.1 palawan', 'dyjr - fm', '99.1 mhz', '5 kw', 'relay', 'puerto princesa palawan'], ['rjfm 98.3 iloilo', 'dynj - fm', '98.3 mhz', '5 kw', 'relay', 'iloilo western visayas region'], ['rjfm 99.9 bacolod', 'dyfj - fm', '99.9 mhz', '5 kw', 'relay', 'bacolod western visayas region'], ['rjfm 100.3 cebu', 'dyrj - fm', '100.3 mhz', '20 kw', 'originating', 'cebu central visayas region'], ['rjfm 88.5 cagayan de oro', 'dxrj - fm', '88.5 mhz', '10 kw', 'relay', 'cagayan de oro northern mindanao region'], ['rjfm 100.3 davao', 'dxdj - fm', '100.3 mhz', '20 kw', 'originating', 'davao southern mindanao egion']] |
richmond spiders men 's basketball | https://en.wikipedia.org/wiki/Richmond_Spiders_men%27s_basketball | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10645911-2.html.csv | superlative | the player that played the most games for the richmond spiders was kevin anderson . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '2', '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', 'games'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; games }'}, 'player'], 'result': 'kevin anderson', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; games } ; player }'}, 'kevin anderson'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; games } ; player } ; kevin anderson } = true', 'tointer': 'select the row whose games record of all rows is maximum . the player record of this row is kevin anderson .'} | eq { hop { argmax { all_rows ; games } ; player } ; kevin anderson } = true | select the row whose games record of all rows is maximum . the player record of this row is kevin anderson . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'games_5': 5, 'player_6': 6, 'kevin anderson_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'games_5': 'games', 'player_6': 'player', 'kevin anderson_7': 'kevin anderson'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'games_5': [0], 'player_6': [1], 'kevin anderson_7': [2]} | ['rank', 'player', 'years', 'games', 'ppg avg', 'total points'] | [['1', 'johnny newman', '1982 - 86', '122', '19.5', '2383'], ['2', 'kevin anderson', '2007 - 11', '139', '15.6', '2165'], ['3', 'mike perry', '1977 - 81', '108', '19.9', '2145'], ['4', 'ed harrison', '1952 - 56', '115', '16.0', '1843'], ['5', 'david gonzalvez', '2006 - 10', '131', '13.2', '1727'], ['6', 'john schweitz', '1978 - 82', '109', '15.8', '1723'], ['7', 'curtis blair', '1988 - 92', '125', '13.0', '1630'], ['8', 'peter woolfolk', '1984 - 88', '123', '13.0', '1604'], ['9', 'ken atkinson', '1986 - 90', '125', '12.4', '1549'], ['10', 'jarod stevenson', '1994 - 98', '111', '13.4', '1482']] |
cricket in world war ii | https://en.wikipedia.org/wiki/Cricket_in_World_War_II | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10791018-1.html.csv | majority | the majority of england 's cricket players during world war ii were right-handed batting style players . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'right-handed', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'batting style', 'right-handed'], 'result': True, 'ind': 0, 'tointer': 'for the batting style records of all rows , most of them fuzzily match to right-handed .', 'tostr': 'most_eq { all_rows ; batting style ; right-handed } = true'} | most_eq { all_rows ; batting style ; right-handed } = true | for the batting style records of all rows , most of them fuzzily match to right-handed . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'batting style_3': 3, 'right-handed_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'batting style_3': 'batting style', 'right-handed_4': 'right-handed'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'batting style_3': [0], 'right-handed_4': [0]} | ['name', 'club', 'birth date', 'batting style', 'bowling style'] | [['a j holmes', 'sussex', '30 june 1899 ( aged 40 )', 'right - handed', 'none'], ['h t bartlett', 'sussex', '07 october 1914 ( aged 24 )', 'left - handed', 'none'], ['h e dollery', 'warwickshire', '14 october 1914 ( aged 24 )', 'right - handed', 'none'], ['h gimblett', 'somerset', '19 october 1914 ( aged 24 )', 'right - handed', 'right arm medium pace'], ['r h c human', 'worcestershire', '11 may 1909 ( aged 30 )', 'right - handed', 'right arm medium pace'], ['j g langridge', 'sussex', '10 february 1910 ( aged 29 )', 'right - handed', 'right arm medium pace'], ['r e s wyatt', 'warwickshire', '02 may 1901 ( aged 38 )', 'right - handed', 'right arm medium pace']] |
list of republic of doyle episodes | https://en.wikipedia.org/wiki/List_of_Republic_of_Doyle_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27547668-2.html.csv | unique | episode 11 of republic of doyle was the only directed by steve scaini . | {'scope': 'all', 'row': '10', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'steve scaini', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'directed by', 'steve scaini'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose directed by record fuzzily matches to steve scaini .', 'tostr': 'filter_eq { all_rows ; directed by ; steve scaini }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; directed by ; steve scaini } }', 'tointer': 'select the rows whose directed by record fuzzily matches to steve scaini . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'directed by', 'steve scaini'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose directed by record fuzzily matches to steve scaini .', 'tostr': 'filter_eq { all_rows ; directed by ; steve scaini }'}, ''], 'result': '11', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; directed by ; steve scaini } ; }'}, '11'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; directed by ; steve scaini } ; } ; 11 }', 'tointer': 'the record of this unqiue row is 11 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; directed by ; steve scaini } } ; eq { hop { filter_eq { all_rows ; directed by ; steve scaini } ; } ; 11 } } = true', 'tointer': 'select the rows whose directed by record fuzzily matches to steve scaini . there is only one such row in the table . the record of this unqiue row is 11 .'} | and { only { filter_eq { all_rows ; directed by ; steve scaini } } ; eq { hop { filter_eq { all_rows ; directed by ; steve scaini } ; } ; 11 } } = true | select the rows whose directed by record fuzzily matches to steve scaini . there is only one such row in the table . the record of this unqiue row is 11 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'directed by_7': 7, 'steve scaini_8': 8, 'eq_3': 3, 'num_hop_2': 2, '_9': 9, '11_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'directed by_7': 'directed by', 'steve scaini_8': 'steve scaini', 'eq_3': 'eq', 'num_hop_2': 'num_hop', '_9': '', '11_10': '11'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'directed by_7': [0], 'steve scaini_8': [0], 'eq_3': [4], 'num_hop_2': [3], '_9': [2], '11_10': [3]} | ['', 'title', 'directed by', 'written by', 'viewers', 'original airdate', 'prod code'] | [['1', 'fathers and sons', 'mike clattenburg', 'allan hawco , perry chafe and malcolm macrury', '969000', 'january 6 , 2010', '101'], ['2', 'the return of the grievous angel', 'steve dimarco', 'allan hawco and avrum jacobson', '715000', 'january 13 , 2010', '102'], ['3', 'duchess of george', 'mike clattenburg', 'allan hawco , perry chafe and malcolm macrury', '685000', 'january 20 , 2010', '103'], ['5', 'hit and rum', 'steve dimarco', 'matt maclennan', '594000', 'february 3 , 2010', '105'], ['6', 'the one who got away', 'larry mclean', 'jesse mckeown', '1012000', 'february 10 , 2010', '106'], ['7', 'the woman who knew too little', 'robert lieberman', 'jeremy boxen', '1053000', 'march 3 , 2010', '107'], ['8', 'the tell - tale safe', 'jerry ciccoritti', 'john callaghan and steve cochrane', '986000', 'march 10 , 2010', '108'], ['9', 'he sleeps with the chips', 'phil earnshaw', 'perry chafe', '908000', 'march 17 , 2010', '109'], ['10', 'the pen is mightier than the doyle', 'robert lieberman', 'steve cochrane and avrum jacobson', '897000', 'march 24 , 2010', '110'], ['11', 'a horse divided', 'steve scaini', 'jesse mckeown', '902000', 'march 31 , 2010', '111']] |
hadise ( album ) | https://en.wikipedia.org/wiki/Hadise_%28album%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16431493-1.html.csv | count | there are only three tracks that are over 4 minutes long . | {'scope': 'all', 'criterion': 'greater_than', 'value': '4:00', 'result': '3', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'length', '4:00'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose length record is greater than 4:00 .', 'tostr': 'filter_greater { all_rows ; length ; 4:00 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_greater { all_rows ; length ; 4:00 } }', 'tointer': 'select the rows whose length record is greater than 4:00 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater { all_rows ; length ; 4:00 } } ; 3 } = true', 'tointer': 'select the rows whose length record is greater than 4:00 . the number of such rows is 3 .'} | eq { count { filter_greater { all_rows ; length ; 4:00 } } ; 3 } = true | select the rows whose length record is greater than 4:00 . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'length_5': 5, '4:00_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'length_5': 'length', '4:00_6': '4:00', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'length_5': [0], '4:00_6': [0], '3_7': [2]} | ['track', 'title', 'songwriter ( s )', 'producer ( s )', 'length'] | [['1', 'intro', 'hadise açıkgöz', 'yves jongen', '0:52'], ['2', 'my man and the devil on his shoulder', 'hadise açıkgöz , yves jongen', 'hadise açıkgöz , yves jongen', '4:34'], ['3', 'my body', 'hadise açıkgöz', 'l t hutton', '3:14'], ['4', 'prisoner', 'hadise açıkgöz , stefaan fernande , elio deepcore', 'hadise açıkgöz , stefaan fernande , elio deepcore', '3:52'], ['5', 'a good kiss', 'hadise açıkgöz , yves jongen', 'hadise açıkgöz , yves jongen', '3:29'], ['6', 'all together', 'hadise açıkgöz , yves jongen', 'hadise açıkgöz , yves jongen', '3:07'], ['7', 'men chase women choose', 'hadise açıkgöz , yves jongen', 'hadise açıkgöz , yves jongen', '3:08'], ['8', 'creep', 'hadise açıkgöz , stefaan fernande , stano simor', 'hadise açıkgöz , stefaan fernande , stano simor', '3:44'], ['9', 'good morning baby', 'yves jongen', 'yves jongen', '4:16'], ['10', "do n't ask", 'hadise açıkgöz , yves jongen', 'hadise açıkgöz , yves jongen', '3:00'], ['11', 'intimate', 'hadise açıkgöz , stefaan fernande , luca chiaravall', 'hadise açıkgöz , stefaan fernande , luca chiaravall', '3:37'], ['12', 'busy bee', 'hadise açıkgöz , yves jongen', 'hadise açıkgöz , yves jongen', '3:32'], ['13', 'comfort zone', 'hadise açıkgöz , yves jongen', 'hadise açıkgöz , yves jongen', '4:09'], ['14', 'who am i', 'hadise açıkgöz , yves jongen', 'hadise açıkgöz , yves jongen', '3:14'], ['15', 'a song for my mother', 'hadise açıkgöz , stefaan fernande , luca chiaravall', 'hadise açıkgöz , stefaan fernande , luca chiaravall', '3:32'], ['16', 'aşkkolik', 'deniz erten', 'özgür buldum', '3:30'], ['17', 'deli oğlan', 'sezen aksu', 'hadise açıkgöz , yves jongen', '3:12']] |
list of mexican municipalities | https://en.wikipedia.org/wiki/List_of_Mexican_municipalities | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12453743-1.html.csv | superlative | the biggest mexican municipality in terms of squared kilometers is ensenada . | {'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', 'area ( km2 )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; area ( km2 ) }'}, 'municipality'], 'result': 'ensenada', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; area ( km2 ) } ; municipality }'}, 'ensenada'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; area ( km2 ) } ; municipality } ; ensenada } = true', 'tointer': 'select the row whose area ( km2 ) record of all rows is maximum . the municipality record of this row is ensenada .'} | eq { hop { argmax { all_rows ; area ( km2 ) } ; municipality } ; ensenada } = true | select the row whose area ( km2 ) record of all rows is maximum . the municipality record of this row is ensenada . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'area (km2)_5': 5, 'municipality_6': 6, 'ensenada_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'area (km2)_5': 'area ( km2 )', 'municipality_6': 'municipality', 'ensenada_7': 'ensenada'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'area (km2)_5': [0], 'municipality_6': [1], 'ensenada_7': [2]} | ['inegi code', 'municipality', 'municipal seat', 'population ( 2010 )', 'area ( km2 )'] | [['001', 'ensenada', 'ensenada', '466814', '52482.4'], ['002', 'mexicali', 'mexicali', '956826', '13700'], ['003', 'tecate', 'tecate', '101079', '3079'], ['004', 'tijuana', 'tijuana', '1559683', '879.2'], ['005', 'playas de rosarito', 'rosarito', '90668', '513.32']] |
1960 st. louis cardinals ( nfl ) season | https://en.wikipedia.org/wiki/1960_St._Louis_Cardinals_%28NFL%29_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16642141-1.html.csv | majority | the st. louis cardinals lost most of the games they played during october , 1960 . | {'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 23 , 1960', 'los angeles rams', 'w 43 - 21', '47448'], ['2', 'october 2 , 1960', 'new york giants', 'l 35 - 14', '26089'], ['3', 'october 9 , 1960', 'philadelphia eagles', 'l 31 - 27', '33701'], ['4', 'october 16 , 1960', 'pittsburgh steelers', 'l 27 - 14', '22971'], ['5', 'october 23 , 1960', 'dallas cowboys', 'w 12 - 10', '23128'], ['6', 'october 30 , 1960', 'new york giants', 'w 20 - 13', '58516'], ['7', 'november 6 , 1960', 'washington redskins', 'w 44 - 7', '22458'], ['8', 'november 13 , 1960', 'cleveland browns', 'l 28 - 27', '49192'], ['9', 'november 20 , 1960', 'washington redskins', 'w 26 - 14', '23848'], ['10', 'november 27 , 1960', 'cleveland browns', 't 17 - 17', '26146'], ['11', 'december 4 , 1960', 'philadelphia eagles', 'l 20 - 6', '21358'], ['13', 'december 18 , 1960', 'pittsburgh steelers', 'w 38 - 7', '20840']] |
andrew pattison | https://en.wikipedia.org/wiki/Andrew_Pattison | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10833727-1.html.csv | unique | of the competitions that andrew pattison participated in , the only one in dayton , oh was on february 8th , 1976 . | {'scope': 'all', 'row': '8', 'col': '3', 'col_other': '2', 'criterion': 'fuzzily_match', 'value': 'dayton , oh', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'championship', 'dayton , oh'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose championship record fuzzily matches to dayton , oh .', 'tostr': 'filter_eq { all_rows ; championship ; dayton , oh }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; championship ; dayton , oh } }', 'tointer': 'select the rows whose championship record fuzzily matches to dayton , oh . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'championship', 'dayton , oh'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose championship record fuzzily matches to dayton , oh .', 'tostr': 'filter_eq { all_rows ; championship ; dayton , oh }'}, 'date'], 'result': '8 february 1976', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; championship ; dayton , oh } ; date }'}, '8 february 1976'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; championship ; dayton , oh } ; date } ; 8 february 1976 }', 'tointer': 'the date record of this unqiue row is 8 february 1976 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; championship ; dayton , oh } } ; eq { hop { filter_eq { all_rows ; championship ; dayton , oh } ; date } ; 8 february 1976 } } = true', 'tointer': 'select the rows whose championship record fuzzily matches to dayton , oh . there is only one such row in the table . the date record of this unqiue row is 8 february 1976 .'} | and { only { filter_eq { all_rows ; championship ; dayton , oh } } ; eq { hop { filter_eq { all_rows ; championship ; dayton , oh } ; date } ; 8 february 1976 } } = true | select the rows whose championship record fuzzily matches to dayton , oh . there is only one such row in the table . the date record of this unqiue row is 8 february 1976 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'championship_7': 7, 'dayton, oh_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, '8 february 1976_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'championship_7': 'championship', 'dayton, oh_8': 'dayton , oh', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', '8 february 1976_10': '8 february 1976'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'championship_7': [0], 'dayton, oh_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], '8 february 1976_10': [3]} | ['outcome', 'date', 'championship', 'opponent in the final', 'score in the final'] | [['runner - up', '23 july 1972', 'columbus , ohio , us', 'jimmy connors', '5 - 7 , 3 - 6 , 5 - 7'], ['runner - up', '30 july 1972', 'tanglewood , usa', 'bob hewitt', '6 - 3 , 3 - 6 , 1 - 6'], ['runner - up', '14 august 1972', 'montreal , canada', 'ilie năstase', '4 - 6 , 3 - 6'], ['winner', '8 april 1974', 'monte carlo , monaco', 'ilie năstase', '5 - 7 , 6 - 3 , 6 - 4'], ['winner', '15 april 1974', 'johannesburg , south africa', 'john alexander', '6 - 3 , 7 - 5'], ['runner - up', '28 october 1974', 'vienna , austria', 'vitas gerulaitis', '4 - 6 , 6 - 3 , 3 - 6 , 2 - 6'], ['runner - up', '7 january 1976', 'columbus , ohio , us', 'arthur ashe', '6 - 3 , 3 - 6 , 6 - 7 ( 4 )'], ['runner - up', '8 february 1976', 'dayton , ohio , us', 'jaime fillol sr', '4 - 6 , 7 - 6 , 4 - 6'], ['winner', '14 september 1977', 'laguna niguel , us', 'colin dibley', '2 - 6 , 7 - 6 , 6 - 4'], ['winner', '27 november 1979', 'johannesburg , south africa', 'víctor pecci', '2 - 6 , 6 - 3 , 6 - 2 , 6 - 3'], ['runner - up', '7 july 1980', 'newport , rhode island , us', 'vijay amritraj', '1 - 6 , 7 - 5 , 3 - 6']] |
1968 boston patriots season | https://en.wikipedia.org/wiki/1968_Boston_Patriots_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10646744-2.html.csv | comparative | in the 1968 boston patriots season , the attendance on november 17th , 1968 was 29966 higher than on november 24th , 1968 . | {'row_1': '10', 'row_2': '11', 'col': '5', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'november 17 , 1968'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to november 17 , 1968 .', 'tostr': 'filter_eq { all_rows ; date ; november 17 , 1968 }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; november 17 , 1968 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to november 17 , 1968 . take the attendance record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'november 24 , 1968'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to november 24 , 1968 .', 'tostr': 'filter_eq { all_rows ; date ; november 24 , 1968 }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; november 24 , 1968 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to november 24 , 1968 . take the attendance record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; date ; november 17 , 1968 } ; attendance } ; hop { filter_eq { all_rows ; date ; november 24 , 1968 } ; attendance } }', 'tointer': 'select the rows whose date record fuzzily matches to november 17 , 1968 . take the attendance record of this row . select the rows whose date record fuzzily matches to november 24 , 1968 . take the attendance record of this row . the first record is greater than the second record .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'november 17 , 1968'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to november 17 , 1968 .', 'tostr': 'filter_eq { all_rows ; date ; november 17 , 1968 }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; november 17 , 1968 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to november 17 , 1968 . take the attendance record of this row .'}, '48271'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; november 17 , 1968 } ; attendance } ; 48271 }', 'tointer': 'the attendance record of the first row is 48271 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'november 24 , 1968'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to november 24 , 1968 .', 'tostr': 'filter_eq { all_rows ; date ; november 24 , 1968 }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; november 24 , 1968 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to november 24 , 1968 . take the attendance record of this row .'}, '18305'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; november 24 , 1968 } ; attendance } ; 18305 }', 'tointer': 'the attendance record of the second row is 18305 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; date ; november 17 , 1968 } ; attendance } ; 48271 } ; eq { hop { filter_eq { all_rows ; date ; november 24 , 1968 } ; attendance } ; 18305 } }', 'tointer': 'the attendance record of the first row is 48271 . the attendance record of the second row is 18305 .'}], 'result': True, 'ind': 8, 'tostr': 'and { greater { hop { filter_eq { all_rows ; date ; november 17 , 1968 } ; attendance } ; hop { filter_eq { all_rows ; date ; november 24 , 1968 } ; attendance } } ; and { eq { hop { filter_eq { all_rows ; date ; november 17 , 1968 } ; attendance } ; 48271 } ; eq { hop { filter_eq { all_rows ; date ; november 24 , 1968 } ; attendance } ; 18305 } } } = true', 'tointer': 'select the rows whose date record fuzzily matches to november 17 , 1968 . take the attendance record of this row . select the rows whose date record fuzzily matches to november 24 , 1968 . take the attendance record of this row . the first record is greater than the second record . the attendance record of the first row is 48271 . the attendance record of the second row is 18305 .'} | and { greater { hop { filter_eq { all_rows ; date ; november 17 , 1968 } ; attendance } ; hop { filter_eq { all_rows ; date ; november 24 , 1968 } ; attendance } } ; and { eq { hop { filter_eq { all_rows ; date ; november 17 , 1968 } ; attendance } ; 48271 } ; eq { hop { filter_eq { all_rows ; date ; november 24 , 1968 } ; attendance } ; 18305 } } } = true | select the rows whose date record fuzzily matches to november 17 , 1968 . take the attendance record of this row . select the rows whose date record fuzzily matches to november 24 , 1968 . take the attendance record of this row . the first record is greater than the second record . the attendance record of the first row is 48271 . the attendance record of the second row is 18305 . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'greater_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'date_11': 11, 'november 17 , 1968_12': 12, 'attendance_13': 13, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'date_15': 15, 'november 24 , 1968_16': 16, 'attendance_17': 17, 'and_7': 7, 'eq_5': 5, '48271_18': 18, 'eq_6': 6, '18305_19': 19} | {'and_8': 'and', 'result_9': 'true', 'greater_4': 'greater', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', 'november 17 , 1968_12': 'november 17 , 1968', 'attendance_13': 'attendance', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'date_15': 'date', 'november 24 , 1968_16': 'november 24 , 1968', 'attendance_17': 'attendance', 'and_7': 'and', 'eq_5': 'eq', '48271_18': '48271', 'eq_6': 'eq', '18305_19': '18305'} | {'and_8': [9], 'result_9': [], 'greater_4': [8], 'num_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'date_11': [0], 'november 17 , 1968_12': [0], 'attendance_13': [2], 'num_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'date_15': [1], 'november 24 , 1968_16': [1], 'attendance_17': [3], 'and_7': [8], 'eq_5': [7], '48271_18': [5], 'eq_6': [7], '18305_19': [6]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 8 , 1968', 'buffalo bills', 'w 16 - 7', '38865'], ['3', 'september 22 , 1968', 'new york jets', 'l 47 - 31', '22002'], ['4', 'september 29 , 1968', 'denver broncos', 'w 20 - 17', '37024'], ['5', 'october 6 , 1968', 'oakland raiders', 'l 41 - 10', '44253'], ['6', 'october 13 , 1968', 'houston oilers', 'l 16 - 0', '32502'], ['7', 'october 20 , 1968', 'buffalo bills', 'w 23 - 6', '21082'], ['8', 'october 27 , 1968', 'new york jets', 'l 48 - 14', '62351'], ['9', 'november 3 , 1968', 'denver broncos', 'l 35 - 14', '18304'], ['10', 'november 10 , 1968', 'san diego chargers', 'l 27 - 17', '19278'], ['11', 'november 17 , 1968', 'kansas city chiefs', 'l 31 - 17', '48271'], ['12', 'november 24 , 1968', 'miami dolphins', 'l 34 - 10', '18305'], ['13', 'december 1 , 1968', 'cincinnati bengals', 'w 33 - 14', '17796'], ['14', 'december 8 , 1968', 'miami dolphins', 'l 38 - 7', '24242'], ['15', 'december 15 , 1968', 'houston oilers', 'l 45 - 17', '34198']] |
