Datasets:

kasnerz

/

logic2text

like 0

aguaclara

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18268930-1.html.csv

aggregation

the average design flow of all plants designed by aguaclara is 823.57 lpm .

{'scope': 'all', 'col': '6', 'type': 'average', 'result': '823.57', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'design flow ( lpm )'], 'result': '823.57', 'ind': 0, 'tostr': 'avg { all_rows ; design flow ( lpm ) }'}, '823.57'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; design flow ( lpm ) } ; 823.57 } = true', 'tointer': 'the average of the design flow ( lpm ) record of all rows is 823.57 .'}

round_eq { avg { all_rows ; design flow ( lpm ) } ; 823.57 } = true

the average of the design flow ( lpm ) record of all rows is 823.57 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'design flow (lpm)_4': 4, '823.57_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'design flow (lpm)_4': 'design flow ( lpm )', '823.57_5': '823.57'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'design flow (lpm)_4': [0], '823.57_5': [1]}

['location', 'partner', 'construction start', 'inauguration date', 'population served', 'design flow ( lpm )']

[['ojojona , hon', 'app', '2006 june', '2007 july', '2000', '375'], ['tamara , hon', 'app', '2008 january', '2008 june', '3500', '720'], ['marcala , hon', 'irwa', '2007 october', '2008 july', '9000', '1900'], ['4 comunidades , hon', 'app', '2008 october', '2009 march', '2000', '375'], ['agalteca , hon', 'app', '2009 october', '2010 june', '2200', '375'], ['marcala , hon expansion', 'app / acra', '2010 november', '2011 may', '6000', '1300'], ['alauca , el paraiso , hon', 'app', '2011 may', '2012 february', '3600', '720']]

henlopen conference

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13054553-8.html.csv

aggregation

the average number of wins in the division record is approximately 3.14 .

{'scope': 'all', 'col': '3', 'type': 'average', 'result': '3.14', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'division record'], 'result': '3.14', 'ind': 0, 'tostr': 'avg { all_rows ; division record }'}, '3.14'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; division record } ; 3.14 } = true', 'tointer': 'the average of the division record record of all rows is 3.14 .'}

round_eq { avg { all_rows ; division record } ; 3.14 } = true

the average of the division record record of all rows is 3.14 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'division record_4': 4, '3.14_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'division record_4': 'division record', '3.14_5': '3.14'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'division record_4': [0], '3.14_5': [1]}

['school', 'team', 'division record', 'overall record', 'season outcome']

[['delmar', 'wildcats', '6 - 0', '11 - 1', 'loss in div ii championship game'], ['indian river', 'indians', '5 - 1', '8 - 3', 'loss in first round of div ii playoffs'], ['woodbridge', 'blue raiders', '4 - 2', '6 - 4', 'failed to make playoffs'], ['laurel', 'bulldogs', '3 - 3', '6 - 4', 'failed to make playoffs'], ['smyrna', 'eagles', '2 - 4', '3 - 7', 'failed to make playoffs'], ['seaford', 'blue jays', '1 - 5', '2 - 8', 'failed to make playoffs'], ['lake forest', 'spartans', '1 - 5', '1 - 9', 'failed to make playoffs']]

2006 v8 supercar championship series

https://en.wikipedia.org/wiki/2006_V8_Supercar_Championship_Series

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20884160-1.html.csv

majority

the majority of the winner 's teams in the 2006 v8 supercar championship series is the triple eight race engineering .

{'scope': 'all', 'col': '8', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'triple eight race engineering', 'subset': None}

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

most_eq { all_rows ; team ; triple eight race engineering } = true

for the team records of all rows , most of them fuzzily match to triple eight race engineering .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'team_3': 3, 'triple eight race engineering_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'team_3': 'team', 'triple eight race engineering_4': 'triple eight race engineering'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'team_3': [0], 'triple eight race engineering_4': [0]}

['rd', 'race title', 'circuit', 'location', 'date', 'format', 'winner', 'team', 'report']

[['1', 'clipsal 500 adelaide', 'adelaide street circuit', 'adelaide , south australia', '23 - 26 mar', 'two races', 'jamie whincup', 'triple eight race engineering', 'report'], ['2', 'placemakers v8 supercars', 'pukekohe park raceway', 'pukekohe , new zealand', '21 - 23 apr', 'three races', 'mark skaife', 'holden racing team', 'report'], ['3', 'perth v8 400', 'barbagallo raceway', 'wanneroo , western australia', '1214 may', 'three races', 'steven richards', 'perkins engineering', 'report'], ['4', 'winton', 'winton motor raceway', 'benalla , victoria', '2 - 4 jun', 'three races', 'craig lowndes', 'triple eight race engineering', 'report'], ['5', 'skycity triple crown', 'hidden valley raceway', 'darwin , northern territory', '30 jun - 2 jul', 'three races', 'craig lowndes', 'triple eight race engineering', 'report'], ['6', 'bigpond 400', 'queensland raceway', 'ipswich , queensland', '21 - 23 jul', 'three races', 'garth tander', 'hsv dealer team', 'report'], ['7', 'oran park', 'oran park raceway', 'sydney , new south wales', '11 - 13 aug', 'three races', 'craig lowndes', 'triple eight race engineering', 'report'], ['8', 'betta electrical 500', 'sandown raceway', 'melbourne , victoria', '1 - 3 sept', 'one race', 'mark winterbottom jason bright', 'ford performance racing', 'report'], ['9', 'supercheap auto bathurst 1000', 'mount panorama', 'bathurst , new south wales', '5 - 8 oct', 'one race', 'craig lowndes jamie whincup', 'triple eight race engineering', 'report'], ['10', 'gillette v8 supercar challenge', 'gold coast street circuit', 'surfers paradise , queensland', '19 - 22 oct', 'three races', 'todd kelly', 'holden racing team', 'report'], ['11', 'ferodo tasmania challenge', 'symmons plains raceway', 'launceston , tasmania', '10 - 12 nov', 'three races', 'garth tander', 'hsv dealer team', 'report'], ['12', 'desert 400', 'bahrain international circuit', 'manama , bahrain', '23 - 25 nov', 'three races', 'jason bright', 'ford performance racing', 'report']]

pune suburban railway

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29301050-1.html.csv

majority

the majority of pune suburban railway trains originate from the pune railway station .

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

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

most_eq { all_rows ; origin ; pune railway station } = true

for the origin records of all rows , most of them fuzzily match to pune railway station .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'origin_3': 3, 'pune railway station_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'origin_3': 'origin', 'pune railway station_4': 'pune railway station'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'origin_3': [0], 'pune railway station_4': [0]}

['train number', 'train name', 'departure pune', 'arrival lonavla', 'frequency', 'origin']

[['99804', 'lonavala local', '04:45', '06:05', 'daily', 'pune railway station'], ['99806', 'lonavala local', '05:45', '07:05', 'daily', 'pune railway station'], ['99808', 'lonavala local', '06:30', '07:50', 'daily', 'pune railway station'], ['99810', 'lonavala local', '08:05', '10:25', 'daily', 'pune railway station'], ['99812', 'lonavala local', '09:55', '11:15', 'daily', 'pune railway station'], ['99814', 'lonavala local', '10:50', '12:25', 'daily', 'shivajinagar station'], ['99816', 'lonavala local', '12:05', '13:17', 'daily', 'pune railway station'], ['99820', 'lonavala local', '13:00', '14:20', 'daily', 'pune railway station'], ['99822', 'lonavala local', '16:25', '17:45', 'daily', 'pune railway station'], ['99824', 'lonavala local', '16:25', '17:37', 'daily', 'pune railway station'], ['99826', 'lonavala local', '19:05', '20:25', 'daily', 'pune railway station'], ['99828', 'lonavala local', '19:35', '21:05', 'daily', 'shivajinagar station'], ['99830', 'lonavala local', '20:00', '21:20', 'daily', 'pune railway station'], ['99832', 'lonavala local', '20:00', '21:12', 'daily', 'pune railway station'], ['99834', 'lonavala local', '20:45', '21:57', 'daily', 'pune railway station'], ['99836', 'lonavala local', '21:10', '22:22', 'daily', 'pune railway station']]

list of are you afraid of the dark ? episodes

https://en.wikipedia.org/wiki/List_of_Are_You_Afraid_of_the_Dark%3F_episodes

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10470082-4.html.csv

majority

tucker told the most stories during this list of are you afraid of the dark episodes .

{'scope': 'all', 'col': '7', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'tucker', 'subset': None}

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

most_eq { all_rows ; storyteller ; tucker } = true

for the storyteller records of all rows , most of them fuzzily match to tucker .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'storyteller_3': 3, 'tucker_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'storyteller_3': 'storyteller', 'tucker_4': 'tucker'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'storyteller_3': [0], 'tucker_4': [0]}

['no', '-', 'title', 'director', 'writer', 'us air date', 'storyteller', 'villains']

[['27', '1', 'the tale of the midnight ride', 'd j machale', 'darren kotania', 'january 8 , 1994', 'tucker', 'brad & the headless horseman'], ['28', '2', 'the tale of apartment 214', 'scott peters', 'scott peters', 'january 15 , 1994', 'kiki', 'none'], ['29', '3', "the tale of watcher 's woods", 'david winning', 'gregory kennedy', 'january 22 , 1994', 'sam', 'the old hags and the watcher'], ['30', '4', 'the tale of the phone police', 'jean - marie comeau', 'david preston', 'january 19 , 1994', 'tucker on a phone', 'the phone police'], ['31', '5', 'the tale of the dollmaker', 'david winning', 'david preston', 'february 5 , 1994', 'betty ann', 'the dollhouse'], ['33', '7', 'the tale of the carved stone', 'ron oliver', 'susan kim', 'february 26 , 1994', 'gary', 'septimus'], ['34', '8', "the tale of the guardian 's curse", 'd j machale', 'chloe brown', 'march 5 , 1994', 'tucker', 'dr capel - smith'], ['35', '9', 'the tale of the curious camera', 'ron oliver', 'susan kim', 'march 19 , 1994', 'betty ann', 'the gremlin in the camera'], ['36', '10', 'the tale of the dream girl', 'david winning', 'david preston', 'march 26 , 1994', 'sam', 'none'], ['37', '11', 'the tale of the quicksilver', 'michael keusch', 'wendy brotherlin', 'april 2 , 1994', 'kiki', 'the demon'], ['38', '12', 'the tale of the crimson clown', 'ron oliver', 'darren kotania', 'april 9 , 1994', 'gary', 'the crimson clown']]

2006 pga championship

https://en.wikipedia.org/wiki/2006_PGA_Championship

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12475284-6.html.csv

unique

mike weir was the only player from canada in the 2006 pga championship .

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

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

and { only { filter_eq { all_rows ; country ; canada } } ; eq { hop { filter_eq { all_rows ; country ; canada } ; player } ; mike weir } } = true

select the rows whose country record fuzzily matches to canada . there is only one such row in the table . the player record of this unqiue row is mike weir .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'country_7': 7, 'canada_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'mike weir_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'country_7': 'country', 'canada_8': 'canada', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'mike weir_10': 'mike weir'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'country_7': [0], 'canada_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'mike weir_10': [3]}

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

[['t1', 'tiger woods', 'united states', '69 + 68 + 65 = 202', '- 14'], ['t1', 'luke donald', 'england', '68 + 68 + 66 = 202', '- 14'], ['3', 'mike weir', 'canada', '72 + 67 + 65 = 204', '- 12'], ['4', 'geoff ogilvy', 'australia', '69 + 68 + 68 = 205', '- 11'], ['t5', 'shaun micheel', 'united states', '69 + 70 + 67 = 206', '- 10'], ['t5', 'sergio garcía', 'spain', '69 + 70 + 67 = 206', '- 10'], ['7', 'k j choi', 'south korea', '73 + 67 + 67 = 207', '- 9'], ['t8', 'chris dimarco', 'united states', '71 + 70 + 67 = 208', '- 8'], ['t8', 'tim herron', 'united states', '69 + 67 + 72 = 208', '- 8'], ['t8', 'phil mickelson', 'united states', '69 + 71 + 68 = 208', '- 8'], ['t8', 'ian poulter', 'england', '70 + 70 + 68 = 208', '- 8']]

1978 kansas city chiefs season

https://en.wikipedia.org/wiki/1978_Kansas_City_Chiefs_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12536374-2.html.csv

comparative

the game against the cincinnati bengals had a higher attendance than the game against the houston oilers .

{'row_1': '1', 'row_2': '2', 'col': '5', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

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

greater { hop { filter_eq { all_rows ; opponent ; cincinnati bengals } ; attendance } ; hop { filter_eq { all_rows ; opponent ; houston oilers } ; attendance } } = true

select the rows whose opponent record fuzzily matches to cincinnati bengals . take the attendance record of this row . select the rows whose opponent record fuzzily matches to houston oilers . take the attendance record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opponent_7': 7, 'cincinnati bengals_8': 8, 'attendance_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'houston oilers_12': 12, 'attendance_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opponent_7': 'opponent', 'cincinnati bengals_8': 'cincinnati bengals', 'attendance_9': 'attendance', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'houston oilers_12': 'houston oilers', 'attendance_13': 'attendance'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'cincinnati bengals_8': [0], 'attendance_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'houston oilers_12': [1], 'attendance_13': [3]}

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

[['1', 'september 3 , 1978', 'cincinnati bengals', 'w 24 - 23', '41810'], ['2', 'september 10 , 1978', 'houston oilers', 'l 20 - 17', '40213'], ['3', 'september 17 , 1978', 'new york giants', 'l 26 - 10', '70546'], ['4', 'september 24 , 1978', 'denver broncos', 'l 23 - 17', '60593'], ['5', 'october 1 , 1978', 'buffalo bills', 'l 28 - 13', '47310'], ['6', 'october 8 , 1978', 'tampa bay buccaneers', 'l 30 - 13', '38201'], ['7', 'october 15 , 1978', 'oakland raiders', 'l 28 - 6', '50759'], ['8', 'october 22 , 1978', 'cleveland browns', 'w 17 - 3', '41157'], ['9', 'october 29 , 1978', 'pittsburgh steelers', 'l 27 - 24', '48185'], ['10', 'november 5 , 1978', 'oakland raiders', 'l 20 - 10', '75418'], ['11', 'november 12 , 1978', 'san diego chargers', 'l 29 - 23', '41395'], ['12', 'november 19 , 1978', 'seattle seahawks', 'l 13 - 10', '35252'], ['13', 'november 26 , 1978', 'san diego chargers', 'w 23 - 0', '26248'], ['14', 'december 3 , 1978', 'buffalo bills', 'w 14 - 10', '25781'], ['15', 'december 10 , 1978', 'denver broncos', 'l 24 - 3', '74149'], ['16', 'december 17 , 1978', 'seattle seahawks', 'l 23 - 19', '58490']]

list of best - selling music artists

https://en.wikipedia.org/wiki/List_of_best-selling_music_artists

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1291598-3.html.csv

comparative

best - selling music artist dire straits had less claimed sales than barbra streisand .

{'row_1': '16', 'row_2': '8', 'col': '6', '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', 'artist', 'dire straits'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose artist record fuzzily matches to dire straits .', 'tostr': 'filter_eq { all_rows ; artist ; dire straits }'}, 'claimed sales'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; artist ; dire straits } ; claimed sales }', 'tointer': 'select the rows whose artist record fuzzily matches to dire straits . take the claimed sales record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'artist', 'barbra streisand'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose artist record fuzzily matches to barbra streisand .', 'tostr': 'filter_eq { all_rows ; artist ; barbra streisand }'}, 'claimed sales'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; artist ; barbra streisand } ; claimed sales }', 'tointer': 'select the rows whose artist record fuzzily matches to barbra streisand . take the claimed sales record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; artist ; dire straits } ; claimed sales } ; hop { filter_eq { all_rows ; artist ; barbra streisand } ; claimed sales } } = true', 'tointer': 'select the rows whose artist record fuzzily matches to dire straits . take the claimed sales record of this row . select the rows whose artist record fuzzily matches to barbra streisand . take the claimed sales record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; artist ; dire straits } ; claimed sales } ; hop { filter_eq { all_rows ; artist ; barbra streisand } ; claimed sales } } = true

select the rows whose artist record fuzzily matches to dire straits . take the claimed sales record of this row . select the rows whose artist record fuzzily matches to barbra streisand . take the claimed sales 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, 'artist_7': 7, 'dire straits_8': 8, 'claimed sales_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'artist_11': 11, 'barbra streisand_12': 12, 'claimed sales_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', 'artist_7': 'artist', 'dire straits_8': 'dire straits', 'claimed sales_9': 'claimed sales', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'artist_11': 'artist', 'barbra streisand_12': 'barbra streisand', 'claimed sales_13': 'claimed sales'}

{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'artist_7': [0], 'dire straits_8': [0], 'claimed sales_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'artist_11': [1], 'barbra streisand_12': [1], 'claimed sales_13': [3]}

['artist', 'country of origin', 'period active', 'release - year of first charted record', 'genre', 'claimed sales']

[['eagles', 'united states', '1971 - present', '1972', 'soft rock / country rock', '150 million'], ['rihanna', 'barbados united states', '2005 - present', '2005', 'r & b / pop / dance / hip - hop', '150 million'], ['u2', 'ireland', '1976 - present', '1980', 'rock', '150 million'], ['billy joel', 'united states', '1964 - present', '1971', 'pop / rock', '150 million'], ['phil collins', 'united kingdom', '1980 - 2011', '1981', 'adult contemporary', '150 million'], ['aerosmith', 'united states', '1970 - present', '1973', 'hard rock', '150 million'], ['frank sinatra', 'united states', '1935 - 1995', '1939', 'pop / swing', '150 million'], ['barbra streisand', 'united states', '1960 - present', '1963', 'pop / adult contemporary', '145 million'], ['garth brooks', 'united states', '1989 - present', '1989', 'country', '130 million'], ['genesis', 'united kingdom', '1967 - 1999 2006 - 2011', '1969', 'progressive rock / pop rock', '130 million'], ['donna summer', 'united states', '1968 - 2012', '1974', 'pop / disco / r & b', '130 million'], ['neil diamond', 'united states', '1966 - present', '1966', 'pop / rock', '125 million'], ['bruce springsteen', 'united states', '1972 - present', '1973', 'rock', '120 million'], ['bee gees', 'united kingdom', '1963 - 2003 2009 - 2012', '1963', 'pop / disco', '120 million'], ['julio iglesias', 'spain', '1968 - present', '1968', 'latin', '120 million'], ['dire straits', 'united kingdom', '1977 - 1995', '1978', 'rock / pop', '120 million']]

inside business

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10888144-1.html.csv

majority

the majority of inside business seasons had a length of at least 40 episodes .

{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'greater_than_eq', 'value': '40', 'subset': None}

{'func': 'most_greater_eq', 'args': ['all_rows', 'episodes', '40'], 'result': True, 'ind': 0, 'tointer': 'for the episodes records of all rows , most of them are greater than or equal to 40 .', 'tostr': 'most_greater_eq { all_rows ; episodes ; 40 } = true'}

most_greater_eq { all_rows ; episodes ; 40 } = true

for the episodes records of all rows , most of them are greater than or equal to 40 .

1

1

{'most_greater_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'episodes_3': 3, '40_4': 4}

{'most_greater_eq_0': 'most_greater_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'episodes_3': 'episodes', '40_4': '40'}

{'most_greater_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'episodes_3': [0], '40_4': [0]}

['season no', 'season start', 'season end', 'episodes', 'host']

[['1', '4 august 2002', '8 december 2002', '19', 'alan kohler'], ['2', '9 february 2003', '30 november 2003', '41', 'alan kohler'], ['3', '15 february 2004', '5 december 2004', '41', 'alan kohler'], ['4', '13 february 2005', '4 december 2005', '42', 'alan kohler'], ['5', '12 february 2006', '10 december 2006', '43', 'alan kohler'], ['6', '11 february 2007', '9 december 2007', '43', 'alan kohler']]

jacksonville jaguars draft history

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15100419-3.html.csv

comparative

mike logan was drafted in an earlier round by the jacksonville jaguars than jon hesse .

{'row_1': '2', 'row_2': '7', 'col': '1', 'col_other': '4', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'mike logan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to mike logan .', 'tostr': 'filter_eq { all_rows ; name ; mike logan }'}, 'round'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; mike logan } ; round }', 'tointer': 'select the rows whose name record fuzzily matches to mike logan . take the round record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'jon hesse'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to jon hesse .', 'tostr': 'filter_eq { all_rows ; name ; jon hesse }'}, 'round'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; jon hesse } ; round }', 'tointer': 'select the rows whose name record fuzzily matches to jon hesse . take the round record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; name ; mike logan } ; round } ; hop { filter_eq { all_rows ; name ; jon hesse } ; round } } = true', 'tointer': 'select the rows whose name record fuzzily matches to mike logan . take the round record of this row . select the rows whose name record fuzzily matches to jon hesse . take the round record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; name ; mike logan } ; round } ; hop { filter_eq { all_rows ; name ; jon hesse } ; round } } = true

select the rows whose name record fuzzily matches to mike logan . take the round record of this row . select the rows whose name record fuzzily matches to jon hesse . take the round 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, 'name_7': 7, 'mike logan_8': 8, 'round_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'jon hesse_12': 12, 'round_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', 'name_7': 'name', 'mike logan_8': 'mike logan', 'round_9': 'round', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'jon hesse_12': 'jon hesse', 'round_13': 'round'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'mike logan_8': [0], 'round_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'jon hesse_12': [1], 'round_13': [3]}

['round', 'pick', 'overall', 'name', 'position', 'college']

[['1', '21', '21', 'renaldo wynn', 'defensive tackle', 'notre dame'], ['2', '20', '50', 'mike logan', 'cornerback', 'west virginia'], ['3', '19', '79', 'james hamilton', 'linebacker', 'north carolina'], ['4', '18', '114', 'seth payne', 'defensive tackle', 'cornell'], ['5', '17', '147', 'damon jones', 'tight end', 'southern illinois'], ['6', '21', '184', 'daimon shelton', 'fullback', 'sacramento state'], ['7', '20', '221', 'jon hesse', 'linebacker', 'nebraska']]

2011 british gt season

https://en.wikipedia.org/wiki/2011_British_GT_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-30062172-3.html.csv

unique

round five was the only time that chris goodwin and andrew kirkaldy had the pole position .

{'scope': 'all', 'row': '10', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'chris goodwin andrew kirkaldy', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'pole position', 'chris goodwin andrew kirkaldy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose pole position record fuzzily matches to chris goodwin andrew kirkaldy .', 'tostr': 'filter_eq { all_rows ; pole position ; chris goodwin andrew kirkaldy }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; pole position ; chris goodwin andrew kirkaldy } }', 'tointer': 'select the rows whose pole position record fuzzily matches to chris goodwin andrew kirkaldy . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'pole position', 'chris goodwin andrew kirkaldy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose pole position record fuzzily matches to chris goodwin andrew kirkaldy .', 'tostr': 'filter_eq { all_rows ; pole position ; chris goodwin andrew kirkaldy }'}, 'round'], 'result': '5', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; pole position ; chris goodwin andrew kirkaldy } ; round }'}, '5'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; pole position ; chris goodwin andrew kirkaldy } ; round } ; 5 }', 'tointer': 'the round record of this unqiue row is 5 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; pole position ; chris goodwin andrew kirkaldy } } ; eq { hop { filter_eq { all_rows ; pole position ; chris goodwin andrew kirkaldy } ; round } ; 5 } } = true', 'tointer': 'select the rows whose pole position record fuzzily matches to chris goodwin andrew kirkaldy . there is only one such row in the table . the round record of this unqiue row is 5 .'}

and { only { filter_eq { all_rows ; pole position ; chris goodwin andrew kirkaldy } } ; eq { hop { filter_eq { all_rows ; pole position ; chris goodwin andrew kirkaldy } ; round } ; 5 } } = true

select the rows whose pole position record fuzzily matches to chris goodwin andrew kirkaldy . there is only one such row in the table . the round record of this unqiue row is 5 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'pole position_7': 7, 'chris goodwin andrew kirkaldy_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'round_9': 9, '5_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'pole position_7': 'pole position', 'chris goodwin andrew kirkaldy_8': 'chris goodwin andrew kirkaldy', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'round_9': 'round', '5_10': '5'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'pole position_7': [0], 'chris goodwin andrew kirkaldy_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'round_9': [2], '5_10': [3]}

['round', 'circuit', 'date', 'length', 'pole position', 'gt3 winner', 'gt4 winner']

[['1', 'oulton park', '25 april', '60 mins', 'no 5 scuderia vittoria', 'no 1 trackspeed', 'no 44 abg motorsport'], ['1', 'oulton park', '25 april', '60 mins', 'charles bateman michael lyons', 'david ashburn richard westbrook', 'peter belshaw marcus clutton'], ['2', 'oulton park', '25 april', '60 mins', 'no 1 trackspeed', 'no 5 scuderia vittoria', 'no 42 century motorsport'], ['2', 'oulton park', '25 april', '60 mins', 'david ashburn richard westbrook', 'charles bateman michael lyons', 'jake rattenbury josh wakefield'], ['3', 'snetterton 300', '15 may', '120 mins', 'no 10 crs racing', 'no 23 united autosports', 'no 44 abg motorsport'], ['3', 'snetterton 300', '15 may', '120 mins', 'glynn geddie jim geddie', 'matt bell michael guasch', 'peter belshaw marcus clutton'], ['4', 'brands hatch', '19 june', '120 mins', 'no 21 mtech', 'no 2 trackspeed', 'no 44 abg motorsport'], ['4', 'brands hatch', '19 june', '120 mins', 'duncan cameron matt griffin', 'tim bridgman gregor fisken', 'peter belshaw marcus clutton'], ['5', 'spa - francorchamps', '9 july', '60 mins', 'no 59 mclaren gt', 'no 1 trackspeed', 'no 50 scuderia vittoria'], ['5', 'spa - francorchamps', '9 july', '60 mins', 'chris goodwin andrew kirkaldy', 'david ashburn richard westbrook', 'dan denis david mcdonald'], ['6', 'spa - francorchamps', '9 july', '60 mins', 'no 1 trackspeed', 'no 21 mtech', 'no 48 lotus sport uk'], ['6', 'spa - francorchamps', '9 july', '60 mins', 'david ashburn richard westbrook', 'duncan cameron matt griffin', 'phil glew ollie jackson'], ['7', 'rockingham', '4 september', '60 mins', 'no 23 united autosports', 'no 7 beechdean motorsport', 'no 50 scuderia vittoria'], ['7', 'rockingham', '4 september', '60 mins', 'matt bell michael guasch', 'jonathan adam andrew howard', 'dan denis david mcdonald'], ['8', 'rockingham', '4 september', '60 mins', 'no 2 trackspeed', 'no 11 crs racing', 'no 50 scuderia vittoria'], ['8', 'rockingham', '4 september', '60 mins', 'tim bridgman gregor fisken', 'alex mortimer andrew tate', 'dan denis david mcdonald'], ['9', 'donington park', '25 september', '180 mins', 'no 1 trackspeed', 'no 5 scuderia vittoria', 'no 48 lotus sport uk'], ['9', 'donington park', '25 september', '180 mins', 'david ashburn stephen jelley', 'charles bateman michael lyons', 'phil glew james nash'], ['10', 'silverstone arena', '8 october', '120 mins', 'no 7 beechdean motorsport', 'no 7 beechdean motorsport', 'no 48 lotus sport uk']]

henlopen conference

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13054553-5.html.csv

aggregation

the teams in the henlopen conference have a combined total overall record of 31-31 .

{'scope': 'all', 'col': '4', 'type': 'sum', 'result': '31-31', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'overall record'], 'result': '31-31', 'ind': 0, 'tostr': 'sum { all_rows ; overall record }'}, '31-31'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; overall record } ; 31-31 } = true', 'tointer': 'the sum of the overall record record of all rows is 31-31 .'}

round_eq { sum { all_rows ; overall record } ; 31-31 } = true

the sum of the overall record record of all rows is 31-31 .

2

2

{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'overall record_4': 4, '31-31_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'overall record_4': 'overall record', '31-31_5': '31-31'}

{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'overall record_4': [0], '31-31_5': [1]}

['school', 'team', 'division record', 'overall record', 'season outcome']

[['dover', 'senators', '5 - 0', '8 - 3', 'loss in first round of div i playoffs'], ['caesar rodney', 'riders', '3 - 2', '4 - 6', 'failed to make playoffs'], ['sussex central', 'golden knights', '2 - 3', '6 - 5', 'loss in first round of div i playoffs'], ['sussex tech', 'ravens', '2 - 3', '5 - 5', 'failed to make playoffs'], ['cape henlopen', 'vikings', '2 - 3', '4 - 6', 'failed to make playoffs'], ['milford', 'buccaneers', '1 - 4', '4 - 6', 'failed to make playoffs']]

the new adventures of old christine ( season 1 )

https://en.wikipedia.org/wiki/The_New_Adventures_of_Old_Christine_%28season_1%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27910411-1.html.csv

count

adam barr was the writer or co-writer for three episodes .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'adam barr', 'result': '3', 'col': '4', 'subset': None}

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

eq { count { filter_eq { all_rows ; written by ; adam barr } } ; 3 } = true

select the rows whose written by record fuzzily matches to adam barr . 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, 'written by_5': 5, 'adam barr_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', 'written by_5': 'written by', 'adam barr_6': 'adam barr', '3_7': '3'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'written by_5': [0], 'adam barr_6': [0], '3_7': [2]}

['no in series', 'title', 'directed by', 'written by', 'original air date', 'us viewers ( millions )']

[['1', 'pilot', 'andy ackerman', 'kari lizer', 'march 13 , 2006', '12.36'], ['2', 'supertramp', 'andy ackerman', 'teleplay : jeff astrof story : kari lizer', 'march 13 , 2006', '15.09'], ['3', 'open water', 'andy ackerman', 'adam barr', 'march 20 , 2006', '15.13'], ['4', 'one toe over the line , sweet jesus', 'andy ackerman', 'adam barr', 'march 27 , 2006', '11.96'], ['5', "i 'll show you mine", 'andy ackerman', 'steve baldikoski & bryan behar', 'april 3 , 2006', '8.28'], ['6', 'the other f word', 'andy ackerman', 'jeff astrof', 'april 10 , 2006', '11.42'], ['7', 'long days journey into stan', 'andy ackerman', 'danielle evenson', 'april 17 , 2006', '11.38'], ['8', 'teach your children well', 'andy ackerman', 'katie palmer', 'april 24 , 2006', '11.81'], ['9', 'ritchie has two mommies', 'andy ackerman', 'kari lizer', 'may 1 , 2006', '11.92'], ['10', 'no fault divorce', 'andy ackerman', 'jeff astrof & adam barr and kari lizer', 'may 8 , 2006', '11.87']]

mañana es para siempre

https://en.wikipedia.org/wiki/Ma%C3%B1ana_es_para_siempre

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18498743-1.html.csv

ordinal

croatia is the second newest country to begin showing episodes of mañana es para siempre .