2008 - 09 san antonio spurs season | https://en.wikipedia.org/wiki/2008%E2%80%9309_San_Antonio_Spurs_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17288845-5.html.csv | majority | tim duncan was the player with highest points in most of san antonio 's november games in the 2008-09 season . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'tim duncan', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'high points', 'tim duncan'], 'result': True, 'ind': 0, 'tointer': 'for the high points records of all rows , most of them fuzzily match to tim duncan .', 'tostr': 'most_eq { all_rows ; high points ; tim duncan } = true'} | most_eq { all_rows ; high points ; tim duncan } = true | for the high points records of all rows , most of them fuzzily match to tim duncan . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'high points_3': 3, 'tim duncan_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'high points_3': 'high points', 'tim duncan_4': 'tim duncan'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'high points_3': [0], 'tim duncan_4': [0]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'location attendance', 'record'] | [['3', 'november 4', 'dallas', 'l 81 - 98 ( ot )', 'tony parker ( 22 )', 'tim duncan ( 15 )', 'at & t center 17398', '0 - 3'], ['4', 'november 5', 'minnesota', 'w 129 - 125 ( 2ot )', 'tony parker ( 55 )', 'tim duncan ( 16 )', 'target center 11112', '1 - 3'], ['5', 'november 7', 'miami', 'l 83 - 99 ( ot )', 'tim duncan ( 22 )', 'tim duncan ( 11 )', 'at & t center 17387', '1 - 4'], ['6', 'november 11', 'new york', 'w 92 - 80 ( ot )', 'tim duncan ( 23 )', 'tim duncan , fabricio oberto ( 9 )', 'at & t center 16569', '2 - 4'], ['7', 'november 12', 'milwaukee', 'l 78 - 82 ( ot )', 'tim duncan ( 24 )', 'anthony tolliver ( 6 )', 'bradley center 14036', '2 - 5'], ['8', 'november 14', 'houston', 'w 77 - 75 ( ot )', 'tim duncan ( 22 )', 'roger mason ( 9 )', 'at & t center 18797', '3 - 5'], ['9', 'november 16', 'sacramento', 'w 90 - 88 ( ot )', 'michael finley ( 21 )', 'tim duncan ( 10 )', 'arco arena 11699', '4 - 5'], ['10', 'november 17', 'la clippers', 'w 86 - 83 ( ot )', 'roger mason ( 21 )', 'tim duncan ( 15 )', 'staples center 14962', '5 - 5'], ['11', 'november 19', 'denver', 'l 81 - 91 ( ot )', 'george hill ( 20 )', 'tim duncan ( 11 )', 'at & t center 16559', '5 - 6'], ['12', 'november 21', 'utah', 'w 119 - 94 ( ot )', 'roger mason ( 29 )', 'tim duncan ( 7 )', 'at & t center 17354', '6 - 6'], ['13', 'november 24', 'memphis', 'w 94 - 81 ( ot )', 'george hill ( 20 )', 'tim duncan ( 11 )', 'fedexforum 12053', '7 - 6'], ['14', 'november 26', 'chicago', 'w 98 - 88 ( ot )', 'tim duncan ( 21 )', 'george hill ( 11 )', 'at & t center 17837', '8 - 6'], ['15', 'november 28', 'memphis', 'w 109 - 98 ( ot )', 'roger mason ( 20 )', 'tim duncan ( 12 )', 'at & t center 17074', '9 - 6'], ['16', 'november 29', 'houston', 'l 84 - 103 ( ot )', 'tim duncan , matt bonner ( 17 )', 'tim duncan ( 9 )', 'toyota center 18282', '9 - 7']] |
sebastián prieto | https://en.wikipedia.org/wiki/Sebasti%C3%A1n_Prieto | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12746233-2.html.csv | majority | the majority of these tournaments took place on a clay surface . | {'scope': 'all', 'col': '4', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'clay', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'surface', 'clay'], 'result': True, 'ind': 0, 'tointer': 'for the surface records of all rows , all of them fuzzily match to clay .', 'tostr': 'all_eq { all_rows ; surface ; clay } = true'} | all_eq { all_rows ; surface ; clay } = true | for the surface records of all rows , all of them fuzzily match to clay . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'clay_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'clay_4': 'clay'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'clay_4': [0]} | ['outcome', 'date', 'tournament', 'surface', 'partnering', 'opponent in the final', 'score'] | [['winner', 'november 9 , 1998', 'santiago , chile', 'clay', 'mariano hood', 'massimo bertolini devin bowen', '7 - 6 , 6 - 7 , 7 - 6'], ['winner', 'october 4 , 1999', 'palermo , italy', 'clay', 'mariano hood', 'lan bale alberto martín', '6 - 3 , 6 - 1'], ['winner', 'january 28 , 2001', 'bogotá , colombia', 'clay', 'mariano hood', 'martín rodríguez andré sá', '6 - 2 , 6 - 4'], ['winner', 'february 17 , 2003', 'buenos aires , argentina', 'clay', 'mariano hood', 'lucas arnold ker david nalbandian', '6 - 2 , 6 - 2'], ['winner', 'july 18 , 2005', 'stuttgart , germany', 'clay', 'josé acasuso', 'mariano hood tommy robredo', '7 - 6 ( 4 ) , 6 - 3'], ['winner', 'september 12 , 2005', 'bucharest , romania', 'clay', 'josé acasuso', 'victor hănescu andrei pavel', '6 - 3 , 4 - 6 , 6 - 3'], ['winner', 'january 30 , 2006', 'viña del mar , chile', 'clay', 'josé acasuso', 'františek čermák leoš friedl', '7 - 6 ( 2 ) , 6 - 4'], ['winner', 'february 19 , 2007', 'buenos aires , argentina', 'clay', 'martín garcía', 'albert montañés rubén ramírez hidalgo', '6 - 4 , 6 - 2'], ['winner', 'february 2 , 2008', 'viña del mar , chile', 'clay', 'josé acasuso', 'máximo gonzález juan mónaco', '6 - 1 , 3 - 0 , ret'], ['winner', 'february 21 , 2010', 'buenos aires , argentina', 'clay', 'horacio zeballos', 'simon greul peter luczak', '7 - 6 ( 4 ) , 6 - 3']] |
list of supernanny episodes | https://en.wikipedia.org/wiki/List_of_Supernanny_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19897294-10.html.csv | majority | most of the families were featured before the end of february 2005 . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'less_than_eq', 'value': 'february 2005', 'subset': None} | {'func': 'most_less_eq', 'args': ['all_rows', 'original air date', 'february 2005'], 'result': True, 'ind': 0, 'tointer': 'for the original air date records of all rows , most of them are less than or equal to february 2005 .', 'tostr': 'most_less_eq { all_rows ; original air date ; february 2005 } = true'} | most_less_eq { all_rows ; original air date ; february 2005 } = true | for the original air date records of all rows , most of them are less than or equal to february 2005 . | 1 | 1 | {'most_less_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'original air date_3': 3, 'february 2005_4': 4} | {'most_less_eq_0': 'most_less_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'original air date_3': 'original air date', 'february 2005_4': 'february 2005'} | {'most_less_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'original air date_3': [0], 'february 2005_4': [0]} | ['no in series', 'no in season', 'family / families', 'location ( s )', 'original air date'] | [['us1', '1', 'the jeans family', 'denver , co', '1 january 2005'], ['us2', '2', 'the bullard family', 'aurora , co', '24 january 2005'], ['us3', '3', 'the orm family', 'santa clarita , ca', '31 january 2005'], ['us4', '4', 'the wischmeyer family', 'colorado', '7 february 2005'], ['us5', '5', 'the weston family', 'florida', '14 february 2005'], ['us6', '6', 'the bailey family', 'n / a', '21 february 2005'], ['us7', '7', 'the gorbea family', 'california', '28 february 2005'], ['us8', '8', 'the ririe family', 'thousand oaks , ca', '21 march 2005'], ['us9', '9', 'the burnett family', 'n / a', '18 april 2005']] |
swimming at the 2000 summer olympics - women 's 200 metre freestyle | https://en.wikipedia.org/wiki/Swimming_at_the_2000_Summer_Olympics_%E2%80%93_Women%27s_200_metre_freestyle | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12383012-4.html.csv | ordinal | in the women 's 200 meter freestyle race in the swimming events at the 2000 summer olympics , romanian camella potec ranked 2nd with a time of 1:59.54 . | {'scope': 'all', 'row': '2', 'col': '1', 'order': '2', 'col_other': '3,4,5', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_min', 'args': ['all_rows', 'rank', '2'], 'result': '2', 'ind': 0, 'tostr': 'nth_min { all_rows ; rank ; 2 }', 'tointer': 'the 2nd minimum rank record of all rows is 2 .'}, '2'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; rank ; 2 } ; 2 }', 'tointer': 'the 2nd minimum rank record of all rows is 2 .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'rank', '2'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; rank ; 2 }'}, 'name'], 'result': 'camelia potec', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; rank ; 2 } ; name }'}, 'camelia potec'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; rank ; 2 } ; name } ; camelia potec }', 'tointer': 'the name record of the row with 2nd minimum rank record is camelia potec .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'rank', '2'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; rank ; 2 }'}, 'nationality'], 'result': 'romania', 'ind': 5, 'tostr': 'hop { nth_argmin { all_rows ; rank ; 2 } ; nationality }'}, 'romania'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { nth_argmin { all_rows ; rank ; 2 } ; nationality } ; romania }', 'tointer': 'the nationality record of the row with 2nd minimum rank record is romania .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'rank', '2'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; rank ; 2 }'}, 'time'], 'result': '1:59.54', 'ind': 7, 'tostr': 'hop { nth_argmin { all_rows ; rank ; 2 } ; time }'}, '1:59.54'], 'result': True, 'ind': 8, 'tostr': 'eq { hop { nth_argmin { all_rows ; rank ; 2 } ; time } ; 1:59.54 }', 'tointer': 'the time record of the row with 2nd minimum rank record is 1:59.54 .'}], 'result': True, 'ind': 9, 'tostr': 'and { eq { hop { nth_argmin { all_rows ; rank ; 2 } ; nationality } ; romania } ; eq { hop { nth_argmin { all_rows ; rank ; 2 } ; time } ; 1:59.54 } }', 'tointer': 'the nationality record of the row with 2nd minimum rank record is romania . the time record of the row with 2nd minimum rank record is 1:59.54 .'}], 'result': True, 'ind': 10, 'tostr': 'and { eq { hop { nth_argmin { all_rows ; rank ; 2 } ; name } ; camelia potec } ; and { eq { hop { nth_argmin { all_rows ; rank ; 2 } ; nationality } ; romania } ; eq { hop { nth_argmin { all_rows ; rank ; 2 } ; time } ; 1:59.54 } } }', 'tointer': 'the name record of the row with 2nd minimum rank record is camelia potec . the nationality record of the row with 2nd minimum rank record is romania . the time record of the row with 2nd minimum rank record is 1:59.54 .'}], 'result': True, 'ind': 11, 'tostr': 'and { eq { nth_min { all_rows ; rank ; 2 } ; 2 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 2 } ; name } ; camelia potec } ; and { eq { hop { nth_argmin { all_rows ; rank ; 2 } ; nationality } ; romania } ; eq { hop { nth_argmin { all_rows ; rank ; 2 } ; time } ; 1:59.54 } } } } = true', 'tointer': 'the 2nd minimum rank record of all rows is 2 . the name record of the row with 2nd minimum rank record is camelia potec . the nationality record of the row with 2nd minimum rank record is romania . the time record of the row with 2nd minimum rank record is 1:59.54 .'} | and { eq { nth_min { all_rows ; rank ; 2 } ; 2 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 2 } ; name } ; camelia potec } ; and { eq { hop { nth_argmin { all_rows ; rank ; 2 } ; nationality } ; romania } ; eq { hop { nth_argmin { all_rows ; rank ; 2 } ; time } ; 1:59.54 } } } } = true | the 2nd minimum rank record of all rows is 2 . the name record of the row with 2nd minimum rank record is camelia potec . the nationality record of the row with 2nd minimum rank record is romania . the time record of the row with 2nd minimum rank record is 1:59.54 . | 14 | 12 | {'and_11': 11, 'result_12': 12, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_13': 13, 'rank_14': 14, '2_15': 15, '2_16': 16, 'and_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_17': 17, 'rank_18': 18, '2_19': 19, 'name_20': 20, 'camelia potec_21': 21, 'and_9': 9, 'str_eq_6': 6, 'str_hop_5': 5, 'nationality_22': 22, 'romania_23': 23, 'str_eq_8': 8, 'str_hop_7': 7, 'time_24': 24, '1:59.54_25': 25} | {'and_11': 'and', 'result_12': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_13': 'all_rows', 'rank_14': 'rank', '2_15': '2', '2_16': '2', 'and_10': 'and', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_17': 'all_rows', 'rank_18': 'rank', '2_19': '2', 'name_20': 'name', 'camelia potec_21': 'camelia potec', 'and_9': 'and', 'str_eq_6': 'str_eq', 'str_hop_5': 'str_hop', 'nationality_22': 'nationality', 'romania_23': 'romania', 'str_eq_8': 'str_eq', 'str_hop_7': 'str_hop', 'time_24': 'time', '1:59.54_25': '1:59.54'} | {'and_11': [12], 'result_12': [], 'eq_1': [11], 'nth_min_0': [1], 'all_rows_13': [0], 'rank_14': [0], '2_15': [0], '2_16': [1], 'and_10': [11], 'str_eq_4': [10], 'str_hop_3': [4], 'nth_argmin_2': [3, 5, 7], 'all_rows_17': [2], 'rank_18': [2], '2_19': [2], 'name_20': [3], 'camelia potec_21': [4], 'and_9': [10], 'str_eq_6': [9], 'str_hop_5': [6], 'nationality_22': [5], 'romania_23': [6], 'str_eq_8': [9], 'str_hop_7': [8], 'time_24': [7], '1:59.54_25': [8]} | ['rank', 'lane', 'name', 'nationality', 'time'] | [['1', '4', "susie o'neill", 'australia', '1:59.37'], ['2', '3', 'camelia potec', 'romania', '1:59.54'], ['3', '5', 'claudia poll', 'costa rica', '1:59.63'], ['4', '2', 'nadezhda chemezova', 'russia', '1:59.69'], ['5', '6', 'franziska van almsick', 'germany', '2:00.26'], ['6', '1', 'giaan rooney', 'australia', '2:00.84'], ['7', '7', 'carla geurts', 'netherlands', '2:00.88'], ['8', '8', 'rada owen', 'united states', '2:03.34']] |
2008 - 09 leeds united a.f.c. season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Leeds_United_A.F.C._season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17634290-7.html.csv | count | seven of then listed players have a free transfer fee associated with them . | {'scope': 'all', 'criterion': 'equal', 'value': 'free', 'result': '7', 'col': '7', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'transfer fee', 'free'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose transfer fee record fuzzily matches to free .', 'tostr': 'filter_eq { all_rows ; transfer fee ; free }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; transfer fee ; free } }', 'tointer': 'select the rows whose transfer fee record fuzzily matches to free . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; transfer fee ; free } } ; 7 } = true', 'tointer': 'select the rows whose transfer fee record fuzzily matches to free . the number of such rows is 7 .'} | eq { count { filter_eq { all_rows ; transfer fee ; free } } ; 7 } = true | select the rows whose transfer fee record fuzzily matches to free . 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, 'transfer fee_5': 5, 'Free_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', 'transfer fee_5': 'transfer fee', 'Free_6': 'free', '7_7': '7'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'transfer fee_5': [0], 'Free_6': [0], '7_7': [2]} | ['name', 'country', 'type', 'moving from', 'transfer window', 'ends', 'transfer fee', 'source'] | [['robinson', 'eng', 'free agent', 'swansea city', 'summer', '2011', 'free', 'leeds united yorkshire evening post'], ['sheehan', 'ire', 'transferred 1', 'leicester city', 'summer', '2011', 'undisclosed', 'leeds united'], ['showunmi', 'ngr eng', 'free agent', 'bristol city', 'summer', '2010', 'free', 'leeds united'], ['snodgrass', 'sco', 'free agent 1', 'livingston', 'summer', '2011', '35k 2', 'leeds united'], ['becchio', 'arg', 'transferred', 'mérida ud', 'summer', '2011', '300k 3', 'leeds united'], ['telfer', 'sco', 'free agent', 'bournemouth', 'summer', '2009 4', 'free', 'leeds united'], ['christie', 'eng', 'free agent', 'middlesbrough', 'summer', 'n / a 5', 'free', 'leeds united'], ['assoumani', 'mli fra', 'free agent', 'sportfreunde siegen', 'summer', '2009', 'free', 'leeds united'], ['grella', 'usa', 'free agent', 'cary clarets', 'winter', '2010', 'free', 'leeds united'], ['naylor', 'eng', 'transferred', 'ipswich town', 'winter', '2011', 'free', 'leeds united']] |
2011 u.s. f2000 national championship | https://en.wikipedia.org/wiki/2011_U.S._F2000_National_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29285076-2.html.csv | count | for the 2011 u.s. f2000 national championship , when the date was in august , there were 2 times that petri suvanto had the pole position . | {'scope': 'subset', 'criterion': 'equal', 'value': 'petri suvanto', 'result': '2', 'col': '5', 'subset': {'col': '4', 'criterion': 'fuzzily_match', 'value': 'august'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'august'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; august }', 'tointer': 'select the rows whose date record fuzzily matches to august .'}, 'pole position', 'petri suvanto'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to august . among these rows , select the rows whose pole position record fuzzily matches to petri suvanto .', 'tostr': 'filter_eq { filter_eq { all_rows ; date ; august } ; pole position ; petri suvanto }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; date ; august } ; pole position ; petri suvanto } }', 'tointer': 'select the rows whose date record fuzzily matches to august . among these rows , select the rows whose pole position record fuzzily matches to petri suvanto . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; date ; august } ; pole position ; petri suvanto } } ; 2 } = true', 'tointer': 'select the rows whose date record fuzzily matches to august . among these rows , select the rows whose pole position record fuzzily matches to petri suvanto . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; date ; august } ; pole position ; petri suvanto } } ; 2 } = true | select the rows whose date record fuzzily matches to august . among these rows , select the rows whose pole position record fuzzily matches to petri suvanto . 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, 'date_6': 6, 'august_7': 7, 'pole position_8': 8, 'petri suvanto_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', 'date_6': 'date', 'august_7': 'august', 'pole position_8': 'pole position', 'petri suvanto_9': 'petri suvanto', '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], 'date_6': [0], 'august_7': [0], 'pole position_8': [1], 'petri suvanto_9': [1], '2_10': [3]} | ['rnd', 'circuit', 'location', 'date', 'pole position', 'fastest lap', 'most laps led', 'winning driver', 'winning team', 'supporting'] | [['1', 'sebring raceway', 'sebring , florida', 'march 17', 'zach veach', 'zach veach', 'zach veach', 'zach veach', 'andretti autosport', 'alms'], ['2', 'sebring raceway', 'sebring , florida', 'march 18', 'zach veach', 'luke ellery', 'petri suvanto luke ellery', 'luke ellery', 'jdc motorsports', 'alms'], ['3', 'streets of st petersburg', 'st petersburg , florida', 'march 26', 'spencer pigot', 'petri suvanto', 'spencer pigot', 'spencer pigot', 'andretti autosport', 'indycar series'], ['4', 'streets of st petersburg', 'st petersburg , florida', 'march 27', 'petri suvanto', 'petri suvanto', 'petri suvanto', 'petri suvanto', 'cape motorsports', 'indycar series'], ['5', 'lucas oil raceway at indianapolis', 'clermont , indiana', 'may 28', 'petri suvanto', 'petri suvanto', 'petri suvanto', 'petri suvanto', 'cape motorsports', 'usac midgets'], ['6', 'milwaukee mile', 'west allis , wisconsin', 'june 19', 'zach veach', 'luke ellery', 'wayne boyd', 'wayne boyd', 'belardi auto racing', 'indycar series'], ['7', 'mid - ohio sports car course', 'lexington , ohio', 'august 6', 'petri suvanto', 'petri suvanto', 'petri suvanto', 'petri suvanto', 'cape motorsports', 'indycar series'], ['8', 'mid - ohio sports car course', 'lexington , ohio', 'august 7', 'petri suvanto', 'petri suvanto', 'petri suvanto', 'petri suvanto', 'cape motorsports', 'indycar series'], ['9', 'road america', 'elkhart lake , wisconsin', 'august 19', 'spencer pigot', 'spencer pigot', 'wayne boyd spencer pigot', 'spencer pigot', 'andretti autosport', 'alms'], ['10', 'road america', 'elkhart lake , wisconsin', 'august 20', 'spencer pigot', 'spencer pigot', 'wayne boyd spencer pigot', 'petri suvanto', 'cape motorsports', 'alms'], ['11', 'streets of baltimore', 'baltimore , maryland', 'september 3', 'petri suvanto', 'spencer pigot', 'wayne boyd', 'wayne boyd', 'belardi auto racing', 'indycar series']] |
volleyball at the 2008 summer olympics - men 's team rosters | https://en.wikipedia.org/wiki/Volleyball_at_the_2008_Summer_Olympics_%E2%80%93_Men%27s_team_rosters | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18499677-2.html.csv | unique | guo peng was the only player on the 2008 summer olympic team from the army club . | {'scope': 'all', 'row': '3', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'army', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '2008 club', 'army'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 2008 club record fuzzily matches to army .', 'tostr': 'filter_eq { all_rows ; 2008 club ; army }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; 2008 club ; army } }', 'tointer': 'select the rows whose 2008 club record fuzzily matches to army . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '2008 club', 'army'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 2008 club record fuzzily matches to army .', 'tostr': 'filter_eq { all_rows ; 2008 club ; army }'}, 'name'], 'result': 'guo peng', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; 2008 club ; army } ; name }'}, 'guo peng'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; 2008 club ; army } ; name } ; guo peng }', 'tointer': 'the name record of this unqiue row is guo peng .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; 2008 club ; army } } ; eq { hop { filter_eq { all_rows ; 2008 club ; army } ; name } ; guo peng } } = true', 'tointer': 'select the rows whose 2008 club record fuzzily matches to army . there is only one such row in the table . the name record of this unqiue row is guo peng .'} | and { only { filter_eq { all_rows ; 2008 club ; army } } ; eq { hop { filter_eq { all_rows ; 2008 club ; army } ; name } ; guo peng } } = true | select the rows whose 2008 club record fuzzily matches to army . there is only one such row in the table . the name record of this unqiue row is guo peng . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, '2008 club_7': 7, 'army_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'guo peng_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', '2008 club_7': '2008 club', 'army_8': 'army', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'guo peng_10': 'guo peng'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], '2008 club_7': [0], 'army_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'guo peng_10': [3]} | ['name', 'height', 'weight', 'spike', '2008 club'] | [['bian hongmin', 'm', '-', 'cm ( in )', 'zhejiang'], ['yuan zhi', 'm', '-', 'cm ( in )', 'liaoning'], ['guo peng', 'm', '-', 'cm ( in )', 'army'], ['shi hairong', 'm', '-', 'cm ( in )', 'jiangsu'], ['cui jianjun', 'm', '-', 'cm ( in )', 'henan'], ['jiao shuai', 'm', '-', 'cm ( in )', 'henan'], ['yu dawei', 'm', '-', 'cm ( in )', 'shangdong'], ['shen qiong', 'm', '-', 'cm ( in )', 'shanghai'], ['jiang fudong', 'm', '-', 'cm ( in )', 'sichuan'], ['ren qi', 'm', '-', 'cm ( in )', 'shanghai'], ['sui shengsheng', 'm', '-', 'cm ( in )', 'liaoning'], ['fang yingchao', 'm', '-', 'cm ( in )', 'shanghai']] |
1995 - 96 philadelphia flyers season | https://en.wikipedia.org/wiki/1995%E2%80%9396_Philadelphia_Flyers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14344570-5.html.csv | ordinal | the third january overtime game played by the 1995-96 philadelphia flyers was against the st. louis blues . | {'scope': 'subset', 'row': '4', 'col': '2', 'order': '3', 'col_other': '3', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '4', 'criterion': 'fuzzily_match', 'value': 'ot'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', 'ot'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; score ; ot }', 'tointer': 'select the rows whose score record fuzzily matches to ot .'}, 'january', '3'], 'result': None, 'ind': 1, 'tostr': 'nth_argmin { filter_eq { all_rows ; score ; ot } ; january ; 3 }'}, 'opponent'], 'result': 'st louis blues', 'ind': 2, 'tostr': 'hop { nth_argmin { filter_eq { all_rows ; score ; ot } ; january ; 3 } ; opponent }'}, 'st louis blues'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmin { filter_eq { all_rows ; score ; ot } ; january ; 3 } ; opponent } ; st louis blues } = true', 'tointer': 'select the rows whose score record fuzzily matches to ot . select the row whose january record of these rows is 3rd minimum . the opponent record of this row is st louis blues .'} | eq { hop { nth_argmin { filter_eq { all_rows ; score ; ot } ; january ; 3 } ; opponent } ; st louis blues } = true | select the rows whose score record fuzzily matches to ot . select the row whose january record of these rows is 3rd minimum . the opponent record of this row is st louis blues . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'score_6': 6, 'ot_7': 7, 'january_8': 8, '3_9': 9, 'opponent_10': 10, 'st louis blues_11': 11} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmin_1': 'nth_argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'score_6': 'score', 'ot_7': 'ot', 'january_8': 'january', '3_9': '3', 'opponent_10': 'opponent', 'st louis blues_11': 'st louis blues'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'score_6': [0], 'ot_7': [0], 'january_8': [1], '3_9': [1], 'opponent_10': [2], 'st louis blues_11': [3]} | ['game', 'january', 'opponent', 'score', 'record', 'points'] | [['40', '3', 'san jose sharks', '3 - 1', '23 - 11 - 6', '52'], ['41', '4', 'colorado avalanche', '2 - 2 ot', '23 - 11 - 7', '53'], ['42', '9', 'mighty ducks of anaheim', '2 - 2 ot', '23 - 11 - 8', '54'], ['43', '11', 'st louis blues', '4 - 4 ot', '23 - 12 - 9', '55'], ['44', '13', 'new york rangers', '0 - 4', '23 - 13 - 9', '55'], ['45', '15', 'dallas stars', '6 - 1', '24 - 13 - 9', '57'], ['46', '22', 'florida panthers', '1 - 1 ot', '24 - 12 - 10', '58'], ['47', '24', 'new york rangers', '4 - 4 ot', '24 - 12 - 11', '59'], ['48', '27', 'pittsburgh penguins', '4 - 7', '24 - 13 - 11', '59'], ['49', '28', 'washington capitals', '2 - 3 ot', '24 - 14 - 11', '59']] |
iron fist ( album ) | https://en.wikipedia.org/wiki/Iron_Fist_%28album%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1620364-2.html.csv | majority | most versions of the album iron fist were released in the format of vinyl . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'vinyl', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'format', 'vinyl'], 'result': True, 'ind': 0, 'tointer': 'for the format records of all rows , most of them fuzzily match to vinyl .', 'tostr': 'most_eq { all_rows ; format ; vinyl } = true'} | most_eq { all_rows ; format ; vinyl } = true | for the format records of all rows , most of them fuzzily match to vinyl . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'format_3': 3, 'vinyl_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'format_3': 'format', 'vinyl_4': 'vinyl'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'format_3': [0], 'vinyl_4': [0]} | ['date', 'region', 'label', 'catalogue', 'format'] | [['17 april 1982', 'uk', 'bronze', 'bron 539', 'vinyl'], ['17 april 1982', 'north america', 'mercury', 'srm - 1 - 4042', 'vinyl'], ['1982', 'france', 'wea filipacchi music', '893048', 'vinyl'], ['1982', 'germany', 'bronze', '204 636', 'vinyl'], ['21 / dec / 1982', 'yugoslavia', 'jugoton', 'lsbro 11019', 'vinyl'], ['1982', 'australia / nz', 'bronze', 'l - 37841', 'vinyl'], ['1982', 'brazil', 'bronze', '6328444', 'vinyl'], ['1987', 'france', 'castle communications', 'clacd 123', 'cd'], ['1996', 'uk', 'essential , castle music', 'esm cd 372', 'cd'], ['1999', 'us', 'castle music america', 'cdx cmacd - 523', 'cd'], ['2001', 'north america', 'metal - is', 'cdx 85211', 'cd'], ['2003', 'italy', 'earmark', 'lppic 41017', '180 g vinyl picture disc , gatefold cover'], ['2005', 'uk', 'sanctuary', 'smed - 244', '2cd']] |
italian army | https://en.wikipedia.org/wiki/Italian_Army | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1392092-5.html.csv | unique | the italian army had only one type of helicopter in service that originated in the eu . | {'scope': 'all', 'row': '6', 'col': '2', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'european union', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'origin', 'european union'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose origin record fuzzily matches to european union .', 'tostr': 'filter_eq { all_rows ; origin ; european union }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; origin ; european union } } = true', 'tointer': 'select the rows whose origin record fuzzily matches to european union . there is only one such row in the table .'} | only { filter_eq { all_rows ; origin ; european union } } = true | select the rows whose origin record fuzzily matches to european union . there is only one such row in the table . | 2 | 2 | {'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'origin_4': 4, 'european union_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'origin_4': 'origin', 'european union_5': 'european union'} | {'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'origin_4': [0], 'european union_5': [0]} | ['aircraft', 'origin', 'type', 'versions', 'in service'] | [['agusta a109', 'italy', 'recce helicopter', 'a109eoa - 2', '12'], ['agusta a129 mangusta', 'italy', 'attack helicopter', 'cbt', '56'], ['bell uh - 1 iroquois', 'united states', 'transport helicopter', 'ab 205', '42'], ['bell 212', 'united states', 'transport helicopter', 'ab 212', '39'], ['bell 412', 'united states', 'transport helicopter', 'ab 412', '31'], ['nhi nh90', 'european union', 'transport helicopter', 'tth', '21'], ['boeing ch - 47 chinook', 'united states', 'transport helicopter', 'ch - 47c', '14'], ['boeing ch - 47 chinook', 'united states', 'transport helicopter', 'ch - 47f', '0'], ['dornier do 228', 'germany', 'utility transport', 'do 228 - 200', '3'], ['piaggio p180 avanti', 'italy', 'utility transport', 'p180 m', '3']] |
sammy mcilroy | https://en.wikipedia.org/wiki/Sammy_McIlroy | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1699550-1.html.csv | ordinal | the first qualification match that sammy mcilroy had was in 1975 . | {'row': '1', 'col': '6', 'order': '1', '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', 'competition', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; competition ; 1 }'}, 'date'], 'result': '29 october 1975', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; competition ; 1 } ; date }'}, '29 october 1975'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; competition ; 1 } ; date } ; 29 october 1975 } = true', 'tointer': 'select the row whose competition record of all rows is 1st minimum . the date record of this row is 29 october 1975 .'} | eq { hop { nth_argmin { all_rows ; competition ; 1 } ; date } ; 29 october 1975 } = true | select the row whose competition record of all rows is 1st minimum . the date record of this row is 29 october 1975 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'competition_5': 5, '1_6': 6, 'date_7': 7, '29 october 1975_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', 'competition_5': 'competition', '1_6': '1', 'date_7': 'date', '29 october 1975_8': '29 october 1975'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'competition_5': [0], '1_6': [0], 'date_7': [1], '29 october 1975_8': [2]} | ['goal', 'date', 'venue', 'score', 'result', 'competition'] | [['1', '29 october 1975', 'belfast , northern ireland', '2 - 0', '3 - 0', 'euro 1976 qualification'], ['2', '21 september 1977', 'belfast , northern ireland', '2 - 0', '2 - 0', '1978 world cup qualification'], ['3', '15 october 1980', 'belfast , northern ireland', '2 - 0', '3 - 0', '1982 world cup qualification'], ['4', '28 april 1982', 'belfast , northern ireland', '1 - 1', '1 - 1', '1982 british home championship'], ['5', '13 december 1983', 'belfast , northern ireland', '2 - 0', '2 - 0', '1984 british home championship']] |
international cricket in 2008 - 09 | https://en.wikipedia.org/wiki/International_cricket_in_2008%E2%80%9309 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17324788-32.html.csv | majority | in international cricket in 2008-09 , the away captain was always prosper utseya . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'prosper utseya', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'away captain', 'prosper utseya'], 'result': True, 'ind': 0, 'tointer': 'for the away captain records of all rows , all of them fuzzily match to prosper utseya .', 'tostr': 'all_eq { all_rows ; away captain ; prosper utseya } = true'} | all_eq { all_rows ; away captain ; prosper utseya } = true | for the away captain records of all rows , all of them fuzzily match to prosper utseya . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'away captain_3': 3, 'prosper utseya_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'away captain_3': 'away captain', 'prosper utseya_4': 'prosper utseya'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'away captain_3': [0], 'prosper utseya_4': [0]} | ['date', 'home captain', 'away captain', 'venue', 'result'] | [['27 january', 'steve tikolo', 'prosper utseya', 'mombasa sports club , mombasa', 'by 109 runs'], ['29 january', 'steve tikolo', 'prosper utseya', 'mombasa sports club , mombasa', 'by 151 runs'], ['31 january', 'steve tikolo', 'prosper utseya', 'nairobi gymkhana club , nairobi', 'by 4 wickets'], ['1 february', 'steve tikolo', 'prosper utseya', 'nairobi gymkhana club , nairobi', 'by 66 runs'], ['4 february', 'steve tikolo', 'prosper utseya', 'nairobi gymkhana club , nairobi', 'by 7 wickets']] |
phoenix suns all - time roster | https://en.wikipedia.org/wiki/Phoenix_Suns_all-time_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11482079-8.html.csv | superlative | gail goodrich had the greatest number of assists of all the players . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '9', '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', 'asts'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; asts }'}, 'player'], 'result': 'gail goodrich', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; asts } ; player }'}, 'gail goodrich'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; asts } ; player } ; gail goodrich } = true', 'tointer': 'select the row whose asts record of all rows is maximum . the player record of this row is gail goodrich .'} | eq { hop { argmax { all_rows ; asts } ; player } ; gail goodrich } = true | select the row whose asts record of all rows is maximum . the player record of this row is gail goodrich . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'asts_5': 5, 'player_6': 6, 'gail goodrich_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'asts_5': 'asts', 'player_6': 'player', 'gail goodrich_7': 'gail goodrich'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'asts_5': [0], 'player_6': [1], 'gail goodrich_7': [2]} | ['player', 'pos', 'from', 'school / country', 'rebs', 'asts'] | [['rubén garcés', 'pf', '2000', 'providence', '22', '4'], ['diante garrett', 'g', '2012', 'iowa state', '15', '31'], ['pat garrity', 'pf', '1998', 'notre dame', '75', '18'], ['kenny gattison', 'pf', '1986', 'old dominion', '271', '36'], ['armen gilliam', 'pf', '1987', 'unlv', '1045', '132'], ['gordan giriček', 'g / f', '2008', 'croatia', '51', '35'], ['georgi glouchkov', 'pf', '1985', 'bulgaria', '163', '32'], ['grant gondrezick', 'sg', '1986', 'pepperdine', '110', '81'], ['gail goodrich', 'pg', '1968', 'ucla', '777', '1123'], ['archie goodwin', 'g', '2013', 'kentucky', '1', '0'], ['marcin gortat', 'c', '2010', 'poland', '1688', '237'], ['brian grant', 'f / c', '2005', 'xavier', '57', '7'], ['greg grant', 'pg', '1989', 'trenton state', '59', '168'], ['a c green', 'f / c', '1993', 'oregon state', '2114', '353'], ['gerald green', 'g / f', '2013', 'gulf shores academy ( tx )', '2', '0'], ['lamar green', 'pf', '1969', 'morehead state', '2186', '247'], ['gary gregor', 'pf', '1968', 'south carolina', '711', '96'], ['greg griffin', 'f', '1977', 'idaho state', '103', '24'], ['taylor griffin', 'f', '2009', 'oklahoma', '2', '1'], ['tom gugliotta', 'pf', '1999', 'north carolina state', '1438', '353']] |
1959 - 60 segunda división | https://en.wikipedia.org/wiki/1959%E2%80%9360_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17710217-2.html.csv | superlative | real avilés cf has more draws than any other club in the 1959 - 60 segunda división . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '15', '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', 'draws'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; draws }'}, 'club'], 'result': 'real avilés cf', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; draws } ; club }'}, 'real avilés cf'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; draws } ; club } ; real avilés cf } = true', 'tointer': 'select the row whose draws record of all rows is maximum . the club record of this row is real avilés cf .'} | eq { hop { argmax { all_rows ; draws } ; club } ; real avilés cf } = true | select the row whose draws record of all rows is maximum . the club record of this row is real avilés cf . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'draws_5': 5, 'club_6': 6, 'real avilés cf_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'draws_5': 'draws', 'club_6': 'club', 'real avilés cf_7': 'real avilés cf'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'draws_5': [0], 'club_6': [1], 'real avilés cf_7': [2]} | ['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', 'real santander', '30', '42', '17', '8', '5', '63', '28', '+ 35'], ['2', 'rc celta de vigo', '30', '40', '18', '4', '8', '63', '37', '+ 26'], ['3', 'cd orense', '30', '37', '15', '7', '8', '56', '41', '+ 15'], ['4', 'deportivo la coruña', '30', '35', '16', '3', '11', '56', '47', '+ 9'], ['5', 'real gijón', '30', '32', '14', '4', '12', '56', '44', '+ 12'], ['6', 'cd tarrasa', '30', '31', '12', '7', '11', '47', '44', '+ 3'], ['7', 'cd sabadell cf', '30', '31', '13', '5', '12', '52', '41', '+ 11'], ['8', 'sd indautxu', '30', '29', '13', '3', '14', '56', '54', '+ 2'], ['9', 'baracaldo ah', '30', '29', '11', '7', '12', '53', '51', '+ 2'], ['10', 'cd condal', '30', '29', '11', '7', '12', '46', '45', '+ 1'], ['11', 'cd basconia', '30', '27', '11', '5', '14', '41', '57', '- 16'], ['12', 'cultural leonesa', '30', '27', '10', '7', '13', '47', '61', '- 14'], ['13', 'deportivo alavés', '30', '24', '8', '8', '14', '44', '70', '- 26'], ['14', 'club sestao', '30', '24', '9', '6', '15', '30', '47', '- 17'], ['15', 'real avilés cf', '30', '22', '6', '10', '14', '38', '52', '- 14'], ['16', 'club ferrol', '30', '21', '9', '3', '18', '50', '79', '- 29']] |
miss asia pageant | https://en.wikipedia.org/wiki/Miss_Asia_Pageant | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2876467-3.html.csv | superlative | china has won the miss asia pageant more than the other competitors have . | {'scope': 'all', 'col_superlative': '2', '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', 'first place'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; first place }'}, 'region represented'], 'result': 'china', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; first place } ; region represented }'}, 'china'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; first place } ; region represented } ; china } = true', 'tointer': 'select the row whose first place record of all rows is maximum . the region represented record of this row is china .'} | eq { hop { argmax { all_rows ; first place } ; region represented } ; china } = true | select the row whose first place record of all rows is maximum . the region represented record of this row is china . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'first place_5': 5, 'region represented_6': 6, 'china_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'first place_5': 'first place', 'region represented_6': 'region represented', 'china_7': 'china'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'first place_5': [0], 'region represented_6': [1], 'china_7': [2]} | ['region represented', 'first place', 'second place', 'third place', 'total top 3 placements', 'first place winning year ( s ) ( if applicable )'] | [['china', '5', '0', '4', '9', '2004 , 2005 , 2009 , 2010 , 2011'], ['hong kong', '2', '5', '1', '8', '2007 , 2008'], ['kazakhstan', '1', '0', '0', '1', '2006'], ['korea', '0', '2', '1', '3', 'n / a'], ['canada', '0', '1', '0', '1', 'n / a'], ['tajikistan', '0', '1', '0', '1', 'n / a'], ['taiwan', '1', '0', '2', '3', '2012']] |