{'row': '5', 'col': '5', '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', 'june 14 , 2009', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; june 14 , 2009 ; 2 }'}, 'mexico'], 'result': 'croatia', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; june 14 , 2009 ; 2 } ; mexico }'}, 'croatia'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; june 14 , 2009 ; 2 } ; mexico } ; croatia } = true', 'tointer': 'select the row whose june 14 , 2009 record of all rows is 2nd maximum . the mexico record of this row is croatia .'}

eq { hop { nth_argmax { all_rows ; june 14 , 2009 ; 2 } ; mexico } ; croatia } = true

select the row whose june 14 , 2009 record of all rows is 2nd maximum . the mexico record of this row is croatia .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'june 14 , 2009_5': 5, '2_6': 6, 'mexico_7': 7, 'croatia_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', 'june 14 , 2009_5': 'june 14 , 2009', '2_6': '2', 'mexico_7': 'mexico', 'croatia_8': 'croatia'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'june 14 , 2009_5': [0], '2_6': [0], 'mexico_7': [1], 'croatia_8': [2]}

['mexico', 'mañana es para siempre', 'el canal de las estrellas', 'october 20 , 2008', 'june 14 , 2009', 'monday to friday']

[['argentina', 'mañana es para siempre', 'canal 9', 'november 10 , 2011', 'march 16 , 2012', 'monday to friday'], ['bulgaria', 'утре и завинаги', 'diema family', 'january 11 , 2010', 'april 30 , 2010', 'monday to friday'], ['bosnia and herzegovina', 'ljubav je večna', 'pink bh', 'december 3 , 2009', 'may 29 , 2010', 'monday to saturday'], ['croatia', 'odavde do vječnosti', 'nova tv', 'february 1 , 2010', 'june 10 , 2010', 'monday to friday'], ['croatia', 'odavde do vječnosti', 'doma tv', 'june 16 , 2011', 'october 30 , 2011', 'monday to friday'], ['estonia', 'igavene homne', 'tv3', 'march 30 , 2010', 'november 8 , 2010', 'monday to friday'], ['hungary', 'mindörökké szerelem', 'rtl klub', 'november 15 , 2010', 'july 8 , 2011', 'monday to friday'], ['macedonia', 'љубовта е вечна', 'sitel tv', '2009', '2009', 'monday to friday'], ['lithuania', 'amžinai tavo', 'lnk', 'march , 2009', 'october 30 , 2009', 'monday to friday'], ['montenegro', 'ljubav je večna', 'pink m', 'august 10 , 2009', 'february 23 , 2010', 'monday to friday'], ['romania', 'impreuna pentru totdeauna', 'acasă', 'march 29 , 2010', 'september 4 , 2010', 'monday to friday'], ['serbia', 'ljubav je večna', 'rtv pink', 'june 5 , 2009', 'january 29 , 2010', 'monday to friday'], ['slovakia', 'love never dies', 'joj plus', 'december 21 , 2009', 'april , 2010', 'monday to friday'], ['slovenia', 'jutri je za večno', 'pop tv', 'september 25 , 2009', 'may 10 , 2010', 'monday to friday'], ['usa', 'mañana es para siempre', 'univision', 'february 23 , 2009', 'october 5 , 2009', 'monday to friday']]

arkansas highway 60

https://en.wikipedia.org/wiki/Arkansas_Highway_60

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18506777-1.html.csv

majority

most of the listed arkansas highway 60 locations are in the perry county .

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

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

most_eq { all_rows ; county ; perry } = true

for the county records of all rows , most of them fuzzily match to perry .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'county_3': 3, 'perry_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'county_3': 'county', 'perry_4': 'perry'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'county_3': [0], 'perry_4': [0]}

['county', 'location', 'distance', 'total', 'notes']

[['faulkner', 'conway', '0.0', '0.0', 'eastern terminus'], ['faulkner', 'conway', '1.5', '1.5', '1.8 mile spur to office of emergency services'], ['line', 'county line', '5.5', '7.0', 'toad suck ferry lock & dam'], ['perry', 'bigelow', '7.7', '14.7', 'converge with ar 113'], ['perry', 'houston', '3.8', '18.5', 'north end terminus of ar 216'], ['perry', 'houston', '0.1', '18.6', 'diverge with ar 113'], ['perry', 'perryville', '6.5', '25.1', 'converge with ar 9 & ar 10'], ['perry', 'perryville', '0.3', '25.4', 'diverge with ar 9 & ar 10'], ['perry', 'aplin', '10.8', '36.2', 'north end terminus of ar 155'], ['perry', 'fourche junction', '10.1', '46.3', 'cross ar 7'], ['line', 'county line', '1.6', '47.9', 'county line'], ['yell', 'plainview', '7.0', '54.9', 'western terminus']]

list of csi : ny characters

https://en.wikipedia.org/wiki/List_of_CSI%3A_NY_characters

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11240028-1.html.csv

comparative

on csi : ny , mac taylor appeared in more episodes than jo danville .

{'row_1': '1', 'row_2': '2', 'col': '6', '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', 'character', 'mac taylor csi detective'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose character record fuzzily matches to mac taylor csi detective .', 'tostr': 'filter_eq { all_rows ; character ; mac taylor csi detective }'}, 'episodes'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; character ; mac taylor csi detective } ; episodes }', 'tointer': 'select the rows whose character record fuzzily matches to mac taylor csi detective . take the episodes record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'character', 'jo danville csi detective'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose character record fuzzily matches to jo danville csi detective .', 'tostr': 'filter_eq { all_rows ; character ; jo danville csi detective }'}, 'episodes'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; character ; jo danville csi detective } ; episodes }', 'tointer': 'select the rows whose character record fuzzily matches to jo danville csi detective . take the episodes record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; character ; mac taylor csi detective } ; episodes } ; hop { filter_eq { all_rows ; character ; jo danville csi detective } ; episodes } } = true', 'tointer': 'select the rows whose character record fuzzily matches to mac taylor csi detective . take the episodes record of this row . select the rows whose character record fuzzily matches to jo danville csi detective . take the episodes record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; character ; mac taylor csi detective } ; episodes } ; hop { filter_eq { all_rows ; character ; jo danville csi detective } ; episodes } } = true

select the rows whose character record fuzzily matches to mac taylor csi detective . take the episodes record of this row . select the rows whose character record fuzzily matches to jo danville csi detective . take the episodes 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, 'character_7': 7, 'mac taylor csi detective_8': 8, 'episodes_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'character_11': 11, 'jo danville csi detective_12': 12, 'episodes_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', 'character_7': 'character', 'mac taylor csi detective_8': 'mac taylor csi detective', 'episodes_9': 'episodes', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'character_11': 'character', 'jo danville csi detective_12': 'jo danville csi detective', 'episodes_13': 'episodes'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'character_7': [0], 'mac taylor csi detective_8': [0], 'episodes_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'character_11': [1], 'jo danville csi detective_12': [1], 'episodes_13': [3]}

['character', 'portrayed by', 'first appearance', 'last appearance', 'duration', 'episodes']

[['mac taylor csi detective', 'gary sinise', 'blink 1 , 2 , 3', 'today is life', '1.01 - 9.17', '197'], ['jo danville csi detective', 'sela ward', 'the 34th floor', 'today is life', '7.01 - 9.17', '57'], ['danny messer csi detective', 'carmine giovinazzo', 'blink 1', 'today is life', '1.01 - 9.17', '197'], ['lindsay monroe messer csi detective', 'anna belknap', 'zoo york', 'today is life', '2.03 - 9.17', '172 4'], ['dr sid hammerback chief medical examiner', 'robert joy', 'dancing with the fishes', 'today is life', '2.05 - 9.17', '168 4'], ['adam ross lab technician', 'a j buckley', 'bad beat', 'today is life', '2.08 - 9.17', '141 4'], ['dr sheldon hawkes csi', 'hill harper', 'blink 1', 'today is life', '1.01 - 9.17', '197'], ['don flack homicide detective', 'eddie cahill', 'blink', 'today is life', '1.01 - 9.17', '197'], ['aiden burn csi detective', 'vanessa ferlito', 'blink 1', 'heroes', '1.01 - 2.02 , 2.23', '26']]

texas 's 5th congressional district

https://en.wikipedia.org/wiki/Texas%27s_5th_congressional_district

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1140249-1.html.csv

unique

george washington jones was the only greenback to have served the 5th district .

{'scope': 'all', 'row': '4', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'greenback', 'subset': None}

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

and { only { filter_eq { all_rows ; party ; greenback } } ; eq { hop { filter_eq { all_rows ; party ; greenback } ; name } ; george washington jones } } = true

select the rows whose party record fuzzily matches to greenback . there is only one such row in the table . the name record of this unqiue row is george washington jones .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'party_7': 7, 'greenback_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'george washington jones_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'party_7': 'party', 'greenback_8': 'greenback', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'george washington jones_10': 'george washington jones'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'party_7': [0], 'greenback_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'george washington jones_10': [3]}

['name', 'took office', 'left office', 'party', 'district residence']

[['district created march 4 , 1875', 'district created march 4 , 1875', 'district created march 4 , 1875', 'district created march 4 , 1875', 'district created march 4 , 1875'], ['john hancock', 'march 4 , 1875', 'march 3 , 1877', 'democrat', 'austin'], ['dewitt clinton giddings', 'march 4 , 1877', 'march 3 , 1879', 'democrat', 'brenham'], ['george washington jones', 'march 4 , 1879', 'march 3 , 1883', 'greenback', 'bastrop'], ['james w throckmorton', 'march 4 , 1883', 'march 3 , 1887', 'democrat', 'mckinney'], ['silas hare', 'march 4 , 1887', 'march 3 , 1891', 'democrat', 'sherman'], ['joseph w bailey', 'march 4 , 1891', 'march 3 , 1901', 'democrat', 'gainesville'], ['choice b randell', 'march 4 , 1901', 'march 3 , 1903', 'democrat', 'sherman'], ['james andrew beall', 'march 4 , 1903', 'march 3 , 1915', 'democrat', 'waxahachie'], ['hatton w sumners', 'march 4 , 1915', 'january 3 , 1947', 'democrat', 'dallas'], ['joseph franklin wilson', 'january 3 , 1947', 'january 3 , 1955', 'democrat', 'dallas'], ['bruce reynolds alger', 'january 3 , 1955', 'january 3 , 1965', 'republican', 'dallas'], ['earle cabell', 'january 3 , 1965', 'january 3 , 1973', 'democrat', 'dallas'], ['alan steelman', 'january 3 , 1973', 'january 3 , 1977', 'republican', 'dallas'], ['jim mattox', 'january 3 , 1977', 'january 3 , 1983', 'democrat', 'dallas'], ['john w bryant', 'january 3 , 1983', 'january 3 , 1997', 'democrat', 'dallas'], ['pete sessions', 'january 3 , 1997', 'january 3 , 2003', 'republican', 'dallas'], ['jeb hensarling', 'january 3 , 2003', 'present', 'republican', 'dallas']]

wyfk

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14369924-1.html.csv

comparative

call sign w269ax broadcasts on a higher frequency than call sign w230av for wyfk radio .

{'row_1': '2', 'row_2': '1', 'col': '2', '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', 'call sign', 'w269ax'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose call sign record fuzzily matches to w269ax .', 'tostr': 'filter_eq { all_rows ; call sign ; w269ax }'}, 'frequency mhz'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; call sign ; w269ax } ; frequency mhz }', 'tointer': 'select the rows whose call sign record fuzzily matches to w269ax . take the frequency mhz record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'call sign', 'w230av'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose call sign record fuzzily matches to w230av .', 'tostr': 'filter_eq { all_rows ; call sign ; w230av }'}, 'frequency mhz'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; call sign ; w230av } ; frequency mhz }', 'tointer': 'select the rows whose call sign record fuzzily matches to w230av . take the frequency mhz record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; call sign ; w269ax } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; w230av } ; frequency mhz } } = true', 'tointer': 'select the rows whose call sign record fuzzily matches to w269ax . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to w230av . take the frequency mhz record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; call sign ; w269ax } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; w230av } ; frequency mhz } } = true

select the rows whose call sign record fuzzily matches to w269ax . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to w230av . take the frequency mhz 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, 'call sign_7': 7, 'w269ax_8': 8, 'frequency mhz_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'call sign_11': 11, 'w230av_12': 12, 'frequency mhz_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', 'call sign_7': 'call sign', 'w269ax_8': 'w269ax', 'frequency mhz_9': 'frequency mhz', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'call sign_11': 'call sign', 'w230av_12': 'w230av', 'frequency mhz_13': 'frequency mhz'}

{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'call sign_7': [0], 'w269ax_8': [0], 'frequency mhz_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'call sign_11': [1], 'w230av_12': [1], 'frequency mhz_13': [3]}

['call sign', 'frequency mhz', 'city of license', 'erp w', 'class', 'fcc info']

[['w230av', '93.9 fm', 'gadsden , alabama', '10', 'd', 'fcc'], ['w269ax', '101.7 fm', 'anniston , alabama', '10', 'd', 'fcc'], ['w273ae', '102.5 fm', 'albany , georgia', '55', 'd', 'fcc'], ['w282ae', '104.3 fm', 'macon , georgia', '27', 'd', 'fcc'], ['w290ag', '105.9 fm', 'griffin , georgia', '27', 'd', 'fcc']]

list of highest mountain peaks in washington

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19716903-1.html.csv

superlative

mount rainier has the highest isolation of all the highest mountain peaks in washington .