1991 foster 's cup | https://en.wikipedia.org/wiki/1991_Foster%27s_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16387700-1.html.csv | aggregation | at the 1991 foster 's cup , home teams had an average score of 15.18 . | {'scope': 'all', 'col': '2', 'type': 'average', 'result': '15.18', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'home team score'], 'result': '15.18', 'ind': 0, 'tostr': 'avg { all_rows ; home team score }'}, '15.18'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; home team score } ; 15.18 } = true', 'tointer': 'the average of the home team score record of all rows is 15.18 .'} | round_eq { avg { all_rows ; home team score } ; 15.18 } = true | the average of the home team score record of all rows is 15.18 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'home team score_4': 4, '15.18_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'home team score_4': 'home team score', '15.18_5': '15.18'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'home team score_4': [0], '15.18_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'ground', 'crowd', 'date'] | [['carlton', '27.9 ( 171 )', 'fitzroy', '13.8 ( 86 )', 'north hobart oval', '10100', 'sunday 3 february'], ['footscray', '9.6 ( 60 )', 'hawthorn', '19.25 ( 139 )', 'waverley park', '13196', 'wednesday 6 february'], ['collingwood', '11.17 ( 83 )', 'brisbane', '20.20 ( 140 )', 'gabba', '12461', 'saturday 10 february'], ['geelong', '11.13 ( 79 )', 'adelaide', '23.18 ( 156 )', 'football park', '20069', 'wednesday 13 february'], ['st kilda', '12.10 ( 82 )', 'west coast', '9.11 ( 65 )', 'waverley park', '13625', 'saturday 16 february'], ['melbourne', '15.13 ( 103 )', 'richmond', '12.10 ( 82 )', 'waverley park', '14993', 'wednesday 20 february'], ['north melbourne', '19.20 ( 134 )', 'sydney', '13.17 ( 95 )', 'bruce stadium', '5120', 'sunday 24 february']] |
forces of satan records | https://en.wikipedia.org/wiki/Forces_of_Satan_Records | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14728538-1.html.csv | majority | most of the titles were released with a full length format . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'full - length', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'format', 'full - length'], 'result': True, 'ind': 0, 'tointer': 'for the format records of all rows , most of them fuzzily match to full - length .', 'tostr': 'most_eq { all_rows ; format ; full - length } = true'} | most_eq { all_rows ; format ; full - length } = true | for the format records of all rows , most of them fuzzily match to full - length . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'format_3': 3, 'full - length_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'format_3': 'format', 'full - length_4': 'full - length'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'format_3': [0], 'full - length_4': [0]} | ['artist', 'title', 'release date', 'format', 'cat'] | [['gorgoroth', 'bergen 1996', 'november 2007', 'mcd / 7 pic disc', 'fsr001'], ['ophiolatry', 'transmutation', 'january 21 , 2008', 'full - length', 'fsr002'], ['ophiolatry', 'antievangelistical process ( re - release )', '2009', 'full - length', 'fsr003'], ['black flame', 'imperivm', 'june 23 , 2008', 'full - length', 'fsr004'], ['tangorodrim', 'unholy metal way ( re - release )', '2009', 'full - length', 'fsr005'], ['tangorodrim', 'those who unleashed ( re - release )', '2009', 'full - length', 'fsr006'], ['triumfall', 'antithesis of all flesh', 'june 15 , 2009', 'full - length', 'fsr007']] |
2007 - 08 isthmian league | https://en.wikipedia.org/wiki/2007%E2%80%9308_Isthmian_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17494040-8.html.csv | superlative | the highest attendance for the 2008-08 isthmian league was at the game where afc hornchurch was the home team . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'tie no'], 'result': '51', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; tie no }'}, '51'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; tie no } ; 51 } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the tie no record of this row is 51 .'} | eq { hop { argmax { all_rows ; attendance } ; tie no } ; 51 } = true | select the row whose attendance record of all rows is maximum . the tie no record of this row is 51 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'tie no_6': 6, '51_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', 'tie no_6': 'tie no', '51_7': '51'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'tie no_6': [1], '51_7': [2]} | ['tie no', 'home team', 'score', 'away team', 'attendance'] | [['51', 'afc hornchurch', '1 - 2', 'ramsgate', '216'], ['52', 'arlesey town', '1 - 4', 'edgware town', '79'], ['53', 'heybridge swifts', '3 - 0', 'dartford', '152'], ['54', 'horsham', '1 - 2', 'walton casuals', '187'], ['55', 'redbridge', '0 - 1', 'afc sudbury', '76'], ['56', 'tonbridge angels', '1 - 3', 'carshalton athletic', '202'], ['57', 'tooting & mitcham united', '1 - 0', 'whyteleafe', '105'], ['58', 'wealdstone', '1 - 0', 'ashford town ( middx )', '88']] |
conference carolinas | https://en.wikipedia.org/wiki/Conference_Carolinas | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11658094-1.html.csv | count | two of the schools in the carolinas conference have the nickname of the saints . | {'scope': 'all', 'criterion': 'equal', 'value': 'saints', 'result': '1', 'col': '7', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nickname', 'saints'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nickname record fuzzily matches to saints .', 'tostr': 'filter_eq { all_rows ; nickname ; saints }'}], 'result': '1', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; nickname ; saints } }', 'tointer': 'select the rows whose nickname record fuzzily matches to saints . the number of such rows is 1 .'}, '1'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; nickname ; saints } } ; 1 } = true', 'tointer': 'select the rows whose nickname record fuzzily matches to saints . the number of such rows is 1 .'} | eq { count { filter_eq { all_rows ; nickname ; saints } } ; 1 } = true | select the rows whose nickname record fuzzily matches to saints . the number of such rows is 1 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'nickname_5': 5, 'saints_6': 6, '1_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'nickname_5': 'nickname', 'saints_6': 'saints', '1_7': '1'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'nickname_5': [0], 'saints_6': [0], '1_7': [2]} | ['institution', 'location', 'founded', 'type', 'enrollment', 'joined', 'nickname'] | [['barton college', 'wilson , north carolina', '1902', 'private', '1200', '1930 1', 'bulldogs'], ['belmont abbey college', 'belmont , north carolina', '1876', 'private', '1320', '1989', 'crusaders'], ['converse college 2', 'spartanburg , south carolina', '1889', 'private', '750', '2008', 'valkyries'], ['erskine college', 'due west , south carolina', '1839', 'private', '920', '1995', 'flying fleet'], ['king university', 'bristol , tennessee', '1867', 'private', '1800', '2011', 'tornado'], ['leesmcrae college', 'banner elk , north carolina', '1899', 'private', '800', '1993', 'bobcats'], ['limestone college', 'gaffney , south carolina', '1845', 'private', '3300', '1998', 'saints'], ['mount olive college', 'mount olive , north carolina', '1951', 'private', '2500', '1988', 'trojans'], ['north greenville university', 'tigerville , south carolina', '1891', 'private', '2100', '2011', 'crusaders']] |
national football league playoffs | https://en.wikipedia.org/wiki/National_Football_League_playoffs | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1478772-2.html.csv | count | in the national league playoffs , for games in the month of december , two of the games were under 80:00 . | {'scope': 'subset', 'criterion': 'less_than', 'value': '80:00', 'result': '2', 'col': '1', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'december'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'december'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; december }', 'tointer': 'select the rows whose date record fuzzily matches to december .'}, 'length of game', '80:00'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to december . among these rows , select the rows whose length of game record is less than 80:00 .', 'tostr': 'filter_less { filter_eq { all_rows ; date ; december } ; length of game ; 80:00 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_less { filter_eq { all_rows ; date ; december } ; length of game ; 80:00 } }', 'tointer': 'select the rows whose date record fuzzily matches to december . among these rows , select the rows whose length of game record is less than 80:00 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_less { filter_eq { all_rows ; date ; december } ; length of game ; 80:00 } } ; 2 } = true', 'tointer': 'select the rows whose date record fuzzily matches to december . among these rows , select the rows whose length of game record is less than 80:00 . the number of such rows is 2 .'} | eq { count { filter_less { filter_eq { all_rows ; date ; december } ; length of game ; 80:00 } } ; 2 } = true | select the rows whose date record fuzzily matches to december . among these rows , select the rows whose length of game record is less than 80:00 . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_less_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'date_6': 6, 'december_7': 7, 'length of game_8': 8, '80:00_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_less_1': 'filter_less', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'date_6': 'date', 'december_7': 'december', 'length of game_8': 'length of game', '80:00_9': '80:00', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_less_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'date_6': [0], 'december_7': [0], 'length of game_8': [1], '80:00_9': [1], '2_10': [3]} | ['length of game', 'date', 'away team', 'score', 'home team'] | [['82:40', 'december 25 , 1971', 'miami dolphins', '27 - 24', 'kansas city chiefs'], ['77:54', 'december 23 , 1962', 'dallas texans', '20 - 17', 'houston oilers'], ['77:02', 'january 3 , 1987', 'new york jets', '20 - 23', 'cleveland browns'], ['76:42', 'january 12 , 2013', 'baltimore ravens', '38 - 35', 'denver broncos'], ['75:43', 'december 24 , 1977', 'oakland raiders', '37 - 31', 'baltimore colts'], ['75:10', 'january 10 , 2004', 'carolina panthers', '29 - 23', 'st louis rams']] |
1950 green bay packers season | https://en.wikipedia.org/wiki/1950_Green_Bay_Packers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14877831-2.html.csv | majority | the majority of games resulted in losses for the packers in the 1950 green bay packers season . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'loss', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'result', 'loss'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to loss .', 'tostr': 'most_eq { all_rows ; result ; loss } = true'} | most_eq { all_rows ; result ; loss } = true | for the result records of all rows , most of them fuzzily match to loss . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'loss_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'loss_4': 'loss'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'loss_4': [0]} | ['game', 'date', 'opponent', 'result', 'packers points', 'opponents', 'first downs', 'record', 'streak', 'venue', 'attendance'] | [['1', 'september 17', 'detroit lions', 'loss', '7', '45', '11', '0 - 1', 'lost 1', 'city stadium', '22096'], ['2', 'september 24', 'washington redskins', 'win', '35', '21', '21', '1 - 1', 'won 1', 'state fair park', '14109'], ['3', 'oct 1', 'chicago bears', 'win', '31', '21', '8', '2 - 1', 'won 2', 'city stadium', '24893'], ['4', 'oct 8', 'new york yanks', 'loss', '31', '44', '23', '2 - 2', 'lost 1', 'city stadium', '23871'], ['5', 'oct 15', 'chicago bears', 'loss', '14', '28', '11', '2 - 3', 'lost 2', 'wrigley field', '51065'], ['6', 'oct 19', 'new york yanks', 'loss', '17', '35', '14', '2 - 4', 'lost 3', 'yankee stadium', '13661'], ['7', 'nov 5', 'baltimore colts', 'loss', '21', '41', '13', '2 - 5', 'lost 4', 'memorial stadium', '12971'], ['8', 'nov 12', 'los angeles rams', 'loss', '14', '45', '17', '2 - 6', 'lost 5', 'state fair park', '20456'], ['9', 'nov 19', 'detroit lions', 'loss', '21', '24', '16', '2 - 7', 'lost 6', 'briggs stadium', '17752'], ['10', 'nov 26', 'san francisco 49ers', 'win', '25', '21', '13', '3 - 7', 'won 1', 'city stadium', '13196'], ['11', 'dec 3', 'los angeles rams', 'loss', '14', '51', '13', '3 - 8', 'lost 1', 'los angeles memorial coliseum', '39323']] |
1964 st. louis cardinals ( nfl ) season | https://en.wikipedia.org/wiki/1964_St._Louis_Cardinals_%28NFL%29_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16678127-1.html.csv | aggregation | the st. louis cardinals scored an average of nearly 26 points in the 1964 regular season . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '26', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'result'], 'result': '26', 'ind': 0, 'tostr': 'avg { all_rows ; result }'}, '26'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; result } ; 26 } = true', 'tointer': 'the average of the result record of all rows is 26 .'} | round_eq { avg { all_rows ; result } ; 26 } = true | the average of the result record of all rows is 26 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'result_4': 4, '26_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'result_4': 'result', '26_5': '26'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'result_4': [0], '26_5': [1]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 12 , 1964', 'dallas cowboys', 'w 16 - 6', '36605'], ['2', 'september 20 , 1964', 'cleveland browns', 't 33 - 33', '76954'], ['3', 'september 27 , 1964', 'san francisco 49ers', 'w 23 - 13', '30969'], ['4', 'october 4 , 1964', 'washington redskins', 'w 23 - 17', '49219'], ['5', 'october 12 , 1964', 'baltimore colts', 'l 47 - 27', '60213'], ['6', 'october 18 , 1964', 'washington redskins', 'w 38 - 24', '23748'], ['7', 'october 25 , 1964', 'dallas cowboys', 'l 31 - 13', '28253'], ['8', 'november 1 , 1964', 'new york giants', 'l 34 - 17', '63072'], ['9', 'november 8 , 1964', 'pittsburgh steelers', 'w 34 - 30', '28245'], ['10', 'november 15 , 1964', 'new york giants', 't 10 - 10', '29608'], ['11', 'november 22 , 1964', 'philadelphia eagles', 'w 38 - 13', '60671'], ['12', 'november 29 , 1964', 'pittsburgh steelers', 'w 21 - 20', '27807'], ['13', 'december 6 , 1964', 'cleveland browns', 'w 28 - 19', '31585'], ['14', 'december 13 , 1964', 'philadelphia eagles', 'w 36 - 34', '24636']] |
list of rna structure prediction software | https://en.wikipedia.org/wiki/List_of_RNA_structure_prediction_software | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15308316-5.html.csv | majority | most rna structure prediction software has no intra-molecular structure . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'no', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'intra - molecular structure', 'no'], 'result': True, 'ind': 0, 'tointer': 'for the intra - molecular structure records of all rows , most of them fuzzily match to no .', 'tostr': 'most_eq { all_rows ; intra - molecular structure ; no } = true'} | most_eq { all_rows ; intra - molecular structure ; no } = true | for the intra - molecular structure records of all rows , most of them fuzzily match to no . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'intra - molecular structure_3': 3, 'no_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'intra - molecular structure_3': 'intra - molecular structure', 'no_4': 'no'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'intra - molecular structure_3': [0], 'no_4': [0]} | ['name', 'species specific', 'intra - molecular structure', 'comparative', 'link'] | [['diana - microt', 'human , mouse', 'no', 'yes', 'webserver'], ['microtar', 'no', 'no', 'no', 'sourcecode'], ['mitarget', 'no', 'no', 'no', 'webserver'], ['mirror', 'no', 'no', 'no', 'webserver'], ['pictar', '8 vertebrates', 'no', 'yes', 'predictions'], ['pita', 'no', 'yes', 'no', 'executable , webserver , predictions'], ['rna22', 'no', 'no', 'no', 'predictions custom'], ['rnahybrid', 'no', 'no', 'no', 'sourcecode , webserver'], ['sylamer', 'no', 'no', 'no', 'sourcecode webserver'], ['taref', 'yes', 'no', 'no', 'server / sourcecode'], ['p - taref', 'yes', 'no', 'no', 'server / standalone'], ['targetscan', 'vertebrates , flies , nematodes', 'evaluated indirectly', 'yes', 'sourcecode , webserver']] |
list of a1 grand prix seasons | https://en.wikipedia.org/wiki/List_of_A1_Grand_Prix_seasons | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12868148-2.html.csv | aggregation | the average wins scored by each team in all seasons is around 8 . | {'scope': 'all', 'col': '8', 'type': 'average', 'result': '8', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'wins'], 'result': '8', 'ind': 0, 'tostr': 'avg { all_rows ; wins }'}, '8'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; wins } ; 8 } = true', 'tointer': 'the average of the wins record of all rows is 8 .'} | round_eq { avg { all_rows ; wins } ; 8 } = true | the average of the wins record of all rows is 8 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'wins_4': 4, '8_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'wins_4': 'wins', '8_5': '8'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'wins_4': [0], '8_5': [1]} | ['season', 'team', 'racing team', 'chassis', 'engine', 'tyres', 'drivers', 'wins', 'sprints wins', 'main wins', 'poles', 'fastest laps', 'points'] | [['2005 - 06', 'france', 'dams', 'lola', 'zytek', 'cooper avon', 'alexandre prémat nicolas lapierre', '13', '7', '6', '4', '5', '172'], ['2006 - 07', 'germany', 'super nova racing', 'lola', 'zytek', 'cooper avon', 'nico hülkenberg christian vietoris', '9', '3', '6', '3', '3', '128'], ['2007 - 08', 'switzerland', 'max motorsport consulting', 'lola', 'zytek', 'cooper avon', 'neel jani', '4', '2', '2', '5', '5', '168'], ['2008 - 09', 'ireland', 'status grand prix', 'a1 gp', 'ferrari', 'michelin', 'adam carroll', '5', '3', '2', '6', '5', '112'], ['2009 - 10', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled']] |
united states house of representatives elections , 1880 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1880 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1431558-4.html.csv | count | three of the incumbents from south carolina in the united states house of representatives 1880 elections were first elected in the year 1878 . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': '1878', 'result': '3', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'first elected', '1878'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose first elected record fuzzily matches to 1878 .', 'tostr': 'filter_eq { all_rows ; first elected ; 1878 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; first elected ; 1878 } }', 'tointer': 'select the rows whose first elected record fuzzily matches to 1878 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; first elected ; 1878 } } ; 3 } = true', 'tointer': 'select the rows whose first elected record fuzzily matches to 1878 . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; first elected ; 1878 } } ; 3 } = true | select the rows whose first elected record fuzzily matches to 1878 . 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, 'first elected_5': 5, '1878_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', 'first elected_5': 'first elected', '1878_6': '1878', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'first elected_5': [0], '1878_6': [0], '3_7': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result'] | [['south carolina 1', 'john s richardson', 'democratic', '1878', 're - elected'], ['south carolina 2', "michael p o'connor", 'democratic', '1878', 're - elected'], ['south carolina 3', 'd wyatt aiken', 'democratic', '1876', 're - elected'], ['south carolina 4', 'john h evins', 'democratic', '1876', 're - elected'], ['south carolina 5', 'george d tillman', 'democratic', '1878', 're - elected']] |
2003 - 04 new york rangers season | https://en.wikipedia.org/wiki/2003%E2%80%9304_New_York_Rangers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14532362-7.html.csv | comparative | the new york rangers had a game against ottawa senators earlier than nashville predators . | {'row_1': '6', 'row_2': '11', 'col': '2', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'ottawa senators'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to ottawa senators .', 'tostr': 'filter_eq { all_rows ; opponent ; ottawa senators }'}, 'february'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; ottawa senators } ; february }', 'tointer': 'select the rows whose opponent record fuzzily matches to ottawa senators . take the february record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'nashville predators'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to nashville predators .', 'tostr': 'filter_eq { all_rows ; opponent ; nashville predators }'}, 'february'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; nashville predators } ; february }', 'tointer': 'select the rows whose opponent record fuzzily matches to nashville predators . take the february record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; opponent ; ottawa senators } ; february } ; hop { filter_eq { all_rows ; opponent ; nashville predators } ; february } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to ottawa senators . take the february record of this row . select the rows whose opponent record fuzzily matches to nashville predators . take the february record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; opponent ; ottawa senators } ; february } ; hop { filter_eq { all_rows ; opponent ; nashville predators } ; february } } = true | select the rows whose opponent record fuzzily matches to ottawa senators . take the february record of this row . select the rows whose opponent record fuzzily matches to nashville predators . take the february 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, 'opponent_7': 7, 'ottawa senators_8': 8, 'february_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'nashville predators_12': 12, 'february_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', 'opponent_7': 'opponent', 'ottawa senators_8': 'ottawa senators', 'february_9': 'february', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'nashville predators_12': 'nashville predators', 'february_13': 'february'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'ottawa senators_8': [0], 'february_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'nashville predators_12': [1], 'february_13': [3]} | ['game', 'february', 'opponent', 'score', 'record'] | [['54', '2', 'vancouver canucks', '4 - 3', '20 - 23 - 7 - 4'], ['55', '4', 'minnesota wild', '4 - 3', '20 - 24 - 7 - 4'], ['56', '11', 'new jersey devils', '3 - 1', '21 - 24 - 7 - 4'], ['57', '12', 'philadelphia flyers', '2 - 1', '21 - 25 - 7 - 4'], ['58', '14', 'philadelphia flyers', '6 - 2', '21 - 26 - 7 - 4'], ['59', '16', 'ottawa senators', '4 - 1', '21 - 27 - 7 - 4'], ['60', '19', 'new york islanders', '6 - 2', '22 - 27 - 7 - 4'], ['61', '21', 'new jersey devils', '7 - 3', '22 - 28 - 7 - 4'], ['62', '23', 'montreal canadiens', '4 - 1', '22 - 29 - 7 - 4'], ['63', '26', 'new york islanders', '6 - 3', '23 - 29 - 7 - 4'], ['64', '28', 'nashville predators', '2 - 1 ot', '23 - 29 - 7 - 5'], ['65', '29', 'atlanta thrashers', '3 - 2', '23 - 30 - 7 - 5']] |