{'scope': 'all', 'col_superlative': '6', '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', 'isolation'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; isolation }'}, 'mountain peak'], 'result': 'mount rainier', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; isolation } ; mountain peak }'}, 'mount rainier'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; isolation } ; mountain peak } ; mount rainier } = true', 'tointer': 'select the row whose isolation record of all rows is maximum . the mountain peak record of this row is mount rainier .'}

eq { hop { argmax { all_rows ; isolation } ; mountain peak } ; mount rainier } = true

select the row whose isolation record of all rows is maximum . the mountain peak record of this row is mount rainier .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'isolation_5': 5, 'mountain peak_6': 6, 'mount rainier_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'isolation_5': 'isolation', 'mountain peak_6': 'mountain peak', 'mount rainier_7': 'mount rainier'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'isolation_5': [0], 'mountain peak_6': [1], 'mount rainier_7': [2]}

['rank', 'mountain peak', 'mountain range', 'elevation', 'prominence', 'isolation']

[['1', 'mount rainier', 'cascade range', '4393.293 = 14411feet 4392 m', '4027.439 = 13211feet 4027 m', '01175.46 = 730.4 miles 1175.5 km'], ['2', 'mount adams', 'cascade range', '3742.988 = 12277feet 3743 m', '2474.390 = 8116feet 2474 m', '00075.14 = 46.7 miles 75.1 km'], ['3', 'mount baker', 'cascade range', '3285.976 = 10778feet 3286 m', '2706.707 = 8878feet 2706 m', '00213.71 = 132.8 miles 213.7 km'], ['4', 'glacier peak', 'cascade range', '3213.720 = 10541feet 3286 m', '2292.378 = 7519feet 2292 m', '00090.18 = 56.0 miles 90.2 km'], ['5', 'bonanza peak', 'cascade range', '2899.695 = 9511feet 2899 m', '1131.402 = 3711feet 1131 m', '00023.04 = 14.4 miles 23.2 km'], ['6', 'mount stuart', 'cascade range', '2870.427 = 9415feet 2870 m', '1625.524 = 5335feet 1626 m', '00072.00 = 45.0 miles 72.0 km'], ['7', 'mount fernow', 'cascade range', '2819.817 = 9249feet 2819 m', '0857.012 = 2811feet 857 m', '00009.44 = 5.9 miles 9.5 km'], ['8', 'goode mountain', 'cascade range', '2804.878 = 9200feet 2810 m', '0857.012 = 3808feet 1161 m', '00027.20 = 17.0 miles 27.2 km'], ['9', 'mount shuksan', 'cascade range', '2782.622 = 9127feet 2782 m', '1340.549 = 4397feet 1340 m', '00016.64 = 10.4 miles 16.7 km'], ['10', 'buckner mountain', 'cascade range', '2778.659 = 9114feet 2778 m', '0925.000 = 3034feet 925 m', '00006.61 = 4.1 miles 6.6 km'], ['11', 'jack mountain', 'cascade range', '2764.024 = 9066feet 2763 m', '1276.220 = 4186feet 1276 m', '00026.00 = 16.3 miles 26.0 km'], ['12', 'mount spickard', 'cascade range', '2737.500 = 8979feet 2738 m', '1457.021 = 4779feet 1457 m', '00036.46 = 19.0 miles 30.5 km'], ['13', 'black peak', 'cascade range', '2734.756 = 8970feet 2734 m', '1051.829 = 3450feet 1051 m', '00008.16 = 5.1 miles 8.2 km'], ['14', 'mount redoubt', 'cascade range', '2730.488 = 8956feet 2730 m', '0502.744 = 1649feet 503 m', '00004.56 = 2.9 miles 4.6 km'], ['15', 'north gardner mountain', 'cascade range', '2730.488 = 8956feet 2730 m', '1218.293 = 3996feet 1218 m', '00043.68 = 27.3 miles 43.7 km'], ['16', 'dome peak', 'cascade range', '2719.512 = 8920feet 2719 m', '0926.829 = 3040feet 927 m', '00043.68 = 27.3 miles 43.7 km'], ['17', 'silver star mountain', 'cascade range', '2705.793 = 8875feet 2705 m', '0740.854 = 2430feet 742 m', '00007.10 = 4.4 miles 7.1 km']]

shaun murphy ( snooker player )

https://en.wikipedia.org/wiki/Shaun_Murphy_%28snooker_player%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1795208-5.html.csv

count

there were 2 appearances in the world snooker championship .

{'scope': 'all', 'criterion': 'equal', 'value': 'world snooker championship', 'result': '2', 'col': '3', 'subset': None}

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

eq { count { filter_eq { all_rows ; championship ; world snooker championship } } ; 2 } = true

select the rows whose championship record fuzzily matches to world snooker championship . 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, 'championship_5': 5, 'world snooker championship_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', 'championship_5': 'championship', 'world snooker championship_6': 'world snooker championship', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'championship_5': [0], 'world snooker championship_6': [0], '2_7': [2]}

['outcome', 'year', 'championship', 'opponent in the final', 'score']

[['winner', '2005', 'world snooker championship', 'matthew stevens', '18 - 16'], ['runner - up', '2006', 'welsh open', 'stephen lee', '4 - 9'], ['winner', '2007', 'malta cup', 'ryan day', '9 - 4'], ['runner - up', '2008', 'china open', 'stephen maguire', '9 - 10'], ['winner', '2008', 'uk championship', 'marco fu', '10 - 9'], ['runner - up', '2009', 'world snooker championship', 'john higgins', '9 - 18'], ['winner', '2011', 'players tour championship - finals', 'martin gould', '4 - 0'], ['runner - up', '2012', 'uk championship', 'mark selby', '6 - 10']]

indianapolis colts draft history

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13312898-55.html.csv

count

the indianapolis colts drafted a total of three players from ohio state college .

{'scope': 'all', 'criterion': 'equal', 'value': 'ohio state', 'result': '3', 'col': '6', 'subset': None}

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

eq { count { filter_eq { all_rows ; college ; ohio state } } ; 3 } = true

select the rows whose college record fuzzily matches to ohio state . 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, 'college_5': 5, 'ohio state_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', 'college_5': 'college', 'ohio state_6': 'ohio state', '3_7': '3'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'college_5': [0], 'ohio state_6': [0], '3_7': [2]}

['round', 'pick', 'overall', 'name', 'position', 'college']

[['1', '32', '32', 'anthony gonzalez', 'wide receiver', 'ohio state'], ['2', '10', '42', 'tony ugoh', 'offensive tackle', 'arkansas'], ['3', '31', '95', 'daymeion hughes', 'cornerback', 'california'], ['3', '34', '98', 'quinn pitcock', 'defensive tackle', 'ohio state'], ['4', '32', '131', 'brannon condren', 'safety', 'troy'], ['4', '37', '136', 'clint session', 'linebacker', 'pittsburgh'], ['5', '32', '169', 'roy hall', 'wide receiver', 'ohio state'], ['5', '36', '173', 'michael coe', 'cornerback', 'alabama state'], ['7', '32', '232', 'keyunta dawson', 'linebacker', 'texas tech']]

shweta mohan

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18550192-2.html.csv

comparative

nee maatalo was released before the song hey po was released .

{'row_1': '4', 'row_2': '8', 'col': '1', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'song title', 'nee maatalo'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose song title record fuzzily matches to nee maatalo .', 'tostr': 'filter_eq { all_rows ; song title ; nee maatalo }'}, 'year'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; song title ; nee maatalo } ; year }', 'tointer': 'select the rows whose song title record fuzzily matches to nee maatalo . take the year record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'song title', 'hey po'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose song title record fuzzily matches to hey po .', 'tostr': 'filter_eq { all_rows ; song title ; hey po }'}, 'year'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; song title ; hey po } ; year }', 'tointer': 'select the rows whose song title record fuzzily matches to hey po . take the year record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; song title ; nee maatalo } ; year } ; hop { filter_eq { all_rows ; song title ; hey po } ; year } } = true', 'tointer': 'select the rows whose song title record fuzzily matches to nee maatalo . take the year record of this row . select the rows whose song title record fuzzily matches to hey po . take the year record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; song title ; nee maatalo } ; year } ; hop { filter_eq { all_rows ; song title ; hey po } ; year } } = true

select the rows whose song title record fuzzily matches to nee maatalo . take the year record of this row . select the rows whose song title record fuzzily matches to hey po . take the year record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'song title_7': 7, 'nee maatalo_8': 8, 'year_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'song title_11': 11, 'hey po_12': 12, 'year_13': 13}

{'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'song title_7': 'song title', 'nee maatalo_8': 'nee maatalo', 'year_9': 'year', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'song title_11': 'song title', 'hey po_12': 'hey po', 'year_13': 'year'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'song title_7': [0], 'nee maatalo_8': [0], 'year_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'song title_11': [1], 'hey po_12': [1], 'year_13': [3]}

['year', 'song title', 'film', 'co - singer', 'music - director']

[['2010', 'amma thale', 'puli ( soundtrack )', 'naresh iyer', 'a r rahman'], ['2010', 'piliche', 'khaleja', 'hemachandra', 'mani sharma'], ['2010', 'boom boom robo ra', 'enthiran', 'tanvi shah', 'a r rahman'], ['2011', 'nee maatalo', '180 ( 2011 indian film )', 'karthik', 'sharreth'], ['2012', 'dil se', 'gabbar singh ( film )', 'hemachandra', 'devi sri prasad'], ['2012', 'aagipo', 'ko antey koti', 'karthik , chinnaponnu', 'shakti kanth'], ['2012', 'kaatuka kallu', 'sarocharu', 'khushi murali', 'devi sri prasad'], ['2013', 'hey po', 'okkadine', 'solo', 'karthik'], ['2013', 'neetho edo', 'paisa ( film )', 'solo', 'sai karthik'], ['2013', 'nemmadiga', 'bhai ( 2013 film )', 'venu srirangam', 'devi sri prasad']]

1989 cleveland browns season

https://en.wikipedia.org/wiki/1989_Cleveland_Browns_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10650760-1.html.csv

aggregation

during the 1989 cleveland browns season , the average game attendance was about 68,559 .

{'scope': 'all', 'col': '5', 'type': 'average', 'result': '68,559', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '68,559', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '68,559'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 68,559 } = true', 'tointer': 'the average of the attendance record of all rows is 68,559 .'}

round_eq { avg { all_rows ; attendance } ; 68,559 } = true

the average of the attendance record of all rows is 68,559 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '68,559_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '68,559_5': '68,559'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '68,559_5': [1]}

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

[['1', 'september 10 , 1989', 'pittsburgh steelers', 'w 51 - 0', '57928'], ['2', 'september 17 , 1989', 'new york jets', 'w 38 - 24', '73516'], ['3', 'september 25 , 1989', 'cincinnati bengals', 'l 21 - 14', '55996'], ['4', 'october 1 , 1989', 'denver broncos', 'w 16 - 13', '78637'], ['5', 'october 8 , 1989', 'miami dolphins', 'l 13 - 10', '58444'], ['6', 'october 15 , 1989', 'pittsburgh steelers', 'l 17 - 7', '78840'], ['7', 'october 23 , 1989', 'chicago bears', 'w 27 - 7', '78722'], ['8', 'october 29 , 1989', 'houston oilers', 'w 28 - 17', '78765'], ['9', 'november 5 , 1989', 'tampa bay buccaneers', 'w 42 - 31', '69162'], ['10', 'november 12 , 1989', 'seattle seahawks', 'w 17 - 7', '58978'], ['11', 'november 19 , 1989', 'kansas city chiefs', 't 10 - 10', '77922'], ['12', 'november 23 , 1989', 'detroit lions', 'l 13 - 10', '65624'], ['13', 'december 3 , 1989', 'cincinnati bengals', 'l 21 - 0', '76236'], ['14', 'december 10 , 1989', 'indianapolis colts', 'l 23 - 17', '58550'], ['15', 'december 17 , 1989', 'minnesota vikings', 'w 23 - 17', '70777'], ['16', 'december 23 , 1989', 'houston oilers', 'w 24 - 20', '58852']]

1972 nhl amateur draft

https://en.wikipedia.org/wiki/1972_NHL_Amateur_Draft

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1473672-1.html.csv

majority

all of the players selected in the top 16 picks of the 1972 nhl amateur draft were from canada .

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

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

all_eq { all_rows ; nationality ; canada } = true

for the nationality records of all rows , all of them fuzzily match to canada .

1

1

{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'nationality_3': 3, 'canada_4': 4}

{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'nationality_3': 'nationality', 'canada_4': 'canada'}

{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'nationality_3': [0], 'canada_4': [0]}

['pick', 'player', 'position', 'nationality', 'nhl team', 'college / junior / club team']

[['1', 'billy harris', 'right wing', 'canada', 'new york islanders', 'toronto marlboros ( ohl )'], ['2', 'jacques richard', 'left wing', 'canada', 'atlanta flames', 'quebec remparts ( qmjhl )'], ['3', 'don lever', 'centre', 'canada', 'vancouver canucks', 'niagara falls flyers ( omjhl )'], ['4', 'steve shutt', 'left wing', 'canada', 'montreal canadiens', 'toronto marlboros ( omjhl )'], ['5', 'jim schoenfeld', 'defence', 'canada', 'buffalo sabres', 'niagara falls flyers ( omjhl )'], ['6', 'michel larocque', 'goaltender', 'canada', 'montreal canadiens', "ottawa 67 's ( omjhl )"], ['7', 'bill barber', 'left wing', 'canada', 'philadelphia flyers', 'kitchener rangers ( omjhl )'], ['8', 'dave gardner', 'centre', 'canada', 'montreal canadiens', 'toronto marlboros ( omjhl )'], ['9', 'wayne merrick', 'centre', 'canada', 'st louis blues', "ottawa 67 's ( omjhl )"], ['10', 'al blanchard', 'left wing', 'canada', 'new york rangers', 'kitchener rangers ( omjhl )'], ['11', 'george ferguson', 'centre', 'canada', 'toronto maple leafs', 'toronto marlboros ( omjhl )'], ['12', 'jerry byers', 'left wing', 'canada', 'minnesota north stars', 'kitchener rangers ( omjhl )'], ['13', 'phil russell', 'defence', 'canada', 'chicago black hawks', 'edmonton oil kings ( wchl )'], ['14', 'john van boxmeer', 'defence', 'canada', 'montreal canadiens', "guelph cmc 's ( sojhl )"], ['15', 'bob macmillan', 'centre', 'canada', 'new york rangers', 'st catharines black hawks ( omjhl )'], ['16', 'mike bloom', 'left wing', 'canada', 'boston bruins', 'st catharines black hawks ( omjhl )']]

gulf coast athletic conference

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10577579-2.html.csv

aggregation

the average enrollment of all institutions in the gulf coast athletic conference is 1128.5 .

{'scope': 'all', 'col': '7', 'type': 'average', 'result': '1128.5', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'enrollment'], 'result': '1128.5', 'ind': 0, 'tostr': 'avg { all_rows ; enrollment }'}, '1128.5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; enrollment } ; 1128.5 } = true', 'tointer': 'the average of the enrollment record of all rows is 1128.5 .'}

round_eq { avg { all_rows ; enrollment } ; 1128.5 } = true

the average of the enrollment record of all rows is 1128.5 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'enrollment_4': 4, '1128.5_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'enrollment_4': 'enrollment', '1128.5_5': '1128.5'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'enrollment_4': [0], '1128.5_5': [1]}

['institution', 'location', 'mens nickname', 'womens nickname', 'founded', 'type', 'enrollment', 'joined']

[['dillard university', 'new orleans , louisiana', 'bleu devils', 'lady bleu devils', '1869', 'private / ( methodist & church of christ )', '900', '1981'], ['edward waters college', 'jacksonville , florida', 'tigers', 'lady tigers', '1866', 'private / ( african methodist )', '800', '2010'], ['fisk university', 'nashville , tennessee', 'bulldogs', 'lady bulldogs', '1866', 'private / ( church of christ )', '800', '2010'], ['philander smith college', 'little rock , arkansas', 'panthers', 'lady panthers', '1864', 'private / ( methodist )', '700', '2011'], ['southern university at new orleans', 'new orleans , louisiana', 'black knights', 'lady knights', '1956', 'public', '3200', '1986'], ['talladega college', 'talladega , alabama', 'tornadoes', 'lady tornadoes', '1867', 'private / ( united church of christ )', '600', '1999 , 2011'], ['tougaloo college', 'tougaloo , mississippi', 'bulldogs', 'lady bulldogs', '1869', 'private / ( church of christ )', '900', '1981']]

jak oni śpiewają

https://en.wikipedia.org/wiki/Jak_oni_%C5%9Bpiewaj%C4%85

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11680517-4.html.csv

unique

season 4 of jak oni śpiewają is the only season that had fifteen stars .

{'scope': 'all', 'row': '4', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': '15', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'of stars', '15'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose of stars record is equal to 15 .', 'tostr': 'filter_eq { all_rows ; of stars ; 15 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; of stars ; 15 } }', 'tointer': 'select the rows whose of stars record is equal to 15 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'of stars', '15'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose of stars record is equal to 15 .', 'tostr': 'filter_eq { all_rows ; of stars ; 15 }'}, 'season'], 'result': '4 - autumn 2008', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; of stars ; 15 } ; season }'}, '4 - autumn 2008'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; of stars ; 15 } ; season } ; 4 - autumn 2008 }', 'tointer': 'the season record of this unqiue row is 4 - autumn 2008 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; of stars ; 15 } } ; eq { hop { filter_eq { all_rows ; of stars ; 15 } ; season } ; 4 - autumn 2008 } } = true', 'tointer': 'select the rows whose of stars record is equal to 15 . there is only one such row in the table . the season record of this unqiue row is 4 - autumn 2008 .'}

and { only { filter_eq { all_rows ; of stars ; 15 } } ; eq { hop { filter_eq { all_rows ; of stars ; 15 } ; season } ; 4 - autumn 2008 } } = true

select the rows whose of stars record is equal to 15 . there is only one such row in the table . the season record of this unqiue row is 4 - autumn 2008 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'of stars_7': 7, '15_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'season_9': 9, '4 - autumn 2008_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'of stars_7': 'of stars', '15_8': '15', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'season_9': 'season', '4 - autumn 2008_10': '4 - autumn 2008'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'of stars_7': [0], '15_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'season_9': [2], '4 - autumn 2008_10': [3]}

['season', 'of stars', 'of weeks', 'season premiere date', 'season finale date', 'winner', 'runner - up', 'third place']

[['1 - spring 2007', '13', '13', 'march 16 , 2007', 'june 2 , 2007', 'agnieszka włodarczyk', 'natasza urbańska', 'robert moskwa'], ['2 - autumn 2007', '13', '13', 'september 8 , 2007', 'december 15 , 2007', 'joanna liszowska', 'piotr polk', 'patricia kazadi'], ['3 - spring 2008', '13', '13', 'march 8 , 2008', 'may 31 , 2008', 'krzysztof respondek', 'joanna jabłczyńska', 'kacper kuszewski'], ['4 - autumn 2008', '15', '13', 'september 6 , 2008', 'december 6 , 2008', 'artur chamski', 'karolina nowakowska', 'aleksandra szwed'], ['5 - spring 2009', '14', '12', 'march 7 , 2009', 'may 23 , 2009', 'laura samojłowicz', 'maciej jachowski', 'robert kudelski'], ['6 - autumn 2009', '11', '10', 'september 12 , 2009', 'november 23 , 2009', 'krzysztof respondek', 'agnieszka włodarczyk', 'artur chamski']]

1979 miami dolphins season

https://en.wikipedia.org/wiki/1979_Miami_Dolphins_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18847736-2.html.csv

ordinal

the miami dolphins scored the 2nd highest number of points during the game on sept 23 .

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

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'dolphins points', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; dolphins points ; 2 }'}, 'date'], 'result': 'sept 23', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; dolphins points ; 2 } ; date }'}, 'sept 23'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; dolphins points ; 2 } ; date } ; sept 23 } = true', 'tointer': 'select the row whose dolphins points record of all rows is 2nd maximum . the date record of this row is sept 23 .'}

eq { hop { nth_argmax { all_rows ; dolphins points ; 2 } ; date } ; sept 23 } = true

select the row whose dolphins points record of all rows is 2nd maximum . the date record of this row is sept 23 .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'dolphins points_5': 5, '2_6': 6, 'date_7': 7, 'sept 23_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', 'dolphins points_5': 'dolphins points', '2_6': '2', 'date_7': 'date', 'sept 23_8': 'sept 23'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'dolphins points_5': [0], '2_6': [0], 'date_7': [1], 'sept 23_8': [2]}

['game', 'date', 'opponent', 'result', 'dolphins points', 'opponents', 'record', 'attendance']

[['1', 'sept 2', 'buffalo bills', 'win', '9', '7', '1 - 0', '69441'], ['2', 'sept 9', 'seattle seahawks', 'win', '19', '10', '2 - 0', '56233'], ['3', 'sept 16', 'minnesota vikings', 'win', '27', '12', '3 - 0', '46187'], ['4', 'sept 23', 'chicago bears', 'win', '31', '16', '4 - 0', '66011'], ['5', 'sept 30', 'new york jets', 'loss', '27', '33', '4 - 1', '51496'], ['6', 'oct 8', 'oakland raiders', 'loss', '3', '13', '4 - 2', '52419'], ['7', 'oct 14', 'buffalo bills', 'win', '17', '7', '5 - 2', '45597'], ['8', 'oct 21', 'new england patriots', 'loss', '13', '28', '5 - 3', '61096'], ['9', 'oct 28', 'green bay packers', 'win', '27', '7', '6 - 3', '47741'], ['10', 'nov 5', 'houston oilers', 'loss', '6', '9', '6 - 4', '70273'], ['11', 'nov 11', 'baltimore colts', 'win', '19', '0', '7 - 4', '50193'], ['12', 'nov 18', 'cleveland browns', 'loss ( ot )', '24', '30', '7 - 5', '80374'], ['13', 'nov 25', 'baltimore colts', 'win', '28', '24', '8 - 5', '38016'], ['14', 'nov 29', 'new england patriots', 'win', '39', '24', '9 - 5', '69174'], ['15', 'dec 9', 'detroit lions', 'win', '28', '10', '10 - 5', '78087']]

1941 vfl season

https://en.wikipedia.org/wiki/1941_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10807673-18.html.csv

unique

in the 1941 vfl season , when the crowd was under 15000 , the only time the venue was lake oval was when the home team was south melbourne .

{'scope': 'subset', 'row': '3', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'lake oval', 'subset': {'col': '6', 'criterion': 'less_than', 'value': '15000'}}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'crowd', '15000'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; crowd ; 15000 }', 'tointer': 'select the rows whose crowd record is less than 15000 .'}, 'venue', 'lake oval'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose crowd record is less than 15000 . among these rows , select the rows whose venue record fuzzily matches to lake oval .', 'tostr': 'filter_eq { filter_less { all_rows ; crowd ; 15000 } ; venue ; lake oval }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_less { all_rows ; crowd ; 15000 } ; venue ; lake oval } }', 'tointer': 'select the rows whose crowd record is less than 15000 . among these rows , select the rows whose venue record fuzzily matches to lake oval . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'crowd', '15000'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; crowd ; 15000 }', 'tointer': 'select the rows whose crowd record is less than 15000 .'}, 'venue', 'lake oval'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose crowd record is less than 15000 . among these rows , select the rows whose venue record fuzzily matches to lake oval .', 'tostr': 'filter_eq { filter_less { all_rows ; crowd ; 15000 } ; venue ; lake oval }'}, 'home team'], 'result': 'south melbourne', 'ind': 3, 'tostr': 'hop { filter_eq { filter_less { all_rows ; crowd ; 15000 } ; venue ; lake oval } ; home team }'}, 'south melbourne'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_less { all_rows ; crowd ; 15000 } ; venue ; lake oval } ; home team } ; south melbourne }', 'tointer': 'the home team record of this unqiue row is south melbourne .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_less { all_rows ; crowd ; 15000 } ; venue ; lake oval } } ; eq { hop { filter_eq { filter_less { all_rows ; crowd ; 15000 } ; venue ; lake oval } ; home team } ; south melbourne } } = true', 'tointer': 'select the rows whose crowd record is less than 15000 . among these rows , select the rows whose venue record fuzzily matches to lake oval . there is only one such row in the table . the home team record of this unqiue row is south melbourne .'}

and { only { filter_eq { filter_less { all_rows ; crowd ; 15000 } ; venue ; lake oval } } ; eq { hop { filter_eq { filter_less { all_rows ; crowd ; 15000 } ; venue ; lake oval } ; home team } ; south melbourne } } = true

select the rows whose crowd record is less than 15000 . among these rows , select the rows whose venue record fuzzily matches to lake oval . there is only one such row in the table . the home team record of this unqiue row is south melbourne .

8

6

{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_less_0': 0, 'all_rows_7': 7, 'crowd_8': 8, '15000_9': 9, 'venue_10': 10, 'lake oval_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'home team_12': 12, 'south melbourne_13': 13}

{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_less_0': 'filter_less', 'all_rows_7': 'all_rows', 'crowd_8': 'crowd', '15000_9': '15000', 'venue_10': 'venue', 'lake oval_11': 'lake oval', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'home team_12': 'home team', 'south melbourne_13': 'south melbourne'}

{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_less_0': [1], 'all_rows_7': [0], 'crowd_8': [0], '15000_9': [0], 'venue_10': [1], 'lake oval_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'home team_12': [3], 'south melbourne_13': [4]}

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

[['footscray', '16.14 ( 110 )', 'st kilda', '13.17 ( 95 )', 'western oval', '7000', '30 august 1941'], ['carlton', '16.17 ( 113 )', 'melbourne', '11.22 ( 88 )', 'princes park', '29000', '30 august 1941'], ['south melbourne', '7.12 ( 54 )', 'hawthorn', '11.17 ( 83 )', 'lake oval', '3000', '30 august 1941'], ['richmond', '20.12 ( 132 )', 'geelong', '12.11 ( 83 )', 'punt road oval', '9000', '30 august 1941'], ['fitzroy', '15.16 ( 106 )', 'essendon', '18.14 ( 122 )', 'brunswick street oval', '11000', '30 august 1941'], ['north melbourne', '12.8 ( 80 )', 'collingwood', '13.20 ( 98 )', 'arden street oval', '5000', '30 august 1941']]

comparison of amd chipsets

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12341355-5.html.csv

ordinal

the sb700 codename series was the second earliest released amd chipset .

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

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'released', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; released ; 2 }'}, 'codename'], 'result': 'sb700', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; released ; 2 } ; codename }'}, 'sb700'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; released ; 2 } ; codename } ; sb700 } = true', 'tointer': 'select the row whose released record of all rows is 2nd minimum . the codename record of this row is sb700 .'}

eq { hop { nth_argmin { all_rows ; released ; 2 } ; codename } ; sb700 } = true

select the row whose released record of all rows is 2nd minimum . the codename record of this row is sb700 .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'released_5': 5, '2_6': 6, 'codename_7': 7, 'sb700_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', 'released_5': 'released', '2_6': '2', 'codename_7': 'codename', 'sb700_8': 'sb700'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'released_5': [0], '2_6': [0], 'codename_7': [1], 'sb700_8': [2]}

['model', 'codename', 'released', 'fab ( nm )', 'sata', 'usb 2.0 + 1.1', 'usb 3.0', 'parallel ata 1', 'raid', 'gb ethernet mac', 'package', 'tdp ( w )']

[['amd 480 / 570 / 580 / 690 crossfire chipset', 'sb600', '2006', '130', '43 gbit / s ahci 1.1 sata revision 2.0', '10 + 0', 'no', '1 ata / 133', '0 , 1 , 10', 'no', '548 - pin fc - bga', '4.0'], ['amd 700 chipset series', 'sb700', 'q1 2008', '130', '63 gbit / s ahci1 .1 sata revision 2.0', '12 + 2', 'no', '1 ata / 133', '0 , 1 , 10', 'no', '548 - pin fc - bga', '4.5'], ['amd 700s chipset series', 'sb700s', 'q1 2008', '130', '63 gbit / s ahci1 .1 sata revision 2.0', '12 + 2', 'no', '1 ata / 133', '0 , 1 , 10', 'no', '548 - pin fc - bga', '4.5'], ['amd 700 chipset series', 'sb710', 'q4 2008', '130', '63 gbit / s ahci1 .1 sata revision 2.0', '12 + 2', 'no', '1 ata / 133', '0 , 1 , 10', 'no', '548 - pin fc - bga', '4.5'], ['amd 700 chipset series', 'sb750', 'q4 2008', '130', '63 gbit / s ahci1 .1 sata revision 2.0', '12 + 2', 'no', '1 ata / 133', '0 , 1 , 5 , 10', 'no', '548 - pin fc - bga', '4.5'], ['amd 800 chipset series', 'sb810', 'q1 2010', '65', '63 gbit / s ahci1 .2 sata revision 2.0', '14 + 2', 'no', 'no', '0 , 1 , 10', '10 / 100 / 1000', '605 - pin fc - bga', '6.0'], ['amd 800 chipset series', 'sb850', 'q1 2010', '65', '66 gbps ahci1 .2 sata revision 3.0', '14 + 2', 'no', 'no', '0 , 1 , 5 , 10', '10 / 100 / 1000', '605 - pin fc - bga', '6.0'], ['amd 900 chipset series', 'sb920', 'may 30 , 2011', '65', '66 gbps ahci1 .2 sata revision 3.0', '14 + 2', 'no', 'no', '0 , 1 , 10', '10 / 100 / 1000', '605 - pin fc - bga', '6.0'], ['amd 900 chipset series', 'sb950', 'may 30 , 2011', '65', '66 gbps ahci1 .2 sata revision 3.0', '14 + 2', 'no', 'no', '0 , 1 , 5 , 10', '10 / 100 / 1000', '605 - pin fc - bga', '6.0']]

2005 pga championship

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12512153-2.html.csv

ordinal

hal sutton had the highest total during the 2005 pga championship .

{'row': '7', 'col': '4', 'order': '1', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

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

eq { hop { nth_argmax { all_rows ; total ; 1 } ; player } ; hal sutton } = true

select the row whose total record of all rows is 1st maximum . the player record of this row is hal sutton .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'total_5': 5, '1_6': 6, 'player_7': 7, 'hal sutton_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'total_5': 'total', '1_6': '1', 'player_7': 'player', 'hal sutton_8': 'hal sutton'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'total_5': [0], '1_6': [0], 'player_7': [1], 'hal sutton_8': [2]}

['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish']

[['steve elkington', 'australia', '1995', '277', '- 3', 't2'], ['davis love iii', 'united states', '1997', '278', '- 2', 't4'], ['tiger woods', 'united states', '1999 , 2000', '278', '- 2', 't4'], ['vijay singh', 'fiji', '1998 , 2004', '280', 'e', 't10'], ['david toms', 'united states', '2001', '280', 'e', 't10'], ['john daly', 'united states', '1991', '292', '+ 12', 't74'], ['hal sutton', 'united states', '1983', '300', '+ 20', '79']]

2004 úrvalsdeild

https://en.wikipedia.org/wiki/2004_%C3%9Arvalsdeild

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11179106-1.html.csv

superlative

in the 2004 season of úrvalsdeild , the fh team ranks the highest .

{'scope': 'all', 'col_superlative': '8', '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', 'points'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; points }'}, 'team'], 'result': 'fh', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; points } ; team }'}, 'fh'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; points } ; team } ; fh } = true', 'tointer': 'select the row whose points record of all rows is maximum . the team record of this row is fh .'}

eq { hop { argmax { all_rows ; points } ; team } ; fh } = true

select the row whose points record of all rows is maximum . the team record of this row is fh .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'points_5': 5, 'team_6': 6, 'fh_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'points_5': 'points', 'team_6': 'team', 'fh_7': 'fh'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'points_5': [0], 'team_6': [1], 'fh_7': [2]}

['team', 'played', 'draw', 'lost', 'goals for', 'goals against', 'goal difference', 'points']

[['fh', '18', '7', '1', '33', '16', '+ 17', '37'], ['íbv', '18', '4', '5', '35', '20', '+ 15', '31'], ['ía', '18', '7', '3', '28', '19', '+ 9', '31'], ['fylkir', '18', '5', '5', '26', '20', '+ 6', '29'], ['keflavík', '18', '3', '8', '31', '33', '- 2', '24'], ['kr', '18', '7', '6', '21', '22', '- 1', '22'], ['grindavík', '18', '7', '6', '24', '31', '- 7', '22'], ['fram', '18', '5', '9', '19', '28', '- 9', '17'], ['víkingur', '18', '4', '10', '19', '30', '- 11', '16'], ['ka', '18', '3', '11', '13', '30', '- 17', '15']]

2007 - 08 belgian first division

https://en.wikipedia.org/wiki/2007%E2%80%9308_Belgian_First_Division

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11713303-2.html.csv

ordinal

peter voets ( caretaker ) had the earliest appointment date in the 2007 - 08 belgian first division .