1999 senior pga tour | https://en.wikipedia.org/wiki/1999_Senior_PGA_Tour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11621747-4.html.csv | superlative | in the 1999 senior pga tour , lee trevino had the most number of wins among the top five ranked golfers . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '3', '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', 'wins'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; wins }'}, 'player'], 'result': 'lee trevino', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; wins } ; player }'}, 'lee trevino'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; wins } ; player } ; lee trevino } = true', 'tointer': 'select the row whose wins record of all rows is maximum . the player record of this row is lee trevino .'} | eq { hop { argmax { all_rows ; wins } ; player } ; lee trevino } = true | select the row whose wins record of all rows is maximum . the player record of this row is lee trevino . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'wins_5': 5, 'player_6': 6, 'lee trevino_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'wins_5': 'wins', 'player_6': 'player', 'lee trevino_7': 'lee trevino'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'wins_5': [0], 'player_6': [1], 'lee trevino_7': [2]} | ['rank', 'player', 'country', 'earnings', 'wins'] | [['1', 'hale irwin', 'united states', '9645485', '25'], ['2', 'jim colbert', 'united states', '8887831', '19'], ['3', 'lee trevino', 'united states', '8666030', '28'], ['4', 'dave stockton', 'united states', '8104786', '14'], ['5', 'bob charles', 'new zealand', '8001710', '23']] |
strikeout | https://en.wikipedia.org/wiki/Strikeout | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-242813-2.html.csv | aggregation | the major league baseball pitchers recorded a combined total of 3471 strikeouts . | {'scope': 'all', 'col': '2', 'type': 'sum', 'result': '3471', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'strikeouts'], 'result': '3471', 'ind': 0, 'tostr': 'sum { all_rows ; strikeouts }'}, '3471'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; strikeouts } ; 3471 } = true', 'tointer': 'the sum of the strikeouts record of all rows is 3471 .'} | round_eq { sum { all_rows ; strikeouts } ; 3471 } = true | the sum of the strikeouts record of all rows is 3471 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'strikeouts_4': 4, '3471_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'strikeouts_4': 'strikeouts', '3471_5': '3471'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'strikeouts_4': [0], '3471_5': [1]} | ['pitcher', 'strikeouts', 'season', 'team', 'league', 'overall rank'] | [['matt kilroy', '513', '1886', 'baltimore orioles', 'aa', '1'], ['toad ramsey', '499', '1886', 'louisville colonels', 'aa', '2'], ['dupee shaw', '451', '1884', 'detroit wolverines / boston reds', 'nl / ua', '4'], ['old hoss radbourn', '441', '1884', 'providence grays', 'nl', '5'], ['charlie buffington', '417', '1884', 'boston beaneaters', 'nl', '6'], ['guy hecker', '385', '1884', 'louisville eclipse', 'aa', '7'], ['nolan ryan', '383', '1973', 'california angels', 'al', '8'], ['sandy koufax', '382', '1965', 'los angeles dodgers', 'nl', '9']] |
farsi1 | https://en.wikipedia.org/wiki/FARSI1 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28803803-1.html.csv | unique | one show on farsi1 had a runtime less than 30 minutes . | {'scope': 'all', 'row': '6', 'col': '6', 'col_other': 'n/a', 'criterion': 'less_than', 'value': '30 minutes', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'running time', '30 minutes'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose running time record is less than 30 minutes .', 'tostr': 'filter_less { all_rows ; running time ; 30 minutes }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; running time ; 30 minutes } } = true', 'tointer': 'select the rows whose running time record is less than 30 minutes . there is only one such row in the table .'} | only { filter_less { all_rows ; running time ; 30 minutes } } = true | select the rows whose running time record is less than 30 minutes . there is only one such row in the table . | 2 | 2 | {'only_1': 1, 'result_2': 2, 'filter_less_0': 0, 'all_rows_3': 3, 'running time_4': 4, '30 minutes_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_less_0': 'filter_less', 'all_rows_3': 'all_rows', 'running time_4': 'running time', '30 minutes_5': '30 minutes'} | {'only_1': [2], 'result_2': [], 'filter_less_0': [1], 'all_rows_3': [0], 'running time_4': [0], '30 minutes_5': [0]} | ['no', 'name', 'country', 'original channel', 'no of episodes', 'running time', 'launched', 'date', 'irst'] | [['1', "lara 's choice", 'croatia', 'nova tv ( 2011 )', '182', '45 minutes', '28 jul 2012', 'saturday to wednesday', '21:00 - 22:00'], ['2', 'falling angel', 'united states', 'telemundo ( 2009 )', '182', '45 minutes', '11 mar 2013', 'saturday to wednesday', '20:00 - 21:00'], ['3', 'elisa', 'italy', 'canale 5 ( 2003 )', '68', '50 minutes', '9 feb 2013', 'saturday to wednesday', '22:00 - 23:00'], ['4', 'the queen of the south', 'united states', 'telemundo ( 2011 )', '62', '45 minutes', '1 oct 2012', 'saturday to wednesday', '12:00 - 13:00'], ['5', 'aurora', 'united states', 'telemundo ( 2010 )', '135', '45 minutes', '5 may 2012', 'saturday to wednesday', '13:00 - 14:00'], ['6', 'still standing', 'united states', 'cbs ( 2002 )', '88', '21 minutes', '9 feb 2013', 'saturday to wednesday', '17:00 - 17:30'], ['7', 'project runway', 'united states', 'bravo ( 2004 )', '58', '45 minutes', '14 feb 2013', 'thursday & friday', '20:00 - 21:00'], ['8', 'a matter of respect', 'italy', 'canale 5 ( 2006 )', '24', '50 minutes', '25 oct 2012', 'thursday & friday', '21:00 - 22:00']] |
1976 - 77 coupe de france | https://en.wikipedia.org/wiki/1976%E2%80%9377_Coupe_de_France | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16982165-1.html.csv | superlative | ogc nice had the highest score of any team listed . | {'scope': 'all', 'col_superlative': '2', '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', 'score'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; score }'}, 'team 1'], 'result': 'ogc nice ( d1 )', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; score } ; team 1 }'}, 'ogc nice ( d1 )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; score } ; team 1 } ; ogc nice ( d1 ) } = true', 'tointer': 'select the row whose score record of all rows is maximum . the team 1 record of this row is ogc nice ( d1 ) .'} | eq { hop { argmax { all_rows ; score } ; team 1 } ; ogc nice ( d1 ) } = true | select the row whose score record of all rows is maximum . the team 1 record of this row is ogc nice ( d1 ) . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'score_5': 5, 'team 1_6': 6, 'ogc nice (d1)_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'score_5': 'score', 'team 1_6': 'team 1', 'ogc nice (d1)_7': 'ogc nice ( d1 )'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'score_5': [0], 'team 1_6': [1], 'ogc nice (d1)_7': [2]} | ['team 1', 'score', 'team 2', '1st round', '2nd round'] | [['sco angers ( d1 )', '0 - 2', 'rc lens ( d1 )', '0 - 1', '0 - 1'], ['nîmes olympique ( d1 )', '1 - 0', 'girondins de bordeaux ( d1 )', '1 - 0', '0 - 0'], ['fc sochaux - montbéliard ( d1 )', '2 - 2', 'paris sg ( d1 )', '1 - 0', '1 - 2'], ['fc rouen ( d2 )', '1 - 3', 'as saint - étienne ( d1 )', '1 - 1', '0 - 2'], ['fc nantes ( d1 )', '3 - 3', 'rc strasbourg ( d2 )', '2 - 0', '1 - 3'], ['stade de reims ( d1 )', '6 - 2', 'as monaco ( d2 )', '2 - 0', '4 - 2'], ['ogc nice ( d1 )', '11 - 2', 'aspv strasbourg ( dh )', '4 - 1', '7 - 1'], ['fc gueugnon ( d2 )', '3 - 3', 'fc lorient ( d2 )', '3 - 2', '0 - 1']] |
visa requirements for croatian citizens | https://en.wikipedia.org/wiki/Visa_requirements_for_Croatian_citizens | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25965003-3.html.csv | majority | all of the countries have a visa-free requirement . | {'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'visa - free', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'conditions of access', 'visa - free'], 'result': True, 'ind': 0, 'tointer': 'for the conditions of access records of all rows , all of them fuzzily match to visa - free .', 'tostr': 'all_eq { all_rows ; conditions of access ; visa - free } = true'} | all_eq { all_rows ; conditions of access ; visa - free } = true | for the conditions of access records of all rows , all of them fuzzily match to visa - free . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'conditions of access_3': 3, 'visa - free_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'conditions of access_3': 'conditions of access', 'visa - free_4': 'visa - free'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'conditions of access_3': [0], 'visa - free_4': [0]} | ['countries and territories', 'conditions of access', 'length of stay permitted', 'fee ( if applicable )', 'access using a croatian identity card'] | [['european union', 'visa - free', 'freedom of movement', 'n / a', 'yes'], ['albania', 'visa - free', '90 days', 'n / a', 'yes'], ['andorra', 'visa - free', '90 days', 'n / a', 'yes'], ['bosnia and herzegovina', 'visa - free', '90 days', 'n / a', 'yes'], ['faroe islands', 'visa - free', '90 days', 'n / a', 'yes'], ['guernsey', 'visa - free', 'unlimited access', 'n / a', 'yes'], ['iceland', 'visa - free', 'freedom of movement', 'n / a', 'passport required'], ['isle of man', 'visa - free', 'unlimited access', 'n / a', 'yes'], ['jersey', 'visa - free', 'unlimited access', 'n / a', 'yes'], ['kosovo', 'visa - free', '90 days', 'n / a', 'yes'], ['liechtenstein', 'visa - free', 'freedom of movement', 'n / a', 'yes'], ['monaco', 'visa - free', '90 days', 'n / a', 'yes'], ['macedonia', 'visa - free', '90 days', 'n / a', 'yes'], ['moldova', 'visa - free', '90 days', 'n / a', 'passport required'], ['montenegro', 'visa - free', '90 days', 'n / a', 'yes'], ['norway', 'visa - free', 'freedom of movement', 'n / a', 'passport required'], ['san marino', 'visa - free', '90 days', 'n / a', 'yes'], ['serbia', 'visa - free', '90 days', 'n / a', 'yes'], ['switzerland', 'visa - free', 'freedom of movement', 'n / a', 'passport required'], ['ukraine', 'visa - free', '90 days', 'n / a', 'passport required']] |
2006 pga championship | https://en.wikipedia.org/wiki/2006_PGA_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12475284-5.html.csv | aggregation | the average to par standing of players in the 2006 pga championship was -7 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '-7', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'to par'], 'result': '-7', 'ind': 0, 'tostr': 'avg { all_rows ; to par }'}, '-7'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; to par } ; -7 } = true', 'tointer': 'the average of the to par record of all rows is -7 .'} | round_eq { avg { all_rows ; to par } ; -7 } = true | the average of the to par record of all rows is -7 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'to par_4': 4, '-7_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'to par_4': 'to par', '-7_5': '-7'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'to par_4': [0], '-7_5': [1]} | ['place', 'player', 'country', 'score', 'to par'] | [['t1', 'billy andrade', 'united states', '67 + 69 = 136', '- 8'], ['t1', 'luke donald', 'england', '68 + 68 = 136', '- 8'], ['t1', 'henrik stenson', 'sweden', '68 + 68 = 136', '- 8'], ['t1', 'tim herron', 'united states', '69 + 67 = 136', '- 8'], ['t5', 'davis love iii', 'united states', '68 + 69 = 137', '- 7'], ['t5', 'geoff ogilvy', 'australia', '69 + 68 = 137', '- 7'], ['t5', 'tiger woods', 'united states', '69 + 68 = 137', '- 7'], ['t8', 'fred funk', 'united states', '69 + 69 = 138', '- 6'], ['t8', 'billy mayfair', 'united states', '69 + 69 = 138', '- 6'], ['t8', 'chris riley', 'united states', '66 + 72 = 138', '- 6'], ['t8', 'david toms', 'united states', '71 + 67 = 138', '- 6']] |
texas longhorns women 's basketball | https://en.wikipedia.org/wiki/Texas_Longhorns_women%27s_basketball | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10779468-2.html.csv | comparative | longhorns women 's basketball has a longer winning streak against missouri than against oklahoma . | {'row_1': '6', 'row_2': '8', 'col': '8', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'texas vs', 'missouri'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose texas vs record fuzzily matches to missouri .', 'tostr': 'filter_eq { all_rows ; texas vs ; missouri }'}, 'current streak'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; texas vs ; missouri } ; current streak }', 'tointer': 'select the rows whose texas vs record fuzzily matches to missouri . take the current streak record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'texas vs', 'oklahoma'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose texas vs record fuzzily matches to oklahoma .', 'tostr': 'filter_eq { all_rows ; texas vs ; oklahoma }'}, 'current streak'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; texas vs ; oklahoma } ; current streak }', 'tointer': 'select the rows whose texas vs record fuzzily matches to oklahoma . take the current streak record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; texas vs ; missouri } ; current streak } ; hop { filter_eq { all_rows ; texas vs ; oklahoma } ; current streak } } = true', 'tointer': 'select the rows whose texas vs record fuzzily matches to missouri . take the current streak record of this row . select the rows whose texas vs record fuzzily matches to oklahoma . take the current streak record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; texas vs ; missouri } ; current streak } ; hop { filter_eq { all_rows ; texas vs ; oklahoma } ; current streak } } = true | select the rows whose texas vs record fuzzily matches to missouri . take the current streak record of this row . select the rows whose texas vs record fuzzily matches to oklahoma . take the current streak 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, 'texas vs_7': 7, 'missouri_8': 8, 'current streak_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'texas vs_11': 11, 'oklahoma_12': 12, 'current streak_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', 'texas vs_7': 'texas vs', 'missouri_8': 'missouri', 'current streak_9': 'current streak', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'texas vs_11': 'texas vs', 'oklahoma_12': 'oklahoma', 'current streak_13': 'current streak'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'texas vs_7': [0], 'missouri_8': [0], 'current streak_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'texas vs_11': [1], 'oklahoma_12': [1], 'current streak_13': [3]} | ['texas vs', 'overall record', 'austin', "opponent 's venue", 'neutral site', 'last 5 meetings', 'last 10 meetings', 'current streak'] | [['baylor', 'ut , 57 - 23', 'ut , 27 - 5', 'ut , 22 - 13', 'ut , 7 - 2', 'bu , 3 - 2', 'tied , 5 - 5', 'l 2'], ['colorado', 'ut , 14 - 4', 'ut , 6 - 1', 'ut , 6 - 2', 'ut , 2 - 1', 'ut , 4 - 1', 'ut , 8 - 2', 'w 1'], ['iowa state', 'isu , 10 - 9', 'ut , 6 - 2', 'isu , 5 - 2', 'isu , 3 - 1', 'isu , 3 - 2', 'tied , 5 - 5', 'l 2'], ['kansas', 'ut , 11 - 7', 'ut , 4 - 3', 'tied , 4 - 4', 'ut , 3 - 0', 'ut , 3 - 2', 'ut , 7 - 3', 'w 1'], ['kansas state', 'ut , 10 - 8', 'ut , 6 - 2', 'ksu , 4 - 3', 'ksu , 2 - 1', 'ut , 3 - 2', 'ksu , 6 - 4', 'l 1'], ['missouri', 'ut , 15 - 1', 'ut , 9 - 0', 'ut , 5 - 1', 'ut , 1 - 0', 'ut , 5 - 0', 'ut , 9 - 1', 'w 8'], ['nebraska', 'ut , 12 - 5', 'ut , 6 - 1', 'ut , 4 - 3', 'ut , 2 - 1', 'ut , 3 - 2', 'ut , 8 - 2', 'l 2'], ['oklahoma', 'ut , 21 - 13', 'ut , 11 - 4', 'tied , 7 - 7', 'ut , 3 - 2', 'ou , 3 - 2', 'tied , 5 - 5', 'w 1'], ['oklahoma state', 'ut , 21 - 7', 'ut , 11 - 2', 'ut , 7 - 4', 'ut , 2 - 1', 'osu , 3 - 2', 'ut , 7 - 3', 'l 2'], ['texas a & m', 'ut , 58 - 15', 'ut , 28 - 4', 'ut , 23 - 8', 'ut , 7 - 3', 'a & m , 4 - 1', 'tied , 5 - 5', 'l 3'], ['texas tech', 'ut , 55 - 24', 'ut , 25 - 6', 'ut , 17 - 13', 'ut , 13 - 4', 'ttu , 3 - 2', 'ttu , 6 - 4', 'w 2']] |
lee gibson | https://en.wikipedia.org/wiki/Lee_Gibson | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17624963-2.html.csv | count | a total of 5 of lee gibson 's fights went to a decision . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'decision', 'result': '5', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'method', 'decision'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose method record fuzzily matches to decision .', 'tostr': 'filter_eq { all_rows ; method ; decision }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; method ; decision } }', 'tointer': 'select the rows whose method record fuzzily matches to decision . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; method ; decision } } ; 5 } = true', 'tointer': 'select the rows whose method record fuzzily matches to decision . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; method ; decision } } ; 5 } = true | select the rows whose method record fuzzily matches to decision . 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, 'method_5': 5, 'decision_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', 'method_5': 'method', 'decision_6': 'decision', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'method_5': [0], 'decision_6': [0], '5_7': [2]} | ['res', 'record', 'opponent', 'method', 'event', 'round', 'location'] | [['win', '12 - 3', 'joe wilk', 'tko ( strikes )', 'strikeforce challengers : woodley vs bears', '1', 'kansas , united states'], ['loss', '11 - 3', 'muhsin corbbrey', 'decision ( unanimous )', 'shoxcjuly_27 .2 c_2007_card', '3', 'california , united states'], ['win', '11 - 2', 'talon hoffman', 'tko', 'ifo - eastman vs kimmons', '2', 'nevada , united states'], ['win', '10 - 2', 'kyle olsen', 'decision ( unanimous )', 'tuff - n - uff 2', '3', 'nevada , united states'], ['win', '9 - 2', 'tj brown', 'decision ( unanimous )', 'tuff - n - uff 2', '3', 'nevada , united states'], ['win', '8 - 2', 'frank young', 'submission', 'tfc 7 - red rumble', '1', 'kansas , united states'], ['loss', '7 - 2', 'luke gwaltney', 'decision ( unanimous )', 'ggp - good guys promotions', '3', 'kansas , united states'], ['win', '7 - 1', 'billy walters', 'tko', 'tfc 6 - titan fighting championships 6', '1', 'kansas , united states'], ['win', '6 - 1', 'mike funk', 'submission ( strikes )', 'fcf - freestyle cage fighting', '1', 'oklahoma , united states'], ['loss', '5 - 1', 'justin james', 'submission ( armbar )', 'ec 70 - extreme challenge 70', '1', 'wisconsin , united states'], ['win', '5 - 0', 'robert hembree', 'submission ( strikes )', 'tfc 5 - titan fighting championship 5', '1', 'kansas , united states'], ['win', '4 - 0', 'joe davis', 'tko', 'tfc 4 - memorial mayhem', '1', 'kansas , united states'], ['win', '3 - 0', 'nathan murdock', 'decision ( unanimous )', 'tfc 3 - red river rumble', '3', 'oklahoma , united states'], ['win', '2 - 0', 'adrian olivas', 'ko', 'ndn - promotions', '2', 'oklahoma , united states'], ['win', '1 - 0', 'bobby gregg', 'tko', 'iscf - clash of the titans', '1', 'missouri , united states']] |
list of bradford city a.f.c. records and statistics | https://en.wikipedia.org/wiki/List_of_Bradford_City_A.F.C._records_and_statistics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15278857-2.html.csv | ordinal | dean windass is the player that recorded the third highest amount of goals for bradford city a.f.c. | {'row': '3', 'col': '2', 'order': '3', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'goals', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; goals ; 3 }'}, 'name'], 'result': 'dean windass', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; goals ; 3 } ; name }'}, 'dean windass'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; goals ; 3 } ; name } ; dean windass } = true', 'tointer': 'select the row whose goals record of all rows is 3rd maximum . the name record of this row is dean windass .'} | eq { hop { nth_argmax { all_rows ; goals ; 3 } ; name } ; dean windass } = true | select the row whose goals record of all rows is 3rd maximum . the name record of this row is dean windass . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'goals_5': 5, '3_6': 6, 'name_7': 7, 'dean windass_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', 'goals_5': 'goals', '3_6': '3', 'name_7': 'name', 'dean windass_8': 'dean windass'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'goals_5': [0], '3_6': [0], 'name_7': [1], 'dean windass_8': [2]} | ['name', 'goals', 'apps', 'avge', 'career'] | [['bobby campbell', '121', '274', '0.44', '1979 - 1983 , 1983 - 1986'], ["frank o'rourke", '88', '192', '0.46', '1907 - 1914'], ['dean windass', '76', '216', '0.35', '1999 - 2001 , 2003 - 2007'], ['john hallows', '74', '164', '0.45', '1930 - 1936'], ['joe cooke', '68', '271', '0.25', '1971 - 1979 , 1981 - 1984'], ['gerry ingram', '64', '174', '0.37', '1971 - 1977'], ['bobby ham', '64', '188', '0.34', '1967 - 1971 , 1973 - 1975'], ['david mcniven', '64', '212', '0.30', '1978 - 1983'], ['sean mccarthy', '63', '131', '0.48', '1990 - 1994'], ['john hall', '63', '430', '0.15', '1962 - 1974'], ['david jackson', '61', '250', '0.24', '1955 - 1961'], ['bruce bannister', '60', '208', '0.29', '1965 - 1971'], ['dicky bond', '60', '301', '0.20', '1909 - 1922']] |