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

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'date of appointment', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; date of appointment ; 1 }'}, 'replaced by'], 'result': 'peter voets ( caretaker )', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; date of appointment ; 1 } ; replaced by }'}, 'peter voets ( caretaker )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; date of appointment ; 1 } ; replaced by } ; peter voets ( caretaker ) } = true', 'tointer': 'select the row whose date of appointment record of all rows is 1st minimum . the replaced by record of this row is peter voets ( caretaker ) .'}

eq { hop { nth_argmin { all_rows ; date of appointment ; 1 } ; replaced by } ; peter voets ( caretaker ) } = true

select the row whose date of appointment record of all rows is 1st minimum . the replaced by record of this row is peter voets ( caretaker ) .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'date of appointment_5': 5, '1_6': 6, 'replaced by_7': 7, 'peter voets (caretaker)_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'date of appointment_5': 'date of appointment', '1_6': '1', 'replaced by_7': 'replaced by', 'peter voets (caretaker)_8': 'peter voets ( caretaker )'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date of appointment_5': [0], '1_6': [0], 'replaced by_7': [1], 'peter voets (caretaker)_8': [2]}

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

[['sint - truiden', 'valère billen', 'quit', '18 september 2007', 'peter voets ( caretaker )', '18 september 2007'], ['anderlecht', 'franky vercauteren', 'mutual consent', '12 november 2007', 'ariel jacobs', '12 november 2007'], ['dender eh', 'jean - pierre van de velde', 'mutual consent', '19 november 2007', 'johan boskamp', '27 november 2007'], ['charleroi', 'philippe van de walle', 'quit', '10 december 2007', 'thierry siquet', '10 december 2007'], ['sint - truiden', 'peter voets', 'caretaker replaced', '10 december 2007', 'dennis van wijk', '10 december 2007'], ['mouscron', 'marc brys', 'fired', '17 december 2007', 'geert broeckaert ( caretaker )', '17 december 2007'], ['brussels', 'albert cartier', 'fired', '22 december 2007', 'edy de bolle ( caretaker )', '22 december 2007'], ['mouscron', 'geert broeckaert', 'caretaker replaced', '27 december 2007', 'enzo scifo', '27 december 2007'], ['brussels', 'edy de bolle', 'caretaker replaced', '24 january 2008', 'franky van der elst', '24 january 2008'], ['mons', 'josé riga', 'fired', '27 january 2008', 'albert cartier', '28 january 2008'], ['genk', 'hugo broos', 'fired', '23 february 2008', 'ronny van geneugden ( caretaker )', '23 february 2008']]

1997 in paraguayan football

https://en.wikipedia.org/wiki/1997_in_Paraguayan_football

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18703133-6.html.csv

count

paraguayan football played a total of 13 matches in 1997 .

{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '13', 'col': '1', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'position'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record is arbitrary .', 'tostr': 'filter_all { all_rows ; position }'}], 'result': '13', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; position } }', 'tointer': 'select the rows whose position record is arbitrary . the number of such rows is 13 .'}, '13'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; position } } ; 13 } = true', 'tointer': 'select the rows whose position record is arbitrary . the number of such rows is 13 .'}

eq { count { filter_all { all_rows ; position } } ; 13 } = true

select the rows whose position record is arbitrary . the number of such rows is 13 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'position_5': 5, '13_6': 6}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'position_5': 'position', '13_6': '13'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'position_5': [0], '13_6': [2]}

['position', 'team', 'played', 'wins', 'draws pk wins / pk losses', 'losses', 'scored', 'conceded', 'points']

[['1', 'cerro corá', '12', '8', '1 / 2', '1', '22', '11', '28'], ['2', 'guaraní', '12', '6', '1 / 4', '1', '22', '16', '24'], ['3', 'san lorenzo', '12', '5', '2 / 1', '4', '13', '10', '20'], ['4', 'sportivo luqueño', '12', '4', '3 / 2', '3', '18', '16', '20'], ['5', 'olimpia', '12', '4', '4 / 0', '4', '18', '16', '20'], ['6', 'atl colegiales', '12', '4', '3 / 2', '3', '18', '18', '20'], ['7', 'cerro porteño', '12', '4', '1 / 4', '3', '15', '13', '18'], ['8', 'nacional', '12', '4', '1 / 2', '5', '14', '23', '16'], ['9', 'tembetary', '12', '4', '1 / 2', '5', '31', '27', '16'], ['10', 'sport colombia', '12', '3', '1 / 3', '5', '20', '19', '14'], ['11', 'presidente hayes', '12', '3', '2 / 1', '6', '13', '18', '14'], ['12', 'sol de américa', '12', '3', '1 / 1', '7', '11', '15', '12'], ['13', 'libertad', '12', '2', '3 / 0', '7', '8', '21', '12']]

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

count

12 branding of the manila broadcasting company stations use 5 kw power .

{'scope': 'all', 'criterion': 'equal', 'value': '5 kw', 'result': '12', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'power ( kw )', '5 kw'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose power ( kw ) record fuzzily matches to 5 kw .', 'tostr': 'filter_eq { all_rows ; power ( kw ) ; 5 kw }'}], 'result': '12', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; power ( kw ) ; 5 kw } }', 'tointer': 'select the rows whose power ( kw ) record fuzzily matches to 5 kw . the number of such rows is 12 .'}, '12'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; power ( kw ) ; 5 kw } } ; 12 } = true', 'tointer': 'select the rows whose power ( kw ) record fuzzily matches to 5 kw . the number of such rows is 12 .'}

eq { count { filter_eq { all_rows ; power ( kw ) ; 5 kw } } ; 12 } = true

select the rows whose power ( kw ) record fuzzily matches to 5 kw . the number of such rows is 12 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'power (kw)_5': 5, '5 kw_6': 6, '12_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'power (kw)_5': 'power ( kw )', '5 kw_6': '5 kw', '12_7': '12'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'power (kw)_5': [0], '5 kw_6': [0], '12_7': [2]}

['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']]

1967 syracuse orangemen football team

https://en.wikipedia.org/wiki/1967_Syracuse_Orangemen_football_team

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20850339-1.html.csv

majority

the 1967 syracuse orangemen football team won most of their games .

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

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

most_eq { all_rows ; result ; win } = true

for the result records of all rows , most of them fuzzily match to win .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'win_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'win_4': 'win'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'win_4': [0]}

['game', 'date', 'opponent', 'result', 'orangemen points', 'opponents', 'record']

[['1', 'sept 23', 'baylor', 'win', '7', '0', '1 - 0'], ['2', 'sept 30', 'west virginia', 'win', '23', '6', '2 - 0'], ['3', 'oct 7', 'maryland', 'win', '7', '3', '3 - 0'], ['4', 'oct 14', 'navy', 'loss', '14', '27', '3 - 1'], ['5', 'oct 21', 'california', 'win', '20', '14', '4 - 1'], ['6', 'oct 28', 'penn state', 'loss', '20', '29', '4 - 2'], ['7', 'nov 4', 'pittsburgh', 'win', '14', '7', '5 - 2'], ['8', 'nov 11', 'holy cross', 'win', '41', '7', '6 - 2'], ['9', 'nov 18', 'boston college', 'win', '32', '20', '7 - 2']]

wfcr

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1540742-1.html.csv

superlative

in the wfcr stations , w291ch has the highest frequency of 106.1 .

{'scope': 'all', 'col_superlative': '2', 'row_superlative': '1', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '1', 'subset': None}

{'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'frequency mhz'], 'result': '106.1', 'ind': 0, 'tostr': 'max { all_rows ; frequency mhz }', 'tointer': 'the maximum frequency mhz record of all rows is 106.1 .'}, '106.1'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; frequency mhz } ; 106.1 }', 'tointer': 'the maximum frequency mhz record of all rows is 106.1 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'frequency mhz'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; frequency mhz }'}, 'call sign'], 'result': 'w291ch', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; frequency mhz } ; call sign }'}, 'w291ch'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; frequency mhz } ; call sign } ; w291ch }', 'tointer': 'the call sign record of the row with superlative frequency mhz record is w291ch .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { max { all_rows ; frequency mhz } ; 106.1 } ; eq { hop { argmax { all_rows ; frequency mhz } ; call sign } ; w291ch } } = true', 'tointer': 'the maximum frequency mhz record of all rows is 106.1 . the call sign record of the row with superlative frequency mhz record is w291ch .'}

and { eq { max { all_rows ; frequency mhz } ; 106.1 } ; eq { hop { argmax { all_rows ; frequency mhz } ; call sign } ; w291ch } } = true

the maximum frequency mhz record of all rows is 106.1 . the call sign record of the row with superlative frequency mhz record is w291ch .

6

6

{'and_5': 5, 'result_6': 6, 'eq_1': 1, 'max_0': 0, 'all_rows_7': 7, 'frequency mhz_8': 8, '106.1_9': 9, 'str_eq_4': 4, 'str_hop_3': 3, 'argmax_2': 2, 'all_rows_10': 10, 'frequency mhz_11': 11, 'call sign_12': 12, 'w291ch_13': 13}

{'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'max_0': 'max', 'all_rows_7': 'all_rows', 'frequency mhz_8': 'frequency mhz', '106.1_9': '106.1', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'argmax_2': 'argmax', 'all_rows_10': 'all_rows', 'frequency mhz_11': 'frequency mhz', 'call sign_12': 'call sign', 'w291ch_13': 'w291ch'}

{'and_5': [6], 'result_6': [], 'eq_1': [5], 'max_0': [1], 'all_rows_7': [0], 'frequency mhz_8': [0], '106.1_9': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'argmax_2': [3], 'all_rows_10': [2], 'frequency mhz_11': [2], 'call sign_12': [3], 'w291ch_13': [4]}

['call sign', 'frequency mhz', 'city of license', 'erp w', 'class', 'fcc info']

[['w291ch', '106.1', 'pittsfield , massachusetts', '10', 'd', 'fcc'], ['w242at', '96.3', 'williamstown , massachusetts', '250', 'd', 'fcc'], ['w252bg', '98.3', 'lee , massachusetts', '13', 'd', 'fcc'], ['w254au', '98.7', 'great barrington , massachusetts', '250', 'd', 'fcc'], ['w266aw', '101.1', 'north adams , massachusetts', '10', 'd', 'fcc']]

duneland athletic conference

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15090962-1.html.csv

ordinal

of the schools in the duneland athletic conference , portage has the second highest enrollment .

{'row': '7', 'col': '5', 'order': '2', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'enrollment 08 - 09', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; enrollment 08 - 09 ; 2 }'}, 'location'], 'result': 'portage', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; enrollment 08 - 09 ; 2 } ; location }'}, 'portage'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; enrollment 08 - 09 ; 2 } ; location } ; portage } = true', 'tointer': 'select the row whose enrollment 08 - 09 record of all rows is 2nd maximum . the location record of this row is portage .'}

eq { hop { nth_argmax { all_rows ; enrollment 08 - 09 ; 2 } ; location } ; portage } = true

select the row whose enrollment 08 - 09 record of all rows is 2nd maximum . the location record of this row is portage .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'enrollment 08 - 09_5': 5, '2_6': 6, 'location_7': 7, 'portage_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', 'enrollment 08 - 09_5': 'enrollment 08 - 09', '2_6': '2', 'location_7': 'location', 'portage_8': 'portage'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'enrollment 08 - 09_5': [0], '2_6': [0], 'location_7': [1], 'portage_8': [2]}

['school', 'location', 'mascot', 'county', 'enrollment 08 - 09', 'ihsaa class', 'ihsaa class football', 'year joined', 'previous conference']

[['chesterton', 'chesterton', 'trojans', '64 porter', '1921', '4a', '6a', '1970', 'calumet'], ['crown point', 'crown point', 'bulldogs', '45 lake', '2426', '4a', '6a', '1993', 'lake suburban'], ['lake central', 'saint john', 'indians', '45 lake', '3141', '4a', '6a', '2003', 'independents'], ['laporte', 'laporte', 'slicers', '46 laporte', '1956', '4a', '5a', '1976', 'northern indiana'], ['merrillville', 'merrillville', 'pirates', '45 lake', '2241', '4a', '6a', '1975', 'lake suburban'], ['michigan city', 'michigan city', 'wolves', '46 laporte', '1919', '4a', '5a', '1995', 'none ( new school )'], ['portage', 'portage', 'indians', '64 porter', '2618', '4a', '6a', '1970', 'calumet'], ['valparaiso', 'valparaiso', 'vikings', '64 porter', '2072', '4a', '6a', '1970', 'independents']]

1948 ashes series

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

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

superlative

alec bedser had the highest average among all the players from both england and australia in the 1948 ashes series .

{'scope': 'all', 'col_superlative': '5', 'row_superlative': '6', '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', 'average'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; average }'}, 'player'], 'result': 'alec bedser', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; average } ; player }'}, 'alec bedser'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; average } ; player } ; alec bedser } = true', 'tointer': 'select the row whose average record of all rows is maximum . the player record of this row is alec bedser .'}

eq { hop { argmax { all_rows ; average } ; player } ; alec bedser } = true

select the row whose average record of all rows is maximum . the player record of this row is alec bedser .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'average_5': 5, 'player_6': 6, 'alec bedser_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'average_5': 'average', 'player_6': 'player', 'alec bedser_7': 'alec bedser'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'average_5': [0], 'player_6': [1], 'alec bedser_7': [2]}

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

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

2008 - 09 süper lig

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

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

comparative

the bursa atatürk stadium has a smaller capacity than the şükrü saracoğlu stadium .

{'row_1': '5', 'row_2': '8', 'col': '5', 'col_other': '4', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

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

less { hop { filter_eq { all_rows ; venue ; bursa atatürk stadium } ; capacity } ; hop { filter_eq { all_rows ; venue ; şükrü saracoğlu stadium } ; capacity } } = true

select the rows whose venue record fuzzily matches to bursa atatürk stadium . take the capacity record of this row . select the rows whose venue record fuzzily matches to şükrü saracoğlu stadium . take the capacity 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, 'bursa atatürk stadium_8': 8, 'capacity_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'venue_11': 11, 'şükrü saracoğlu stadium_12': 12, 'capacity_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', 'bursa atatürk stadium_8': 'bursa atatürk stadium', 'capacity_9': 'capacity', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'venue_11': 'venue', 'şükrü saracoğlu stadium_12': 'şükrü saracoğlu stadium', 'capacity_13': 'capacity'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'venue_7': [0], 'bursa atatürk stadium_8': [0], 'capacity_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'venue_11': [1], 'şükrü saracoğlu stadium_12': [1], 'capacity_13': [3]}

['team', 'head coach', 'team captain', 'venue', 'capacity', 'kitmaker', 'shirt sponsor', 'club chairman']

[['ankaragücü', 'hakan kutlu', 'murat erdoğan', 'ankara 19 mayıs stadium', '19209', 'lotto', 'turkcell', 'cemal azmi aydın'], ['ankaraspor', 'aykut kocaman', 'hürriyet güçer', 'yenikent asaş stadium', '19626', 'nike', 'turkcell', 'ruhi kurnaz'], ['antalyaspor', 'mehmet özdilek', 'uğur kavuk', 'antalya atatürk stadium', '11137', 'nike', 'mardan', 'hasan y akıncıoğlu'], ['beşiktaş', 'mustafa denizli', 'matías delgado', 'bjk inönü stadium', '32086', 'umbro', 'cola turka', 'yıldırım demirören'], ['bursaspor', 'ertuğrul sağlam', 'ömer erdoğan', 'bursa atatürk stadium', '18587', 'kappa', 'turkcell', 'ibrahim yazıcı'], ['denizlispor', 'mesut bakkal', 'roman kratochvil', 'denizli atatürk stadium', '15427', 'lescon', 'turkcell', 'ali ipek'], ['eskişehirspor', 'rıza çalımbay', 'emre toraman', 'eskişehir atatürk stadium', '18880', 'nike', 'eti', 'halil ünal'], ['fenerbahçe', 'luis aragonés', 'alex', 'şükrü saracoğlu stadium', '53586', 'adidas', 'avea', 'aziz yıldırım'], ['galatasaray', 'bülent korkmaz', 'ayhan akman', 'ali sami yen stadium', '22800', 'adidas', 'avea', 'adnan polat'], ['gaziantepspor', 'josé couceiro', 'bekir irtegün', 'gaziantep kamil ocak stadium', '16981', 'lescon', 'turkcell', 'ibrahim halil kızıl'], ['gençlerbirliği', 'samet aybaba', 'abdel zaher el saka', 'ankara 19 mayıs stadium', '19209', 'lotto', 'turkcell', 'ilhan cavcav'], ['hacettepe', 'erdoğan arıca', 'orhan şam', 'ankara 19 mayıs stadium', '19209', 'lotto', 'turkcell', 'turgay kalemci'], ['istanbul bb', 'abdullah avcı', 'efe inanç', 'atatürk olympic stadium', '76092', 'lescon', 'kalpen', 'göksel gümüşdağ'], ['kayserispor', 'tolunay kafkas', 'mehmet topuz', 'kadir has stadium 1', '32864', 'adidas', 'turkcell', 'recep mamur'], ['kocaelispor', 'erhan altın', 'serdar topraktepe', 'ismet pasa stadium', '12710', 'umbro', 'erciyas', 'serhan gürkan'], ['konyaspor', 'ünal karaman', 'ömer gündostu', 'konya atatürk stadium', '21968', 'lotto', 'turkcell', 'mehmet ali kuntoğlu'], ['sivasspor', 'bülent uygun', 'mehmet yildiz', 'sivas 4 eylül stadium', '14998', 'adidas', 'turkcell', 'mecnun otyakmaz']]

list of game of the year awards

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1851722-10.html.csv

unique

super mario galaxy 2 is the only game of the year award winner for the wii platform .

{'scope': 'all', 'row': '3', 'col': '4', 'col_other': '2', 'criterion': 'equal', 'value': 'wii', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'platform ( s )', 'wii'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose platform ( s ) record fuzzily matches to wii .', 'tostr': 'filter_eq { all_rows ; platform ( s ) ; wii }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; platform ( s ) ; wii } }', 'tointer': 'select the rows whose platform ( s ) record fuzzily matches to wii . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'platform ( s )', 'wii'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose platform ( s ) record fuzzily matches to wii .', 'tostr': 'filter_eq { all_rows ; platform ( s ) ; wii }'}, 'game'], 'result': 'super mario galaxy 2', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; platform ( s ) ; wii } ; game }'}, 'super mario galaxy 2'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; platform ( s ) ; wii } ; game } ; super mario galaxy 2 }', 'tointer': 'the game record of this unqiue row is super mario galaxy 2 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; platform ( s ) ; wii } } ; eq { hop { filter_eq { all_rows ; platform ( s ) ; wii } ; game } ; super mario galaxy 2 } } = true', 'tointer': 'select the rows whose platform ( s ) record fuzzily matches to wii . there is only one such row in the table . the game record of this unqiue row is super mario galaxy 2 .'}

and { only { filter_eq { all_rows ; platform ( s ) ; wii } } ; eq { hop { filter_eq { all_rows ; platform ( s ) ; wii } ; game } ; super mario galaxy 2 } } = true

select the rows whose platform ( s ) record fuzzily matches to wii . there is only one such row in the table . the game record of this unqiue row is super mario galaxy 2 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'platform (s)_7': 7, 'wii_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'game_9': 9, 'super mario galaxy 2_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'platform (s)_7': 'platform ( s )', 'wii_8': 'wii', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'game_9': 'game', 'super mario galaxy 2_10': 'super mario galaxy 2'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'platform (s)_7': [0], 'wii_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'game_9': [2], 'super mario galaxy 2_10': [3]}

['year', 'game', 'genre', 'platform ( s )', 'developer ( s )']

[['2003', 'the legend of zelda : the wind waker', 'action - adventure : open world', 'nintendo gamecube', 'nintendo ead'], ['2009', "demon 's souls", 'action rpg : hack & slash', 'playstation 3', 'from software'], ['2010', 'super mario galaxy 2', 'platformer', 'wii', 'nintendo ead'], ['2011', 'dead space 2', 'survival horror : ( third - person ) shooter', 'microsoft windows , playstation 3 , xbox 360', 'visceral games'], ['2012', 'borderlands 2', 'first - person shooter', 'xbox 360 , windows , playstation 3', 'gearbox software']]

westmorland county , new brunswick

https://en.wikipedia.org/wiki/Westmorland_County%2C_New_Brunswick

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-176529-1.html.csv

unique

beaubassin east has the only rural community status in westmorland county .

{'scope': 'all', 'row': '3', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'rural community', 'subset': None}

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

and { only { filter_eq { all_rows ; status ; rural community } } ; eq { hop { filter_eq { all_rows ; status ; rural community } ; official name } ; beaubassin east } } = true

select the rows whose status record fuzzily matches to rural community . there is only one such row in the table . the official name record of this unqiue row is beaubassin east .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'status_7': 7, 'rural community_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'official name_9': 9, 'beaubassin east_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'status_7': 'status', 'rural community_8': 'rural community', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'official name_9': 'official name', 'beaubassin east_10': 'beaubassin east'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'status_7': [0], 'rural community_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'official name_9': [2], 'beaubassin east_10': [3]}

['official name', 'status', 'area km 2', 'population', 'census ranking']

[['moncton', 'city', '141.17', '69074', '79 of 5008'], ['dieppe', 'city', '51.17', '23310', '174 of 5008'], ['beaubassin east', 'rural community', '291.04', '6200', '600 of 5008'], ['shediac', 'town', '11.97', '6053', '610 of 5008'], ['sackville', 'town', '74.32', '5558', '655 of 5008'], ['memramcook', 'village', '185.71', '4831', '727 of 5008'], ['cap - pelã', 'village', '23.78', '2256', '1229 of 5008'], ['salisbury', 'village', '13.68', '2208', '1243 of 5008'], ['petitcodiac', 'village', '17.22', '1429', '1658 of 5008'], ['dorchester', 'village', '5.74', '1167', '1878 of 5008'], ['port elgin', 'village', '2.61', '418', '3238 of 5008']]

speedway world championship competitions

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12592117-1.html.csv

superlative

first speedway world championship competition among individuals took place in 1931 .

{'scope': 'subset', 'col_superlative': '2', 'row_superlative': '1', 'value_mentioned': 'yes', 'max_or_min': 'min', 'other_col': '1', 'subset': {'col': '1', 'criterion': 'equal', 'value': 'individuals'}}

{'func': 'eq', 'args': [{'func': 'min', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'competing entities', 'individuals'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; competing entities ; individuals }', 'tointer': 'select the rows whose competing entities record fuzzily matches to individuals .'}, 'first held'], 'result': '1931', 'ind': 1, 'tostr': 'min { filter_eq { all_rows ; competing entities ; individuals } ; first held }', 'tointer': 'select the rows whose competing entities record fuzzily matches to individuals . the minimum first held record of these rows is 1931 .'}, '1931'], 'result': True, 'ind': 2, 'tostr': 'eq { min { filter_eq { all_rows ; competing entities ; individuals } ; first held } ; 1931 } = true', 'tointer': 'select the rows whose competing entities record fuzzily matches to individuals . the minimum first held record of these rows is 1931 .'}

eq { min { filter_eq { all_rows ; competing entities ; individuals } ; first held } ; 1931 } = true

select the rows whose competing entities record fuzzily matches to individuals . the minimum first held record of these rows is 1931 .

3

3

{'eq_2': 2, 'result_3': 3, 'min_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'competing entities_5': 5, 'individuals_6': 6, 'first held_7': 7, '1931_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'min_1': 'min', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'competing entities_5': 'competing entities', 'individuals_6': 'individuals', 'first held_7': 'first held', '1931_8': '1931'}

{'eq_2': [3], 'result_3': [], 'min_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'competing entities_5': [0], 'individuals_6': [0], 'first held_7': [1], '1931_8': [2]}

['competing entities', 'first held', 'current holder', 'next', 'held every']

[['individuals', '1931', 'chris holder ( 2012 )', '2013', 'one year'], ['individuals', '1977', 'michael jespen jensen ( 2012 )', '2013', 'one year'], ['national pairs', '1970', 'sweden ( 1993 )', 'defunct', 'one year until 1993'], ['national teams :', '1960', '( 2012 )', '2013', 'one year'], ['national teams :', '2005', '( 2012 )', '2013', 'one year']]

1970 - 71 cleveland cavaliers season

https://en.wikipedia.org/wiki/1970%E2%80%9371_Cleveland_Cavaliers_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16275352-5.html.csv

ordinal

the cleveland cavaliers ' game against the atlanta hawks was the earliest in the 1970 - 71 season .

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

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

eq { hop { nth_argmin { all_rows ; date ; 1 } ; opponent } ; atlanta hawks } = true

select the row whose date record of all rows is 1st minimum . the opponent record of this row is atlanta hawks .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, '1_6': 6, 'opponent_7': 7, 'atlanta hawks_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'date_5': 'date', '1_6': '1', 'opponent_7': 'opponent', 'atlanta hawks_8': 'atlanta hawks'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], '1_6': [0], 'opponent_7': [1], 'atlanta hawks_8': [2]}

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

[['november 1', 'h', 'atlanta hawks', '107 - 130', '0 - 10'], ['november 2', 'a', 'philadelphia 76ers', '87 - 141', '0 - 11'], ['november 4', 'h', 'milwaukee bucks', '108 - 110', '0 - 12'], ['november 7', 'a', 'buffalo braves', '91 - 103', '0 - 13'], ['november 8', 'h', 'seattle supersonics', '105 - 111', '0 - 14'], ['november 10', 'a', 'san francisco warriors', '74 - 109', '0 - 15'], ['november 12', 'a', 'portland trail blazers', '105 - 103', '1 - 15'], ['november 13', 'a', 'seattle supersonics', '91 - 111', '1 - 16'], ['november 14', 'a', 'portland trail blazers', '110 - 125', '1 - 17'], ['november 16', 'a', 'baltimore bullets', '86 - 98', '1 - 18'], ['november 18', 'h', 'baltimore bullets', '98 - 111', '1 - 19'], ['november 20', 'a', 'boston celtics', '112 - 116', '1 - 20'], ['november 21', 'a', 'new york knicks', '94 - 102', '1 - 21'], ['november 22', 'h', 'phoenix suns', '99 - 114', '1 - 22'], ['november 25', 'h', 'san francisco warriors', '99 - 108', '1 - 23'], ['november 27', 'h', 'portland trail blazers', '102 - 111', '1 - 24'], ['november 28', 'a', 'cincinnati royals', '86 - 105', '1 - 25'], ['november 29', 'h', 'detroit pistons', '99 - 120', '1 - 26']]

1963 vfl season

https://en.wikipedia.org/wiki/1963_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10783853-8.html.csv

aggregation

the average crowd size across all the games in the 1963 vfl season was around 35000 .

{'scope': 'all', 'col': '6', 'type': 'average', 'result': '35000', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '35000', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '35000'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 35000 } = true', 'tointer': 'the average of the crowd record of all rows is 35000 .'}

round_eq { avg { all_rows ; crowd } ; 35000 } = true

the average of the crowd record of all rows is 35000 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '35000_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '35000_5': '35000'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '35000_5': [1]}

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

[['essendon', '13.11 ( 89 )', 'richmond', '7.5 ( 47 )', 'windy hill', '21200', '8 june 1963'], ['carlton', '6.8 ( 44 )', 'collingwood', '6.10 ( 46 )', 'princes park', '38698', '8 june 1963'], ['st kilda', '8.13 ( 61 )', 'hawthorn', '9.11 ( 65 )', 'junction oval', '34900', '8 june 1963'], ['footscray', '6.16 ( 52 )', 'south melbourne', '5.9 ( 39 )', 'western oval', '22950', '10 june 1963'], ['fitzroy', '2.11 ( 23 )', 'north melbourne', '6.15 ( 51 )', 'brunswick street oval', '13400', '10 june 1963'], ['melbourne', '11.16 ( 82 )', 'geelong', '4.11 ( 35 )', 'mcg', '81550', '10 june 1963']]

2008 tour de suisse

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17672470-19.html.csv

unique

in the 2008 tour de suisse , when igor anton was the general classification , the only time markus fothen was the winner was in stage 5 .

{'scope': 'subset', 'row': '5', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'markus fothen', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'igor antón'}}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'general classification', 'igor antón'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; general classification ; igor antón }', 'tointer': 'select the rows whose general classification record fuzzily matches to igor antón .'}, 'winner', 'markus fothen'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose general classification record fuzzily matches to igor antón . among these rows , select the rows whose winner record fuzzily matches to markus fothen .', 'tostr': 'filter_eq { filter_eq { all_rows ; general classification ; igor antón } ; winner ; markus fothen }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; general classification ; igor antón } ; winner ; markus fothen } }', 'tointer': 'select the rows whose general classification record fuzzily matches to igor antón . among these rows , select the rows whose winner record fuzzily matches to markus fothen . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'general classification', 'igor antón'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; general classification ; igor antón }', 'tointer': 'select the rows whose general classification record fuzzily matches to igor antón .'}, 'winner', 'markus fothen'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose general classification record fuzzily matches to igor antón . among these rows , select the rows whose winner record fuzzily matches to markus fothen .', 'tostr': 'filter_eq { filter_eq { all_rows ; general classification ; igor antón } ; winner ; markus fothen }'}, 'stage'], 'result': '5', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; general classification ; igor antón } ; winner ; markus fothen } ; stage }'}, '5'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; general classification ; igor antón } ; winner ; markus fothen } ; stage } ; 5 }', 'tointer': 'the stage record of this unqiue row is 5 .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; general classification ; igor antón } ; winner ; markus fothen } } ; eq { hop { filter_eq { filter_eq { all_rows ; general classification ; igor antón } ; winner ; markus fothen } ; stage } ; 5 } } = true', 'tointer': 'select the rows whose general classification record fuzzily matches to igor antón . among these rows , select the rows whose winner record fuzzily matches to markus fothen . there is only one such row in the table . the stage record of this unqiue row is 5 .'}

and { only { filter_eq { filter_eq { all_rows ; general classification ; igor antón } ; winner ; markus fothen } } ; eq { hop { filter_eq { filter_eq { all_rows ; general classification ; igor antón } ; winner ; markus fothen } ; stage } ; 5 } } = true

select the rows whose general classification record fuzzily matches to igor antón . among these rows , select the rows whose winner record fuzzily matches to markus fothen . there is only one such row in the table . the stage record of this unqiue row is 5 .