athletics at the 1978 commonwealth games | https://en.wikipedia.org/wiki/Athletics_at_the_1978_Commonwealth_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10215649-3.html.csv | ordinal | in athletics at the 1978 commonwealth games the country with 2 bronze medals that had the most gold medals was in rank 4 . | {'scope': 'subset', 'row': '4', 'col': '3', 'order': '1', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'subset': {'col': '5', 'criterion': 'equal', 'value': '2'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'bronze', '2'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; bronze ; 2 }', 'tointer': 'select the rows whose bronze record is equal to 2 .'}, 'gold', '1'], 'result': None, 'ind': 1, 'tostr': 'nth_argmax { filter_eq { all_rows ; bronze ; 2 } ; gold ; 1 }'}, 'rank'], 'result': '4', 'ind': 2, 'tostr': 'hop { nth_argmax { filter_eq { all_rows ; bronze ; 2 } ; gold ; 1 } ; rank }'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmax { filter_eq { all_rows ; bronze ; 2 } ; gold ; 1 } ; rank } ; 4 } = true', 'tointer': 'select the rows whose bronze record is equal to 2 . select the row whose gold record of these rows is 1st maximum . the rank record of this row is 4 .'} | eq { hop { nth_argmax { filter_eq { all_rows ; bronze ; 2 } ; gold ; 1 } ; rank } ; 4 } = true | select the rows whose bronze record is equal to 2 . select the row whose gold record of these rows is 1st maximum . the rank record of this row is 4 . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmax_1': 1, 'filter_eq_0': 0, 'all_rows_5': 5, 'bronze_6': 6, '2_7': 7, 'gold_8': 8, '1_9': 9, 'rank_10': 10, '4_11': 11} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmax_1': 'nth_argmax', 'filter_eq_0': 'filter_eq', 'all_rows_5': 'all_rows', 'bronze_6': 'bronze', '2_7': '2', 'gold_8': 'gold', '1_9': '1', 'rank_10': 'rank', '4_11': '4'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmax_1': [2], 'filter_eq_0': [1], 'all_rows_5': [0], 'bronze_6': [0], '2_7': [0], 'gold_8': [1], '1_9': [1], 'rank_10': [2], '4_11': [3]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'england', '16', '5', '12', '33'], ['2', 'australia', '7', '10', '7', '25'], ['3', 'canada', '6', '8', '10', '24'], ['4', 'kenya', '5', '4', '2', '11'], ['5', 'scotland', '2', '1', '3', '6'], ['6', 'jamaica', '1', '2', '2', '5'], ['7', 'tanzania', '1', '1', '0', '2'], ['8', 'wales', '1', '0', '0', '1'], ['9', 'trinidad and tobago', '0', '2', '1', '3'], ['10', 'new zealand', '0', '2', '0', '2'], ['11', 'guyana', '0', '1', '1', '2'], ['12', 'bahamas', '0', '1', '0', '1'], ['13', 'india', '0', '0', '1', '1'], ['total', 'total', '38', '38', '39', '115']] |
list of white collar episodes | https://en.wikipedia.org/wiki/List_of_White_Collar_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24319661-5.html.csv | count | three of the episodes of the season of white collar originally aired in august . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'august', 'result': '3', 'col': '7', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'original air date', 'august'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose original air date record fuzzily matches to august .', 'tostr': 'filter_eq { all_rows ; original air date ; august }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; original air date ; august } }', 'tointer': 'select the rows whose original air date record fuzzily matches to august . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; original air date ; august } } ; 3 } = true', 'tointer': 'select the rows whose original air date record fuzzily matches to august . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; original air date ; august } } ; 3 } = true | select the rows whose original air date record fuzzily matches to august . 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, 'original air date_5': 5, 'august_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', 'original air date_5': 'original air date', 'august_6': 'august', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'original air date_5': [0], 'august_6': [0], '3_7': [2]} | ['no in series', 'no in season', 'title', 'directed by', 'written by', 'us viewers ( million )', 'original air date', 'production code'] | [['47', '1', 'wanted', 'paul holahan', 'jeff eastin', '3.21', 'july 10 , 2012', 'bcw401'], ['48', '2', 'most wanted', 'paul holahan', 'mark goffman', '2.98', 'july 17 , 2012', 'bcw402'], ['49', '3', 'diminishing returns', 'stefan schwartz', 'jim campolongo', '3.01', 'july 24 , 2012', 'bcw403'], ['50', '4', 'parting shots', 'robert duncan mcneill', 'alexandra mcnally', '2.82', 'july 31 , 2012', 'bcw404'], ['51', '5', 'honor among thieves', 'arlene sanford', 'joe henderson', '2.93', 'august 14 , 2012', 'bcw405'], ['52', '6', 'identity crisis', 'david straiton', 'channing powell', '3.89', 'august 21 , 2012', 'bcw406'], ['53', '7', 'compromising positions', 'paul holahan', 'matthew negrete', '3.36', 'august 28 , 2012', 'bcw407'], ['54', '8', 'ancient history', 'russell lee fine', 'daniel shattuck', '3.38', 'september 4 , 2012', 'bcw408'], ['55', '9', 'gloves off', 'renny harlin', 'mark goffman', '3.80', 'september 11 , 2012', 'bcw409'], ['56', '10', 'vested interest', 'russell lee fine', 'jeff eastin', '3.41', 'september 18 , 2012', 'bcw410'], ['57', '11', 'family business', 'paul holahan', 'joe henderson', '2.77', 'january 22 , 2013', 'bcw411'], ['58', '12', 'brass tacks', 'anton cropper', 'jim campolongo & alexandra mcnally', '2.61', 'january 29 , 2013', 'bcw412'], ['59', '13', 'empire city', 'tim dekay', 'channing powell & daniel shattuck', '2.28', 'february 5 , 2013', 'bcw413'], ['60', '14', 'shoot the moon', 'russell lee fine', 'matthew negrete & bob derosa', '2.42', 'february 19 , 2013', 'bcw414'], ['61', '15', 'the original', 'john kretchmer', 'mark goffman', '2.12', 'february 26 , 2013', 'bcw415']] |
list of presidents of india by longevity | https://en.wikipedia.org/wiki/List_of_Presidents_of_India_by_longevity | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18596194-1.html.csv | unique | neelam sanjuva reddy , inaugurated on july 25,1977 , was the youngest president inaugurated , at the age of 64 years and 67 days . | {'scope': 'all', 'row': '6', 'col': '4', 'col_other': '3', 'criterion': 'fuzzily_match', 'value': '64years , 67days', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'age at inauguration', '64years , 67days'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose age at inauguration record fuzzily matches to 64years , 67days .', 'tostr': 'filter_eq { all_rows ; age at inauguration ; 64years , 67days }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; age at inauguration ; 64years , 67days } }', 'tointer': 'select the rows whose age at inauguration record fuzzily matches to 64years , 67days . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'age at inauguration', '64years , 67days'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose age at inauguration record fuzzily matches to 64years , 67days .', 'tostr': 'filter_eq { all_rows ; age at inauguration ; 64years , 67days }'}, 'date of inauguration'], 'result': '25 july 1977', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; age at inauguration ; 64years , 67days } ; date of inauguration }'}, '25 july 1977'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; age at inauguration ; 64years , 67days } ; date of inauguration } ; 25 july 1977 }', 'tointer': 'the date of inauguration record of this unqiue row is 25 july 1977 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; age at inauguration ; 64years , 67days } } ; eq { hop { filter_eq { all_rows ; age at inauguration ; 64years , 67days } ; date of inauguration } ; 25 july 1977 } } = true', 'tointer': 'select the rows whose age at inauguration record fuzzily matches to 64years , 67days . there is only one such row in the table . the date of inauguration record of this unqiue row is 25 july 1977 .'} | and { only { filter_eq { all_rows ; age at inauguration ; 64years , 67days } } ; eq { hop { filter_eq { all_rows ; age at inauguration ; 64years , 67days } ; date of inauguration } ; 25 july 1977 } } = true | select the rows whose age at inauguration record fuzzily matches to 64years , 67days . there is only one such row in the table . the date of inauguration record of this unqiue row is 25 july 1977 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'age at inauguration_7': 7, '64years , 67days_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date of inauguration_9': 9, '25 july 1977_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'age at inauguration_7': 'age at inauguration', '64years , 67days_8': '64years , 67days', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date of inauguration_9': 'date of inauguration', '25 july 1977_10': '25 july 1977'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'age at inauguration_7': [0], '64years , 67days_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date of inauguration_9': [2], '25 july 1977_10': [3]} | ['president', 'date of birth', 'date of inauguration', 'age at inauguration', 'end of term', 'length of retirement', 'date of death', 'lifespan'] | [['prasad , rajendra rajendra prasad', '1884 - 12 - 03 3 december 1884', '26 january 1950', '65 - 054 65years , 54days', '13 may 1962', '0291 days', '1963 - 02 - 28 28 february 1963', 'days ( 78years , 87days )'], ['radhakrishnan , sarvepalli sarvepalli radhakrishnan', '1888 - 09 - 05 5 september 1888', '13 may 1962', '73 - 250 73years , 250days', '13 may 1967', '2896 days', '1975 - 04 - 17 17 april 1975', 'days ( 86years , 224days )'], ['hussain , zakir zakir hussain', '1897 - 02 - 08 8 february 1897', '13 may 1967', '70 - 094 70years , 94days', '3 may 1969', '0000 n / a', '1969 - 05 - 03 3 may 1969', 'days ( 72years , 84days )'], ['giri , v v vv giri', '1894 - 08 - 10 10 august 1894', '24 august 1969', '75 - 014 75years , 14days', '24 august 1974', '2130 days', '1980 - 06 - 23 23 june 1980', 'days ( 85years , 318days )'], ['ahmed , fakhruddin fakhruddin ahmed', '1905 - 05 - 13 13 may 1905', '24 august 1974', '69 - 103 69years , 103days', '11 february 1977', '0000 n / a', '1977 - 02 - 11 11 february 1977', 'days ( 71years , 274days )'], ['reddy , neelam neelam reddy', '1913 - 05 - 19 19 may 1913', '25 july 1977', '64 - 067 64years , 67days', '25 july 1982', '5060 days', '1996 - 06 - 01 1 june 1996', 'days ( 83years , 13days )'], ['singh , zail zail singh', '1916 - 05 - 05 5 may 1916', '25 july 1982', '66 - 081 66years , 81days', '25 july 1987', '2710 days', '1994 - 12 - 25 25 december 1994', 'days ( 78years , 234days )'], ['venkataraman , ramaswamy ramaswamy venkataraman', '1910 - 12 - 04 4 december 1910', '25 july 1987', '76 - 233 76years , 233days', '25 july 1992', '6030 days', '2009 - 01 - 27 27 january 2009', 'days ( 98years , 54days )'], ['sharma , shankar shankar dayal sharma', '1918 - 08 - 19 19 august 1918', '25 july 1992', '73 - 341 73years , 341days', '25 july 1997', '0884 days', '1999 - 12 - 26 26 december 1999', 'days ( 81years , 129days )'], ['narayanan , k r kr narayanan', '1920 - 10 - 27 27 october 1920', '25 july 1997', '76 - 271 76years , 271days', '25 july 2002', '1203 days', '2005 - 11 - 09 9 november 2005', 'days ( 85years , 13days )'], ['kalam , a p j apjabdul kalam', '1931 - 10 - 15 15 october 1931', '25 july 2002', '70 - 283 70years , 283days', '25 july 2007', '0 , 2383 days', '2014 - 02 - 1', 'days ( 82years , 109days )'], ['patil , pratibha pratibha patil', '1934 - 12 - 19 19 december 1934', '25 july 2007', '72 - 218 72years , 218days', '25 july 2012', '0 , 556 days', '2014 - 02 - 1', 'days ( 79years , 44days )'], ['mukherjee , pranab pranab mukherjee', '1934 - 12 - 19 11 december 1935', '25 july 2012', '76years , 227days', 'incumbent', '0000 incumbent', '2014 - 02 - 1', 'days ( 78years , 52days )'], ['president', 'date of birth', 'date of inauguration', 'age at inauguration', 'end of term', 'length of retirement', 'date of death 25 - 7 - 2012', 'lifespan']] |
bmw 3 series compact | https://en.wikipedia.org/wiki/BMW_3_Series_Compact | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1180976-2.html.csv | majority | the majority of bmw 3 series compact models were released for the years 2001 - 2004 . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': '2001 - 2004', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'years', '2001 - 2004'], 'result': True, 'ind': 0, 'tointer': 'for the years records of all rows , most of them fuzzily match to 2001 - 2004 .', 'tostr': 'most_eq { all_rows ; years ; 2001 - 2004 } = true'} | most_eq { all_rows ; years ; 2001 - 2004 } = true | for the years records of all rows , most of them fuzzily match to 2001 - 2004 . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'years_3': 3, '2001 - 2004_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'years_3': 'years', '2001 - 2004_4': '2001 - 2004'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'years_3': [0], '2001 - 2004_4': [0]} | ['model', 'years', 'engine code', 'power', 'torque'] | [['316ti', '2001 - 2004', 'n42b18 / n46b18', '5500', '3750'], ['318ti', '2001 - 2004', 'n42b20 / n46b20', '6000', '3750'], ['325ti', '2001 - 2004', 'm54b25', '6000', '3500'], ['318td ( diesel )', '2003 - 2004', 'm47d20', '4000', '1750'], ['320td ( diesel )', '2001 - 2004', 'm47d20', '4000', '2000']] |
1994 toronto argonauts season | https://en.wikipedia.org/wiki/1994_Toronto_Argonauts_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23612439-2.html.csv | aggregation | a total 95326 fans were in attendance during the last five games of the 1994 toronto argonauts season . | {'scope': 'subset', 'col': '6', 'type': 'sum', 'result': '95326', 'subset': {'col': '1', 'criterion': 'greater_than_eq', 'value': '13'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'week', '13'], 'result': None, 'ind': 0, 'tostr': 'filter_greater_eq { all_rows ; week ; 13 }', 'tointer': 'select the rows whose week record is greater than or equal to 13 .'}, 'attendance'], 'result': '95326', 'ind': 1, 'tostr': 'sum { filter_greater_eq { all_rows ; week ; 13 } ; attendance }'}, '95326'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_greater_eq { all_rows ; week ; 13 } ; attendance } ; 95326 } = true', 'tointer': 'select the rows whose week record is greater than or equal to 13 . the sum of the attendance record of these rows is 95326 .'} | round_eq { sum { filter_greater_eq { all_rows ; week ; 13 } ; attendance } ; 95326 } = true | select the rows whose week record is greater than or equal to 13 . the sum of the attendance record of these rows is 95326 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_greater_eq_0': 0, 'all_rows_4': 4, 'week_5': 5, '13_6': 6, 'attendance_7': 7, '95326_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_4': 'all_rows', 'week_5': 'week', '13_6': '13', 'attendance_7': 'attendance', '95326_8': '95326'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_greater_eq_0': [1], 'all_rows_4': [0], 'week_5': [0], '13_6': [0], 'attendance_7': [1], '95326_8': [2]} | ['week', 'date', 'opponent', 'location', 'final score', 'attendance', 'record'] | [['1', 'july 7', 'cflers', 'skydome', 'l 28 - 20', '13101', '0 - 1'], ['2', 'july 16', 'pirates', 'independence stadium', 'w 35 - 34', '20624', '1 - 1'], ['3', 'july 22', 'roughriders', 'taylor field', 'l 35 - 24', '23433', '1 - 2'], ['4', 'july 29', 'posse', 'skydome', 'w 39 - 20', '14296', '2 - 2'], ['5', 'august 4', 'blue bombers', 'skydome', 'l 54 - 34', '13407', '2 - 3'], ['6', 'august 11', 'lions', 'bc place stadium', 'l 54 - 39', '19424', '2 - 4'], ['7', 'august 20', 'cflers', 'memorial stadium', 'w 31 - 24', '41155', '3 - 4'], ['8', 'august 25', 'stampeders', 'skydome', 'l 52 - 3', '19158', '3 - 5'], ['9', 'september 5', 'tiger - cats', 'ivor wynne stadium', 'w 31 - 19', '20687', '4 - 5'], ['10', 'september 11', 'lions', 'skydome', 'l 28 - 18', '15259', '4 - 6'], ['11', 'september 18', 'rough riders', 'skydome', 'l 40 - 32', '15102', '4 - 7'], ['12', 'september 25', 'eskimos', 'commonwealth stadium', 'l 28 - 25', '24132', '4 - 8'], ['13', 'october 2', 'tiger - cats', 'skydome', 'w 39 - 36', '18709', '5 - 8'], ['14', 'october 8', 'gold miners', 'hornet stadium', 'l 34 - 32', '13050', '5 - 9'], ['15', 'october 16', 'rough riders', 'frank clair stadium', 'w 24 - 22', '21029', '6 - 9'], ['16', 'october 23', 'eskimos', 'skydome', 'w 23 - 6', '22210', '7 - 9'], ['17', 'october 28', 'pirates', 'skydome', 'l 29 - 27', '20328', '7 - 10']] |
melville , saskatchewan | https://en.wikipedia.org/wiki/Melville%2C_Saskatchewan | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1034685-1.html.csv | unique | the cbc radio 2 channel is the only channel in melville , saskatchewan to have the public broadcasting format . | {'scope': 'all', 'row': '2', 'col': '4', 'col_other': '3', 'criterion': 'equal', 'value': 'public broadcasting', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'format', 'public broadcasting'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose format record fuzzily matches to public broadcasting .', 'tostr': 'filter_eq { all_rows ; format ; public broadcasting }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; format ; public broadcasting } }', 'tointer': 'select the rows whose format record fuzzily matches to public broadcasting . 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', 'public broadcasting'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose format record fuzzily matches to public broadcasting .', 'tostr': 'filter_eq { all_rows ; format ; public broadcasting }'}, 'branding'], 'result': 'cbc radio 2', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; format ; public broadcasting } ; branding }'}, 'cbc radio 2'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; format ; public broadcasting } ; branding } ; cbc radio 2 }', 'tointer': 'the branding record of this unqiue row is cbc radio 2 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; format ; public broadcasting } } ; eq { hop { filter_eq { all_rows ; format ; public broadcasting } ; branding } ; cbc radio 2 } } = true', 'tointer': 'select the rows whose format record fuzzily matches to public broadcasting . there is only one such row in the table . the branding record of this unqiue row is cbc radio 2 .'} | and { only { filter_eq { all_rows ; format ; public broadcasting } } ; eq { hop { filter_eq { all_rows ; format ; public broadcasting } ; branding } ; cbc radio 2 } } = true | select the rows whose format record fuzzily matches to public broadcasting . there is only one such row in the table . the branding record of this unqiue row is cbc radio 2 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'format_7': 7, 'public broadcasting_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'branding_9': 9, 'cbc radio 2_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', 'public broadcasting_8': 'public broadcasting', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'branding_9': 'branding', 'cbc radio 2_10': 'cbc radio 2'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'format_7': [0], 'public broadcasting_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'branding_9': [2], 'cbc radio 2_10': [3]} | ['frequency', 'call sign', 'branding', 'format', 'owner'] | [['am 940', 'cjgx', 'gx94', 'country music', 'harvard broadcasting'], ['fm 91.7', 'cbk - fm3', 'cbc radio 2', 'public broadcasting', 'canadian broadcasting corporation'], ['fm 92.9', 'cjlr - fm - 5', 'mbc radio', 'first nationscommunity radio', 'missinipi broadcasting corporation'], ['fm 94.1', 'cfgw - fm', 'fox fm', 'hot adult contemporary', 'harvard broadcasting'], ['fm 98.5', 'cjjc - fm', '98.5 the rock', 'christian music', 'dennis m dyck']] |
allen county conference | https://en.wikipedia.org/wiki/Allen_County_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18765101-1.html.csv | unique | in the allen county conference , the only school to join in 1971 was southern wells . | {'scope': 'all', 'row': '7', 'col': '7', 'col_other': '1', 'criterion': 'equal', 'value': '1971', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year joined', '1971'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year joined record is equal to 1971 .', 'tostr': 'filter_eq { all_rows ; year joined ; 1971 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; year joined ; 1971 } }', 'tointer': 'select the rows whose year joined record is equal to 1971 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year joined', '1971'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year joined record is equal to 1971 .', 'tostr': 'filter_eq { all_rows ; year joined ; 1971 }'}, 'school'], 'result': 'southern wells', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year joined ; 1971 } ; school }'}, 'southern wells'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; year joined ; 1971 } ; school } ; southern wells }', 'tointer': 'the school record of this unqiue row is southern wells .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; year joined ; 1971 } } ; eq { hop { filter_eq { all_rows ; year joined ; 1971 } ; school } ; southern wells } } = true', 'tointer': 'select the rows whose year joined record is equal to 1971 . there is only one such row in the table . the school record of this unqiue row is southern wells .'} | and { only { filter_eq { all_rows ; year joined ; 1971 } } ; eq { hop { filter_eq { all_rows ; year joined ; 1971 } ; school } ; southern wells } } = true | select the rows whose year joined record is equal to 1971 . there is only one such row in the table . the school record of this unqiue row is southern wells . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'year joined_7': 7, '1971_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'school_9': 9, 'southern wells_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'year joined_7': 'year joined', '1971_8': '1971', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'school_9': 'school', 'southern wells_10': 'southern wells'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'year joined_7': [0], '1971_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'school_9': [2], 'southern wells_10': [3]} | ['school', 'location', 'mascot', 'enrollment 08 - 09', 'ihsaa class / football class', 'county', 'year joined', 'previous conference'] | [['adams central', 'monroe', 'flying jets', '404', '2a / 1a', '01 adams', '1969', 'independent'], ['bluffton', 'bluffton', 'tigers', '467', '2a / 2a', '90 wells', '1989', 'northeastern indiana'], ['garrett', 'garrett', 'railroaders', '598', '3a / 3a', '17 dekalb', '2005', 'northeast corner'], ['heritage', 'monroeville', 'patriots', '734', '3a / 3a', '02 allen', '1969', 'independent'], ['leo', 'leo', 'lions', '980', '3a / 4a', '02 allen', '1969', 'independent'], ['south adams', 'berne', 'starfires', '398', '2a / 1a', '01 adams', '1989', 'northeastern indiana'], ['southern wells', 'poneto', 'raiders', '227', '1a / 1a', '90 wells', '1971', 'none ( new school )'], ['woodlan', 'woodburn', 'warriors', '591', '3a / 2a', '02 allen', '1969', 'independents']] |
2007 - 08 los angeles clippers season | https://en.wikipedia.org/wiki/2007%E2%80%9308_Los_Angeles_Clippers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11965402-4.html.csv | majority | all games of the los angeles clippers ' in the 2007 - 08 season were played in the month of december . | {'scope': 'all', 'col': '1', 'most_or_all': 'all', 'criterion': 'fuzzily_match', 'value': 'december', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'date', 'december'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to december .', 'tostr': 'all_eq { all_rows ; date ; december } = true'} | all_eq { all_rows ; date ; december } = true | for the date records of all rows , all of them fuzzily match to december . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, 'december_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', 'december_4': 'december'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], 'december_4': [0]} | ['date', 'visitor', 'score', 'home', 'leading scorer', 'attendance', 'record'] | [['december 2 , 2007', 'pacers', '101 - 95', 'clippers', 'chris kaman ( 22 )', '13741', '6 - 9'], ['december 4 , 2007', 'bucks', '87 - 78', 'clippers', 'corey maggette ( 20 )', '16004', '6 - 10'], ['december 5 , 2007', 'clippers', '88 - 95', 'sonics', 'corey maggette ( 23 )', '10961', '6 - 11'], ['december 7 , 2007', 'clippers', '97 - 87', 'kings', 'chris kaman ( 26 )', '13094', '7 - 11'], ['december 9 , 2007', 'miami heat', '100 - 94', 'clippers', 'corey maggette ( 24 )', '16335', '7 - 12'], ['december 11 , 2007', 'clippers', '91 - 82', 'nets', 'two way tie ( 18 )', '13433', '8 - 12'], ['december 12 , 2007', 'clippers', '103 - 108', 'bobcats', 'corey maggette ( 23 )', '10751', '8 - 13'], ['december 14 , 2007', 'clippers', '98 - 91', 'grizzlies', 'two way tie ( 23 )', '10819', '9 - 13'], ['december 16 , 2007', 'clippers', '92 - 113', 'lakers', 'corey maggette ( 27 )', '18997', '9 - 14'], ['december 18 , 2007', 'raptors', '80 - 77', 'clippers', 'corey maggette ( 22 )', '14455', '9 - 15'], ['december 21 , 2007', 'clippers', '89 - 102', 'mavericks', 'chris kaman ( 24 )', '20246', '9 - 16'], ['december 22 , 2007', 'clippers', '90 - 99', 'spurs', 'al thornton ( 25 )', '18797', '9 - 17'], ['december 27 , 2007', 'suns', '108 - 88', 'clippers', 'corey maggette ( 21 )', '18422', '9 - 18'], ['december 28 , 2007', 'clippers', '88 - 94', 'suns', 'chris kaman ( 22 )', '17871', '9 - 19'], ['december 31 , 2007', 'timberwolves', '82 - 91', 'clippers', 'cuttino mobley ( 18 )', '14404', '10 - 19']] |
kristy mcpherson | https://en.wikipedia.org/wiki/Kristy_McPherson | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14853156-2.html.csv | superlative | kristy mcpherson had the most cuts made in the year 2009 . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'cuts made'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; cuts made }'}, 'year'], 'result': '2009', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; cuts made } ; year }'}, '2009'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; cuts made } ; year } ; 2009 } = true', 'tointer': 'select the row whose cuts made record of all rows is maximum . the year record of this row is 2009 .'} | eq { hop { argmax { all_rows ; cuts made } ; year } ; 2009 } = true | select the row whose cuts made record of all rows is maximum . the year record of this row is 2009 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'cuts made_5': 5, 'year_6': 6, '2009_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'cuts made_5': 'cuts made', 'year_6': 'year', '2009_7': '2009'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'cuts made_5': [0], 'year_6': [1], '2009_7': [2]} | ['year', 'tournaments played', 'cuts made', 'wins', '2nd', '3rd', 'top 10s', 'best finish', 'earnings', 'money list rank', 'scoring average', 'scoring rank'] | [['2005', '1', '0', '0', '0', '0', '0', 'mc', '0', 'n / a', '77.00', 'n / a'], ['2007', '18', '11', '0', '0', '0', '0', 't18', '79724', '97', '73.73', 't99'], ['2008', '26', '19', '0', '0', '0', '6', 't4', '407237', '47', '71.86', '34'], ['2009', '24', '21', '0', '2', '1', '6', 't2', '816182', '16', '71.25', '17'], ['2010', '22', '17', '0', '1', '0', '4', 't2', '418217', '27', '72.26', '40'], ['2011', '21', '17', '0', '0', '0', '0', 't18', '157025', '56', '72.65', '50']] |
angela stanford | https://en.wikipedia.org/wiki/Angela_Stanford | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14836185-3.html.csv | count | angela stanford had four wins in tournaments played from 2001 to 2012 . | {'scope': 'all', 'criterion': 'not_equal', 'value': '0', 'result': '4', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_not_eq', 'args': ['all_rows', 'wins', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose wins record is not equal to 0 .', 'tostr': 'filter_not_eq { all_rows ; wins ; 0 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_not_eq { all_rows ; wins ; 0 } }', 'tointer': 'select the rows whose wins record is not equal to 0 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_not_eq { all_rows ; wins ; 0 } } ; 4 } = true', 'tointer': 'select the rows whose wins record is not equal to 0 . the number of such rows is 4 .'} | eq { count { filter_not_eq { all_rows ; wins ; 0 } } ; 4 } = true | select the rows whose wins record is not equal to 0 . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_not_eq_0': 0, 'all_rows_4': 4, 'wins_5': 5, '0_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_not_eq_0': 'filter_not_eq', 'all_rows_4': 'all_rows', 'wins_5': 'wins', '0_6': '0', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_not_eq_0': [1], 'all_rows_4': [0], 'wins_5': [0], '0_6': [0], '4_7': [2]} | ['year', 'tournaments played', 'cuts made', 'wins', '2nd', '3rd', 'top 10s', 'best finish', 'earnings', 'money list rank', 'scoring average', 'scoring rank'] | [['2001', '26', '12', '0', '0', '0', '0', 't15', '66956', '98', '73.24', '103'], ['2002', '19', '12', '0', '1', '0', '2', '2', '221857', '45', '72.37', '46'], ['2003', '21', '17', '1', '1', '0', '3', '1', '643192', '17', '71.94', '38'], ['2004', '24', '19', '0', '0', '0', '2', 't4', '297790', '39', '71.86', 't43'], ['2005', '25', '15', '0', '0', '1', '3', 't3', '272288', '44', '73.11', '69'], ['2006', '25', '20', '0', '2', '0', '3', '2', '473218', '23', '71.80', 't29'], ['2007', '24', '21', '0', '0', '2', '12', 't3', '713880', '19', '71.62', '11'], ['2008', '27', '23', '2', '1', '2', '10', '1', '1134753', '9', '71.22', '9'], ['2009', '21', '20', '1', '2', '2', '11', '1', '1081916', '10', '70.64', '11'], ['2010', '22', '19', '0', '1', '0', '7', '2', '596830', '18', '71.35', '19'], ['2011', '21', '20', '0', '0', '3', '9', '3', '1017196', '7', '71.42', '15'], ['2012', '26', '23', '1', '2', '1', '6', '1', '794294', '16', '71.51', '21']] |
2001 new orleans saints season | https://en.wikipedia.org/wiki/2001_New_Orleans_Saints_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16882035-1.html.csv | unique | the only game is october of the 2001 new orleans saints season with an attendance of less than 70000 was against st louis rams . | {'scope': 'subset', 'row': '6', 'col': '5', 'col_other': '3', 'criterion': 'less_than', 'value': '70000', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'october'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', '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 .'}, 'attendance', '70000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to october . among these rows , select the rows whose attendance record is less than 70000 .', 'tostr': 'filter_less { filter_eq { all_rows ; date ; october } ; attendance ; 70000 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_less { filter_eq { all_rows ; date ; october } ; attendance ; 70000 } }', 'tointer': 'select the rows whose date record fuzzily matches to october . among these rows , select the rows whose attendance record is less than 70000 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_less', '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 .'}, 'attendance', '70000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to october . among these rows , select the rows whose attendance record is less than 70000 .', 'tostr': 'filter_less { filter_eq { all_rows ; date ; october } ; attendance ; 70000 }'}, 'opponent'], 'result': 'st louis rams', 'ind': 3, 'tostr': 'hop { filter_less { filter_eq { all_rows ; date ; october } ; attendance ; 70000 } ; opponent }'}, 'st louis rams'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_less { filter_eq { all_rows ; date ; october } ; attendance ; 70000 } ; opponent } ; st louis rams }', 'tointer': 'the opponent record of this unqiue row is st louis rams .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_less { filter_eq { all_rows ; date ; october } ; attendance ; 70000 } } ; eq { hop { filter_less { filter_eq { all_rows ; date ; october } ; attendance ; 70000 } ; opponent } ; st louis rams } } = true', 'tointer': 'select the rows whose date record fuzzily matches to october . among these rows , select the rows whose attendance record is less than 70000 . there is only one such row in the table . the opponent record of this unqiue row is st louis rams .'} | and { only { filter_less { filter_eq { all_rows ; date ; october } ; attendance ; 70000 } } ; eq { hop { filter_less { filter_eq { all_rows ; date ; october } ; attendance ; 70000 } ; opponent } ; st louis rams } } = true | select the rows whose date record fuzzily matches to october . among these rows , select the rows whose attendance record is less than 70000 . there is only one such row in the table . the opponent record of this unqiue row is st louis rams . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_less_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'date_8': 8, 'october_9': 9, 'attendance_10': 10, '70000_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'opponent_12': 12, 'st louis rams_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_less_1': 'filter_less', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'date_8': 'date', 'october_9': 'october', 'attendance_10': 'attendance', '70000_11': '70000', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'opponent_12': 'opponent', 'st louis rams_13': 'st louis rams'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_less_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'date_8': [0], 'october_9': [0], 'attendance_10': [1], '70000_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'opponent_12': [3], 'st louis rams_13': [4]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 9 , 2001', 'buffalo bills', 'w 24 - 6', '71447'], ['3', 'september 30 , 2001', 'new york giants', 'l 21 - 13', '78451'], ['4', 'october 7 , 2001', 'minnesota vikings', 'w 28 - 15', '70020'], ['5', 'october 14 , 2001', 'carolina panthers', 'w 27 - 25', '72049'], ['6', 'october 21 , 2001', 'atlanta falcons', 'l 20 - 13', '70020'], ['7', 'october 28 , 2001', 'st louis rams', 'w 34 - 31', '66189'], ['8', 'november 4 , 2001', 'new york jets', 'l 16 - 9', '70020'], ['9', 'november 11 , 2001', 'san francisco 49ers', 'l 28 - 27', '68063'], ['10', 'november 18 , 2001', 'indianapolis colts', 'w 34 - 20', '70020'], ['11', 'november 25 , 2001', 'new england patriots', 'l 34 - 17', '60292'], ['12', 'december 2 , 2001', 'carolina panthers', 'w 27 - 23', '70020'], ['13', 'december 9 , 2001', 'atlanta falcons', 'w 28 - 10', '68826'], ['14', 'december 17 , 2001', 'st louis rams', 'l 34 - 21', '70332'], ['15', 'december 23 , 2001', 'tampa bay buccaneers', 'l 48 - 21', '65526'], ['16', 'december 30 , 2001', 'washington redskins', 'l 40 - 10', '70020'], ['17', 'january 6 , 2002', 'san francisco 49ers', 'l 38 - 0', '70020']] |
first championship | https://en.wikipedia.org/wiki/FIRST_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15584199-3.html.csv | ordinal | sap g33k was the team with the third highest team number in the first championship . | {'row': '5', 'col': '4', 'order': '3', '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', 'team number', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; team number ; 3 }'}, 'team name'], 'result': 'sap g33k', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; team number ; 3 } ; team name }'}, 'sap g33k'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; team number ; 3 } ; team name } ; sap g33k } = true', 'tointer': 'select the row whose team number record of all rows is 3rd maximum . the team name record of this row is sap g33k .'} | eq { hop { nth_argmax { all_rows ; team number ; 3 } ; team name } ; sap g33k } = true | select the row whose team number record of all rows is 3rd maximum . the team name record of this row is sap g33k . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'team number_5': 5, '3_6': 6, 'team name_7': 7, 'sap g33k_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', 'team number_5': 'team number', '3_6': '3', 'team name_7': 'team name', 'sap g33k_8': 'sap g33k'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'team number_5': [0], '3_6': [0], 'team name_7': [1], 'sap g33k_8': [2]} | ['year / theme', 'award name', 'team name', 'team number', 'city , state / country'] | [['2012 / food factor', 'championship winner - 1st place', 'falcons japan', '15650', 'tokyo , japan'], ['2012 / food factor', 'championship winner - 2nd place', 'blue gear ticks', '252', 'lincoln , ma , usa'], ['2012 / food factor', 'championship winner - 3rd place', 'nxtremers', '15200', 'bengaluru , india'], ['2011 / body forward', 'championship winner - 1st place', 'the sentinels', '3663', 'oakville , on , canada'], ['2011 / body forward', 'championship winner - 2nd place', 'sap g33k', '13300', 'mpumalanga , south africa'], ['2011 / body forward', 'championship winner - 3rd place', 'hammerheads', '4129', 'umatilla , fl , usa'], ['2011 / body forward', 'robot performance award', 'hammerheads', '4129', 'umatilla , fl , usa'], ['2010 / smart move', 'championship winner - 3rd place', 'cougar robotics team', '437', 'columbus , oh , usa'], ['2009 / climate connections', 'championship winner - 1st place', 'da peeps', '55', 'swartz creek , mi , usa'], ['2009 / climate connections', 'championship winner - 2nd place', 'steele', '1232', 'illinois , usa'], ['2009 / climate connections', 'championship winner - 3rd place', 'nxt generation', '9201', 'nordborg , denmark'], ['2009 / climate connections', 'robot performance award - 1st place', 'emerotecos', '8004', 'brazil'], ['2009 / climate connections', 'robot performance award - 2nd place', 'team singapore', '8254', 'singapore'], ['2009 / climate connections', 'robot performance award - 3rd place', 'giant panda', '8060', 'china'], ['2008 / power puzzle', 'championship winner - 1st place', 'external fusion', '8095', 'singapore'], ['2008 / power puzzle', 'championship winner - 2nd place', 'pixelation', '2560', 'north branch , mn , usa'], ['2008 / power puzzle', 'championship winner - 3rd place', 'power peeps', '334', 'swartz creek , mi , usa'], ['2008 / power puzzle', 'robot performance award - 1st place', 'black ocean current', '8110', 'kaohsiung , taiwan'], ['2008 / power puzzle', 'robot performance award - 1st place', 'green man group', '1', 'windham , nh , usa'], ['2008 / power puzzle', 'robot performance award - 3rd place', 'landroids', '2254', 'livingston , nj , usa']] |
vivian girls | https://en.wikipedia.org/wiki/Vivian_Girls | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18710512-3.html.csv | comparative | the single titled ' tell the world ' by vivian girls sold more copies than the single ' my love will follow me ' . | {'row_1': '2', 'row_2': '6', 'col': '6', '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', 'single', 'tell the world'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose single record fuzzily matches to tell the world .', 'tostr': 'filter_eq { all_rows ; single ; tell the world }'}, 'other details'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; single ; tell the world } ; other details }', 'tointer': 'select the rows whose single record fuzzily matches to tell the world . take the other details record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'single', 'my love will follow me'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose single record fuzzily matches to my love will follow me .', 'tostr': 'filter_eq { all_rows ; single ; my love will follow me }'}, 'other details'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; single ; my love will follow me } ; other details }', 'tointer': 'select the rows whose single record fuzzily matches to my love will follow me . take the other details record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; single ; tell the world } ; other details } ; hop { filter_eq { all_rows ; single ; my love will follow me } ; other details } } = true', 'tointer': 'select the rows whose single record fuzzily matches to tell the world . take the other details record of this row . select the rows whose single record fuzzily matches to my love will follow me . take the other details record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; single ; tell the world } ; other details } ; hop { filter_eq { all_rows ; single ; my love will follow me } ; other details } } = true | select the rows whose single record fuzzily matches to tell the world . take the other details record of this row . select the rows whose single record fuzzily matches to my love will follow me . take the other details 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, 'single_7': 7, 'tell the world_8': 8, 'other details_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'single_11': 11, 'my love will follow me_12': 12, 'other details_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', 'single_7': 'single', 'tell the world_8': 'tell the world', 'other details_9': 'other details', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'single_11': 'single', 'my love will follow me_12': 'my love will follow me', 'other details_13': 'other details'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'single_7': [0], 'tell the world_8': [0], 'other details_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'single_11': [1], 'my love will follow me_12': [1], 'other details_13': [3]} | ['date', 'single', 'backed with', 'record label', 'format', 'other details'] | [['2008', 'wild eyes', 'my baby wants me dead', 'plays with dolls / wild world', '7 single', '4000 copies'], ['2008', 'tell the world', 'i believe in nothing & damaged', 'woodsist', '7 single', '3000 copies'], ['2008', "i ca n't stay", 'blind spot', 'in the red', '7 single', '2000 copies'], ['2008', 'surfin away & second date', "girl do n't tell me ( wilson )", 'wild world', '7 single', '1000 copies'], ['2009', 'moped girls', 'death', 'for us', '7 single', '1500 copies'], ['2010', 'my love will follow me', "he 's gone ( the chantels cover )", 'wild world', '7 single', '2000 copies']] |
new england football conference | https://en.wikipedia.org/wiki/New_England_Football_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-261927-1.html.csv | aggregation | the average enrollment for private colleges in the new england football conference is 4,523 . | {'scope': 'subset', 'col': '5', 'type': 'average', 'result': '4,523', 'subset': {'col': '4', 'criterion': 'not_equal', 'value': 'public'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_not_eq', 'args': ['all_rows', 'type', 'public'], 'result': None, 'ind': 0, 'tostr': 'filter_not_eq { all_rows ; type ; public }', 'tointer': 'select the rows whose type record does not match to public .'}, 'enrollment'], 'result': '4,523', 'ind': 1, 'tostr': 'avg { filter_not_eq { all_rows ; type ; public } ; enrollment }'}, '4,523'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_not_eq { all_rows ; type ; public } ; enrollment } ; 4,523 } = true', 'tointer': 'select the rows whose type record does not match to public . the average of the enrollment record of these rows is 4,523 .'} | round_eq { avg { filter_not_eq { all_rows ; type ; public } ; enrollment } ; 4,523 } = true | select the rows whose type record does not match to public . the average of the enrollment record of these rows is 4,523 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_not_eq_0': 0, 'all_rows_4': 4, 'type_5': 5, 'public_6': 6, 'enrollment_7': 7, '4,523_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_not_eq_0': 'filter_str_not_eq', 'all_rows_4': 'all_rows', 'type_5': 'type', 'public_6': 'public', 'enrollment_7': 'enrollment', '4,523_8': '4,523'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_not_eq_0': [1], 'all_rows_4': [0], 'type_5': [0], 'public_6': [0], 'enrollment_7': [1], '4,523_8': [2]} | ['institution', 'location', 'founded', 'type', 'enrollment', 'nickname', 'joined', 'primary conference', 'colors'] | [['endicott college', 'beverly , massachusetts', '1939', 'private', '3810', 'gulls', '2003', 'tccc', 'blue & green'], ['maine maritime academy', 'castine , maine', '1941', 'public', '858', 'mariners', '1965', 'nac', 'blue & gold'], ['massachusetts institute of technology ( mit )', 'cambridge , massachusetts', '1861', 'private', '10235', 'engineers', '1998', 'newmac', 'red & silver'], ['nichols college', 'dudley , massachusetts', '1815', 'private', '1459', 'bison', '1972 1998', 'tccc', 'green & black'], ['salve regina university', 'newport , rhode island', '1934', 'private / catholic', '2589', 'seahawks', '1998', 'tccc', 'blue & green']] |
central collegiate lacrosse association | https://en.wikipedia.org/wiki/Central_Collegiate_Lacrosse_Association | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28211213-2.html.csv | unique | northern michigan university is the only institution located in marquette , michigan . | {'scope': 'all', 'row': '13', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'marquette , michigan', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'marquette , michigan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to marquette , michigan .', 'tostr': 'filter_eq { all_rows ; location ; marquette , michigan }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; location ; marquette , michigan } }', 'tointer': 'select the rows whose location record fuzzily matches to marquette , michigan . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'marquette , michigan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to marquette , michigan .', 'tostr': 'filter_eq { all_rows ; location ; marquette , michigan }'}, 'institution'], 'result': 'northern michigan university', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; location ; marquette , michigan } ; institution }'}, 'northern michigan university'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; location ; marquette , michigan } ; institution } ; northern michigan university }', 'tointer': 'the institution record of this unqiue row is northern michigan university .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; location ; marquette , michigan } } ; eq { hop { filter_eq { all_rows ; location ; marquette , michigan } ; institution } ; northern michigan university } } = true', 'tointer': 'select the rows whose location record fuzzily matches to marquette , michigan . there is only one such row in the table . the institution record of this unqiue row is northern michigan university .'} | and { only { filter_eq { all_rows ; location ; marquette , michigan } } ; eq { hop { filter_eq { all_rows ; location ; marquette , michigan } ; institution } ; northern michigan university } } = true | select the rows whose location record fuzzily matches to marquette , michigan . there is only one such row in the table . the institution record of this unqiue row is northern michigan university . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'location_7': 7, 'marquette , michigan_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'institution_9': 9, 'northern michigan university_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'location_7': 'location', 'marquette , michigan_8': 'marquette , michigan', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'institution_9': 'institution', 'northern michigan university_10': 'northern michigan university'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'location_7': [0], 'marquette , michigan_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'institution_9': [2], 'northern michigan university_10': [3]} | ['institution', 'location', 'founded', 'affiliation', 'enrollment', 'team nickname', 'primary conference'] | [['aquinas college', 'grand rapids , michigan', '1886', 'private', '2159', 'saints', 'whac ( naia )'], ['butler university', 'indianapolis , indiana', '1855', 'private', '4512', 'bulldogs', 'horizon ( division i )'], ['carnegie mellon university', 'pittsburgh , pennsylvania', '1900', 'private / nonsectarian', '10875', 'tartans', 'uaa ( division iii )'], ['university of dayton', 'dayton , ohio', '1850', 'private / catholic', '10569', 'flyers', 'atlantic 10 ( division i )'], ['ferris state university', 'big rapids , michigan', '1884', 'public', '13865', 'bulldogs', 'gliac ( division ii )'], ['grand valley state university', 'allendale , michigan', '1960', 'public', '24408', 'lakers', 'gliac ( division ii )'], ['grove city college', 'grove city , pennsylvania', '1876', 'private / christian', '2500', 'wolverines', 'pac ( division iii )'], ['indiana institute of technology', 'fort wayne , indiana', '1930', 'private', '3207', 'warriors', 'whac ( naia )'], ['john carroll university', 'university heights , ohio', '1886', 'private / catholic', '3709', 'blue streaks', 'oac ( division iii )'], ['lawrence technological university', 'southfield , mi', '1932', 'private', '4000', 'blue devils', 'whac ( naia )'], ['lourdes college', 'sylvania , oh', '1958', 'private / catholic', '2616', 'gray wolves', 'whac ( naia )'], ['university of michigan - dearborn', 'dearborn , michigan', '1959', 'public', '8634', 'wolves', 'wolverine - hoosier ( naia )'], ['northern michigan university', 'marquette , michigan', '1899', 'public', '8578', 'wildcats', 'gliac ( division ii )'], ['northwood university', 'midland , michigan', '1961', 'private', '1987', 'timberwolves', 'gliac ( division ii )'], ['oakland university', 'rochester , michigan', '1957', 'public', '18553', 'grizzlies', 'the summit league ( division i )'], ['siena heights university', 'adrian , michigan', '1919', 'private / catholic', '2274', 'saints', 'wolverine - hoosier ( naia )']] |
law & order : special victims unit ( season 2 ) | https://en.wikipedia.org/wiki/Law_%26_Order%3A_Special_Victims_Unit_%28season_2%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14857583-1.html.csv | aggregation | the last two episodes of law and order : special victims unit had a total of 26.10 million viewers . | {'scope': 'subset', 'col': '8', 'type': 'sum', 'result': '26.10', 'subset': {'col': '1', 'criterion': 'greater_than_eq', 'value': '40'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'no in series', '40'], 'result': None, 'ind': 0, 'tostr': 'filter_greater_eq { all_rows ; no in series ; 40 }', 'tointer': 'select the rows whose no in series record is greater than or equal to 40 .'}, 'us viewers ( millions )'], 'result': '26.10', 'ind': 1, 'tostr': 'sum { filter_greater_eq { all_rows ; no in series ; 40 } ; us viewers ( millions ) }'}, '26.10'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_greater_eq { all_rows ; no in series ; 40 } ; us viewers ( millions ) } ; 26.10 } = true', 'tointer': 'select the rows whose no in series record is greater than or equal to 40 . the sum of the us viewers ( millions ) record of these rows is 26.10 .'} | round_eq { sum { filter_greater_eq { all_rows ; no in series ; 40 } ; us viewers ( millions ) } ; 26.10 } = true | select the rows whose no in series record is greater than or equal to 40 . the sum of the us viewers ( millions ) record of these rows is 26.10 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_greater_eq_0': 0, 'all_rows_4': 4, 'no in series_5': 5, '40_6': 6, 'us viewers (millions)_7': 7, '26.10_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_4': 'all_rows', 'no in series_5': 'no in series', '40_6': '40', 'us viewers (millions)_7': 'us viewers ( millions )', '26.10_8': '26.10'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_greater_eq_0': [1], 'all_rows_4': [0], 'no in series_5': [0], '40_6': [0], 'us viewers (millions)_7': [1], '26.10_8': [2]} | ['no in series', 'no in season', 'title', 'directed by', 'written by', 'original air date', 'production code', 'us viewers ( millions )'] | [['23', '1', 'wrong is right', 'ted kotcheff', 'david j burke & jeff eckerle', 'october 20 , 2000', 'e1403', '13.39'], ['24', '2', 'honor', 'alan metzger', 'robert f campbell & jonathan greene', 'october 27 , 2000', 'e1407', '13.20'], ['26', '4', 'legacy', 'jud taylor', 'jeff eckerle', 'november 10 , 2000', 'e1401', '13.40'], ['27', '5', 'baby killer', 'juan j campanella', 'dawn denoon & lisa marie petersen', 'november 17 , 2000', 'e1411', '12.80'], ['28', '6', 'noncompliance', 'elodie keene', 'judith mccreary', 'november 24 , 2000', 'e1417', '15.40'], ['29', '7', 'asunder', 'david platt', 'judith mccreary', 'december 1 , 2000', 'e1404', '15.43'], ['30', '8', 'taken', 'michael fields', 'dawn denoon & lisa marie petersen', 'december 15 , 2000', 'e1406', '14.31'], ['32', '10', 'consent', 'james quinn', 'jeff eckerle', 'january 19 , 2001', 'e1419', '13.60'], ['35', '13', 'victims', 'constantine makris', 'nick kendrick', 'february 9 , 2001', 'e1420', '14.90'], ['36', '14', 'paranoia', 'richard dobbs', 'robert f campbell & jonathan greene', 'february 16 , 2001', 'e1426', '14.10'], ['37', '15', 'countdown', 'steve shill', 'dawn denoon & lisa marie petersen', 'february 23 , 2001', 'e1412', '15.50'], ['38', '16', 'runaway', 'richard dobbs', 'david j burke & nick kendrick', 'march 2 , 2001', 'e1405', '14.56'], ['39', '17', 'folly', 'jud taylor', 'todd robinson', 'march 23 , 2001', 'e1428', '14.30'], ['40', '18', 'manhunt', 'stephen wertimer', 'jeff eckerle', 'april 20 , 2001', 'e1431', '12.70'], ['41', '19', 'parasites', 'david platt', 'martin weiss', 'april 27 , 2001', 'e1427', '13.40']] |
2005 - 06 mighty ducks of anaheim season | https://en.wikipedia.org/wiki/2005%E2%80%9306_Mighty_Ducks_of_Anaheim_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18987966-27.html.csv | majority | most of the players on the 2005-06 mighty ducks team were from canada . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'canada', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'nationality', 'canada'], 'result': True, 'ind': 0, 'tointer': 'for the nationality records of all rows , most of them fuzzily match to canada .', 'tostr': 'most_eq { all_rows ; nationality ; canada } = true'} | most_eq { all_rows ; nationality ; canada } = true | for the nationality records of all rows , most of them fuzzily match to canada . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'nationality_3': 3, 'canada_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'nationality_3': 'nationality', 'canada_4': 'canada'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'nationality_3': [0], 'canada_4': [0]} | ['round', 'player', 'position', 'nationality', 'college / junior / club team ( league )'] | [['1', 'bobby ryan', '( rw )', 'united states', 'owen sound attack ( ohl )'], ['2', 'brendan mikkelson', '( d )', 'canada', 'portland winter hawks ( whl )'], ['3', 'jason bailey', '( rw )', 'canada', 'us national team development program'], ['5', 'bobby bolt', '( lw )', 'canada', 'kingston frontenacs ( ohl )'], ['5', 'brian salcido', '( d )', 'united states', 'colorado college ( wcha )'], ['7', 'jean - philippe levasseur', 'g', 'canada', 'rouyn - noranda huskies ( qmjhl )']] |
2007 - 08 washington capitals season | https://en.wikipedia.org/wiki/2007%E2%80%9308_Washington_Capitals_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11772462-4.html.csv | majority | them majority of the matches played had an attendance of less than 17,000 . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '17000', 'subset': None} | {'func': 'most_less', 'args': ['all_rows', 'attendance', '17000'], 'result': True, 'ind': 0, 'tointer': 'for the attendance records of all rows , most of them are less than 17000 .', 'tostr': 'most_less { all_rows ; attendance ; 17000 } = true'} | most_less { all_rows ; attendance ; 17000 } = true | for the attendance records of all rows , most of them are less than 17000 . | 1 | 1 | {'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'attendance_3': 3, '17000_4': 4} | {'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'attendance_3': 'attendance', '17000_4': '17000'} | {'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'attendance_3': [0], '17000_4': [0]} | ['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record'] | [['november 1', 'washington', '0 - 2', 'ny rangers', 'kolzig', '18200', '5 - 7 - 0'], ['november 2', 'philadelphia', '3 - 2', 'washington', 'kolzig', '16055', '5 - 8 - 0'], ['november 5', 'washington', '0 - 5', 'carolina', 'kolzig', '12171', '5 - 9 - 0'], ['november 6', 'washington', '1 - 2', 'atlanta', 'johnson', '15530', '5 - 9 - 1'], ['november 8', 'washington', '4 - 1', 'ottawa', 'kolzig', '19666', '6 - 9 - 1'], ['november 10', 'tampa bay', '5 - 2', 'washington', 'kolzig', '14617', '6 - 10 - 1'], ['november 15', 'washington', '1 - 2', 'florida', 'kolzig', '12101', '6 - 11 - 1'], ['november 16', 'washington', '2 - 5', 'tampa bay', 'kolzig', '19526', '6 - 12 - 1'], ['november 19', 'florida', '4 - 3', 'washington', 'kolzig', '13411', '6 - 13 - 1'], ['november 21', 'atlanta', '5 - 1', 'washington', 'kolzig', '11669', '6 - 14 - 1'], ['november 23', 'washington', '4 - 3', 'philadelphia', 'kolzig', '19727', '7 - 14 - 1'], ['november 24', 'carolina', '2 - 5', 'washington', 'kolzig', '13650', '8 - 14 - 1'], ['november 26', 'buffalo', '3 - 1', 'washington', 'kolzig', '11204', '8 - 15 - 1'], ['november 28', 'florida', '2 - 1', 'washington', 'kolzig', '10526', '8 - 15 - 2'], ['november 30', 'washington', '3 - 4', 'carolina', 'kolzig', '16386', '8 - 16 - 2']] |
the secret garden ( musical ) | https://en.wikipedia.org/wiki/The_Secret_Garden_%28musical%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1901751-1.html.csv | majority | most of the characters in the secret garden that have a surname belong to the craven family . | {'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'craven', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'character', 'craven'], 'result': True, 'ind': 0, 'tointer': 'for the character records of all rows , most of them fuzzily match to craven .', 'tostr': 'most_eq { all_rows ; character ; craven } = true'} | most_eq { all_rows ; character ; craven } = true | for the character records of all rows , most of them fuzzily match to craven . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'character_3': 3, 'craven_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'character_3': 'character', 'craven_4': 'craven'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'character_3': [0], 'craven_4': [0]} | ['character', 'original broadway performer', 'original australian performer', 'original west end performer', '2005 world aids day benefit dream cast'] | [['mary lennox', 'daisy eagan', 'samantha fiddes / sarah ogden', 'natalie morgan', 'jaclyn neidenthal'], ['archibald craven', 'mandy patinkin', 'anthony warlow', 'philip quast', 'steven pasquale'], ['lily craven', 'rebecca luker', 'marina prior', 'meredith braun', 'laura benanti'], ['neville craven', 'robert westenberg', 'philip quast', 'peter polycarpou', 'will chase'], ['martha', 'alison fraser', 'susan - ann walker', 'linzi hateley', 'celia keenan - bolger'], ['dickon', 'john cameron mitchell', 'tom blair', 'jordan dunne', 'michael arden'], ['colin craven', 'john babcock', 'bart ritchie / ross hannaford', 'luke newberry', 'struan erlenborn'], ['ben weatherstaff', 'tom toner', 'raymond duprac', 'n / a', 'david canary']] |
1986 icf canoe sprint world championships | https://en.wikipedia.org/wiki/1986_ICF_Canoe_Sprint_World_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18715280-4.html.csv | ordinal | of the nations competing in the 1986 icf canoe sprint world championships , romania won the 2nd most gold medals . | {'row': '3', 'col': '3', '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', 'gold', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; gold ; 2 }'}, 'nation'], 'result': 'romania', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; gold ; 2 } ; nation }'}, 'romania'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; gold ; 2 } ; nation } ; romania } = true', 'tointer': 'select the row whose gold record of all rows is 2nd maximum . the nation record of this row is romania .'} | eq { hop { nth_argmax { all_rows ; gold ; 2 } ; nation } ; romania } = true | select the row whose gold record of all rows is 2nd maximum . the nation record of this row is romania . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'gold_5': 5, '2_6': 6, 'nation_7': 7, 'romania_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', 'gold_5': 'gold', '2_6': '2', 'nation_7': 'nation', 'romania_8': 'romania'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'gold_5': [0], '2_6': [0], 'nation_7': [1], 'romania_8': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'hungary', '7', '3', '1', '11'], ['2', 'soviet union', '1', '6', '3', '10'], ['3', 'romania', '3', '3', '2', '8'], ['4', 'east germany', '2', '3', '2', '7'], ['5', 'poland', '1', '1', '1', '3'], ['6', 'bulgaria', '1', '0', '2', '3'], ['7', 'west germany', '1', '0', '2', '3'], ['8', 'united kingdom', '2', '0', '0', '2'], ['9', 'france', '0', '1', '1', '2'], ['10', 'yugoslavia', '0', '1', '1', '2'], ['11', 'denmark', '0', '0', '2', '2'], ['12', 'australia', '0', '0', '1', '1'], ['total', 'total', '18', '18', '18', '54']] |
dinamo riga | https://en.wikipedia.org/wiki/Dinamo_Riga | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20833768-4.html.csv | comparative | dinamo riga had a higher league ranking in the 2010-11 season than the 2012-13 season . | {'row_1': '3', 'row_2': '5', 'col': '8', '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', 'season', '2010 - 11'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose season record fuzzily matches to 2010 - 11 .', 'tostr': 'filter_eq { all_rows ; season ; 2010 - 11 }'}, 'rank ( league / conference )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; season ; 2010 - 11 } ; rank ( league / conference ) }', 'tointer': 'select the rows whose season record fuzzily matches to 2010 - 11 . take the rank ( league / conference ) record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'season', '2012 - 13'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose season record fuzzily matches to 2012 - 13 .', 'tostr': 'filter_eq { all_rows ; season ; 2012 - 13 }'}, 'rank ( league / conference )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; season ; 2012 - 13 } ; rank ( league / conference ) }', 'tointer': 'select the rows whose season record fuzzily matches to 2012 - 13 . take the rank ( league / conference ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; season ; 2010 - 11 } ; rank ( league / conference ) } ; hop { filter_eq { all_rows ; season ; 2012 - 13 } ; rank ( league / conference ) } } = true', 'tointer': 'select the rows whose season record fuzzily matches to 2010 - 11 . take the rank ( league / conference ) record of this row . select the rows whose season record fuzzily matches to 2012 - 13 . take the rank ( league / conference ) record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; season ; 2010 - 11 } ; rank ( league / conference ) } ; hop { filter_eq { all_rows ; season ; 2012 - 13 } ; rank ( league / conference ) } } = true | select the rows whose season record fuzzily matches to 2010 - 11 . take the rank ( league / conference ) record of this row . select the rows whose season record fuzzily matches to 2012 - 13 . take the rank ( league / conference ) 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, 'season_7': 7, '2010 - 11_8': 8, 'rank (league / conference)_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'season_11': 11, '2012 - 13_12': 12, 'rank (league / conference)_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', 'season_7': 'season', '2010 - 11_8': '2010 - 11', 'rank (league / conference)_9': 'rank ( league / conference )', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'season_11': 'season', '2012 - 13_12': '2012 - 13', 'rank (league / conference)_13': 'rank ( league / conference )'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'season_7': [0], '2010 - 11_8': [0], 'rank (league / conference)_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'season_11': [1], '2012 - 13_12': [1], 'rank (league / conference)_13': [3]} | ['season', 'gp', 'w ( ot / so )', 'l ( ot / so )', 'pts', 'pts / gp', 'gf - ga', 'rank ( league / conference )', 'top scorer'] | [['2008 - 09', '56', '24 ( 3 / 2 )', '23 ( 1 / 3 )', '86', '1.54', '132 - 156', '10th / -', 'marcel hossa ( 44 )'], ['2009 - 10', '56', '23 ( 1 / 3 )', '22 ( 3 / 4 )', '84', '1.50', '174 - 175', '13th / 8th', 'marcel hossa ( 55 )'], ['2010 - 11', '54', '20 ( 2 / 5 )', '20 ( 5 / 2 )', '81', '1.50', '160 - 149', '13th / 7th', 'lauris dārziņš ( 44 )'], ['2011 - 12', '54', '20 ( 2 / 4 )', '21 ( 0 / 7 )', '79', '1.46', '129 - 136', '15th / 7th', 'miķelis rēdlihs ( 44 )'], ['2012 - 13', '52', '11 ( 2 / 2 )', '31 ( 2 / 2 )', '51', '0.98', '109 - 151', '24th / 14th', 'mārtiņš karsums ( 35 )']] |
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 | majority | the majority of tony kanaan 's chassises were made by dallara . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'dallara', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'chassis', 'dallara'], 'result': True, 'ind': 0, 'tointer': 'for the chassis records of all rows , most of them fuzzily match to dallara .', 'tostr': 'most_eq { all_rows ; chassis ; dallara } = true'} | most_eq { all_rows ; chassis ; dallara } = true | for the chassis records of all rows , most of them fuzzily match to dallara . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'chassis_3': 3, 'dallara_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'chassis_3': 'chassis', 'dallara_4': 'dallara'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'chassis_3': [0], 'dallara_4': [0]} | ['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']] |
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