8

6

{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'general classification_8': 8, 'igor antón_9': 9, 'winner_10': 10, 'markus fothen_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'stage_12': 12, '5_13': 13}

{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'general classification_8': 'general classification', 'igor antón_9': 'igor antón', 'winner_10': 'winner', 'markus fothen_11': 'markus fothen', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'stage_12': 'stage', '5_13': '5'}

{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'general classification_8': [0], 'igor antón_9': [0], 'winner_10': [1], 'markus fothen_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'stage_12': [3], '5_13': [4]}

['stage', 'winner', 'general classification', 'mountains classification', 'points classification', 'sprints classification', 'team classification']

[['1', 'óscar freire', 'óscar freire', 'no award', 'óscar freire', 'no award', "caisse d'epargne"], ['2', 'igor antón', 'igor antón', 'david loosli', 'kim kirchen', 'david loosli', 'team csc'], ['3', 'robbie mcewen', 'igor antón', 'david loosli', 'óscar freire', 'rené weissinger', 'team csc'], ['4', 'robbie mcewen', 'igor antón', 'david loosli', 'óscar freire', 'rené weissinger', 'team csc'], ['5', 'markus fothen', 'igor antón', 'david loosli', 'óscar freire', 'rené weissinger', 'gerolsteiner'], ['6', 'kim kirchen', 'kim kirchen', 'david loosli', 'óscar freire', 'rené weissinger', 'astana'], ['7', 'fabian cancellara', 'kim kirchen', 'maxim iglinsky', 'óscar freire', 'rené weissinger', 'astana'], ['8', 'roman kreuziger', 'roman kreuziger', 'maxim iglinsky', 'óscar freire', 'rené weissinger', 'astana'], ['9', 'fabian cancellara', 'roman kreuziger', 'maxim iglinsky', 'fabian cancellara', 'rené weissinger', 'astana'], ['final', 'final', 'roman kreuziger', 'maxim iglinsky', 'fabian cancellara', 'rené weissinger', 'astana']]

1984 - 85 boston celtics season

https://en.wikipedia.org/wiki/1984%E2%80%9385_Boston_Celtics_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17344651-6.html.csv

count

the boston celtics played against the new york knicks two times .

{'scope': 'all', 'criterion': 'equal', 'value': 'new york knicks', 'result': '2', 'col': '3', 'subset': None}

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

eq { count { filter_eq { all_rows ; opponent ; new york knicks } } ; 2 } = true

select the rows whose opponent record fuzzily matches to new york knicks . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'opponent_5': 5, 'new york knicks_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'opponent_5': 'opponent', 'new york knicks_6': 'new york knicks', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'opponent_5': [0], 'new york knicks_6': [0], '2_7': [2]}

['game', 'date', 'opponent', 'score', 'location', 'record']

[['33', 'wed jan 2', 'new jersey nets', '110 - 95', 'brendan byrne arena', '27 - 6'], ['34', 'fri jan 4', 'new york knicks', '105 - 94', 'boston garden', '28 - 6'], ['35', 'mon jan 7', 'new york knicks', '108 - 97', 'madison square garden', '29 - 6'], ['36', 'wed jan 9', 'chicago bulls', '111 - 108', 'boston garden', '30 - 6'], ['37', 'fri jan 11', 'washington bullets', '103 - 101', 'boston garden', '31 - 6'], ['38', 'sat jan 12', 'atlanta hawks', '119 - 111', 'the omni', '32 - 6'], ['39', 'wed jan 16', 'los angeles lakers', '104 - 102', 'boston garden', '33 - 6'], ['40', 'fri jan 18', 'indiana pacers', '86 - 91', 'market square arena', '33 - 7'], ['41', 'sun jan 20', 'philadelphia 76ers', '113 - 97', 'boston garden', '34 - 7'], ['42', 'wed jan 23', 'seattle supersonics', '97 - 107', 'boston garden', '34 - 8'], ['43', 'fri jan 25', 'indiana pacers', '125 - 94', 'boston garden', '35 - 8'], ['44', 'sun jan 27', 'portland trail blazers', '128 - 127', 'boston garden', '36 - 8'], ['45', 'tue jan 29', 'detroit pistons', '131 - 130', 'hartford civic center', '37 - 8'], ['46', 'wed jan 30', 'philadelphia 76ers', '104 - 122', 'the spectrum', '37 - 9']]

wru division four west

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13758945-2.html.csv

unique

burry port rfc is the only club that lost fourteen times .

{'scope': 'all', 'row': '10', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': '14', 'subset': None}

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

and { only { filter_eq { all_rows ; lost ; 14 } } ; eq { hop { filter_eq { all_rows ; lost ; 14 } ; club } ; burry port rfc } } = true

select the rows whose lost record is equal to 14 . there is only one such row in the table . the club record of this unqiue row is burry port rfc .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'lost_7': 7, '14_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'club_9': 9, 'burry port rfc_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'lost_7': 'lost', '14_8': '14', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'club_9': 'club', 'burry port rfc_10': 'burry port rfc'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'lost_7': [0], '14_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'club_9': [2], 'burry port rfc_10': [3]}

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

[['club', 'played', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points'], ['llandeilo rfc', '22', '1', '0', '917', '119', '136', '14', '19', '0', '105'], ['brynamman rfc', '22', '1', '2', '821', '210', '116', '27', '16', '2', '96'], ['tenby united rfc', '22', '0', '8', '562', '461', '78', '61', '10', '1', '67'], ['pembroke dock harlequins rfc', '22', '0', '8', '423', '351', '56', '40', '7', '3', '66'], ['pontarddulais rfc', '22', '1', '9', '550', '503', '79', '68', '11', '5', '66'], ['betws rfc', '22', '1', '9', '528', '440', '72', '63', '9', '0', '59'], ['trimsaran rfc', '22', '0', '12', '471', '540', '68', '77', '7', '1', '48'], ['pembroke rfc', '22', '0', '13', '467', '500', '69', '66', '8', '4', '48'], ['burry port rfc', '22', '1', '14', '373', '688', '47', '99', '3', '2', '31'], ['hendy rfc', '22', '0', '17', '292', '707', '38', '109', '1', '6', '27'], ['tycroes rfc', '22', '0', '18', '267', '645', '35', '89', '3', '3', '18'], ['cwmgors rfc', '22', '1', '19', '211', '718', '28', '109', '2', '3', '15']]

test matches ( 1991 - 2000 )

https://en.wikipedia.org/wiki/Test_matches_%281991%E2%80%932000%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12410929-70.html.csv

superlative

of the test matches of 1991-2000 , the match won with the highest number of runs was played at the adelaide oval .

{'scope': 'all', 'col_superlative': '5', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '4', 'subset': None}

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

eq { hop { argmax { all_rows ; result } ; venue } ; adelaide oval } = true

select the row whose result record of all rows is maximum . the venue record of this row is adelaide oval .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'result_5': 5, 'venue_6': 6, 'adelaide oval_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'result_5': 'result', 'venue_6': 'venue', 'adelaide oval_7': 'adelaide oval'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'result_5': [0], 'venue_6': [1], 'adelaide oval_7': [2]}

['date', 'home captain', 'away captain', 'venue', 'result']

[['22 , 23 , 24 , 25 , 26 november 1996', 'mark taylor', 'courtney walsh', 'brisbane cricket ground', 'aus by 123 runs'], ['29 , 30 november , 1 , 2 , 3 december 1996', 'mark taylor', 'courtney walsh', 'sydney cricket ground', 'aus by 124 runs'], ['26 , 27 , 28 december 1996', 'mark taylor', 'courtney walsh', 'melbourne cricket ground', 'wi by 6 wkts'], ['25 , 26 , 27 , 28 january 1997', 'mark taylor', 'courtney walsh', 'adelaide oval', 'aus by inns & 183 runs'], ['1 , 2 , 3 february 1997', 'mark taylor', 'courtney walsh', 'waca ground', 'wi by 10 wkts']]

huntington area rapid transit

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14858130-4.html.csv

ordinal

considering the retired fleet of the huntington area rapid transit , the flxible 35096-6 -1 model had the third highest length .

{'row': '2', 'col': '3', 'order': '3', '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', 'length', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; length ; 3 }'}, 'model'], 'result': 'flxible 35096 - 6 - 1', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; length ; 3 } ; model }'}, 'flxible 35096 - 6 - 1'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; length ; 3 } ; model } ; flxible 35096 - 6 - 1 } = true', 'tointer': 'select the row whose length record of all rows is 3rd maximum . the model record of this row is flxible 35096 - 6 - 1 .'}

eq { hop { nth_argmax { all_rows ; length ; 3 } ; model } ; flxible 35096 - 6 - 1 } = true

select the row whose length record of all rows is 3rd maximum . the model record of this row is flxible 35096 - 6 - 1 .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'length_5': 5, '3_6': 6, 'model_7': 7, 'flxible 35096 - 6 - 1_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', 'length_5': 'length', '3_6': '3', 'model_7': 'model', 'flxible 35096 - 6 - 1_8': 'flxible 35096 - 6 - 1'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'length_5': [0], '3_6': [0], 'model_7': [1], 'flxible 35096 - 6 - 1_8': [2]}

['year', 'model', 'length', 'width', 'fleet number']

[['1970', 'gmc t6h4521a', "35 '", '96', '101 - 103'], ['1977', 'flxible 35096 - 6 - 1', "31 '", '96', '201 - 211'], ['1984', 'orion 01.507', "36 ' 8", '96', '301 - 303'], ['19xx', 'chance rt52', "25 ' 11", '96', "400 's"], ['1993', 'gillig phantom 3096tb', "30 '", '96', '501 - 506'], ['1998', 'gillig phantom 3096tb', "30 '", '96', '603'], ['1999', 'chance rt52', "25 ' 11", '96', '701 - 705']]

kentucky intercollegiate athletic conference

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10581768-2.html.csv

superlative

indiana university southeast has the most enrollment among institutions in the kentucky intercollegiate athletic conference .

{'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', 'enrollment'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; enrollment }'}, 'institution'], 'result': 'indiana university southeast', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; enrollment } ; institution }'}, 'indiana university southeast'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; enrollment } ; institution } ; indiana university southeast } = true', 'tointer': 'select the row whose enrollment record of all rows is maximum . the institution record of this row is indiana university southeast .'}

eq { hop { argmax { all_rows ; enrollment } ; institution } ; indiana university southeast } = true

select the row whose enrollment record of all rows is maximum . the institution record of this row is indiana university southeast .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'enrollment_5': 5, 'institution_6': 6, 'indiana university southeast_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'enrollment_5': 'enrollment', 'institution_6': 'institution', 'indiana university southeast_7': 'indiana university southeast'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'enrollment_5': [0], 'institution_6': [1], 'indiana university southeast_7': [2]}

['institution', 'nickname', 'location', 'founded', 'type', 'enrollment']

[['alice lloyd college', 'eagles', 'pippa passes , kentucky', '1923', 'private', '600'], ['asbury university', 'eagles', 'wilmore , kentucky', '1890', 'private', '1300'], ['berea college', 'mountaineers', 'berea , kentucky', '1855', 'private', '1514'], ['brescia university', 'bearcats', 'owensboro , kentucky', '1950', 'private', '750'], ['carlow university 1', 'celtics', 'pittsburgh , pennsylvania', '1929', 'private', '2400'], ['cincinnati christian university', 'eagles', 'cincinnati , ohio', '1924', 'private', '1100'], ['indiana university east', 'red wolves', 'richmond , indiana', '1971', 'public', '2700'], ['indiana university kokomo', 'cougars', 'kokomo , indiana', '1945', 'public', '3719'], ['indiana university southeast', 'grenadiers', 'new albany , indiana', '1941', 'public', '6840'], ['midway college 1', 'eagles', 'midway , kentucky', '1847', 'private', '1800'], ['point park university', 'pioneers', 'pittsburgh , pennsylvania', '1960', 'private', '3376']]

list of actors who played president of the united states

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1673723-9.html.csv

count

anthony hopkins was nominated for two different categories .

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

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

eq { count { filter_eq { all_rows ; nominee ; anthony hopkins } } ; 2 } = true

select the rows whose nominee record fuzzily matches to anthony hopkins . 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, 'nominee_5': 5, 'anthony hopkins_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', 'nominee_5': 'nominee', 'anthony hopkins_6': 'anthony hopkins', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'nominee_5': [0], 'anthony hopkins_6': [0], '2_7': [2]}

['year', 'category', 'president', 'nominee', 'film', 'result']

[['1941', 'best actor', 'abraham lincoln', 'raymond massey', 'abe lincoln in illinois', 'nominated'], ['1976', 'best actor', 'harry s truman', 'james whitmore', "give 'em hell , harry !", 'nominated'], ['1996', 'best actor', 'richard nixon', 'anthony hopkins', 'nixon', 'nominated'], ['1998', 'best supporting actor', 'john quincy adams', 'anthony hopkins', 'amistad', 'nominated'], ['2009', 'best actor', 'richard nixon', 'frank langella', 'frost / nixon', 'nominated'], ['2013', 'best actor', 'abraham lincoln', 'daniel day - lewis', 'lincoln', 'won']]

wnba finals

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1164512-3.html.csv

count

2 teams which participated in the wnba finals had single wins .

{'scope': 'all', 'criterion': 'equal', 'value': '1', 'result': '2', 'col': '3', 'subset': None}

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

eq { count { filter_eq { all_rows ; wins ; 1 } } ; 2 } = true

select the rows whose wins record is equal to 1 . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'wins_5': 5, '1_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'wins_5': 'wins', '1_6': '1', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'wins_5': [0], '1_6': [0], '2_7': [2]}

['finals', 'team', 'wins', 'losses', 'pct']

[['4', 'houston comets 2', '4', '0', '1.000'], ['4', 'detroit shock 3', '3', '1', '750'], ['4', 'new york liberty', '0', '4', '000'], ['3', 'los angeles sparks', '2', '1', '667'], ['3', 'phoenix mercury', '2', '1', '667'], ['3', 'atlanta dream', '0', '3', '000'], ['3', 'minnesota lynx', '2', '1', '667'], ['2', 'seattle storm', '2', '0', '1.000'], ['2', 'sacramento monarchs 4', '1', '1', '500'], ['2', 'connecticut sun', '0', '2', '000'], ['2', 'indiana fever', '1', '1', '500'], ['1', 'san antonio silver stars', '0', '1', '000'], ['1', 'charlotte sting 1', '0', '1', '000']]

sidecarcross world championship

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16729457-16.html.csv

unique

the only people to use husaberg - wht equipment were daniel millard/joe millard .

{'scope': 'all', 'row': '10', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'husaberg - wht', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'equipment', 'husaberg - wht'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose equipment record fuzzily matches to husaberg - wht .', 'tostr': 'filter_eq { all_rows ; equipment ; husaberg - wht }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; equipment ; husaberg - wht } }', 'tointer': 'select the rows whose equipment record fuzzily matches to husaberg - wht . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'equipment', 'husaberg - wht'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose equipment record fuzzily matches to husaberg - wht .', 'tostr': 'filter_eq { all_rows ; equipment ; husaberg - wht }'}, 'driver / passenger'], 'result': 'daniel millard / joe millard', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; equipment ; husaberg - wht } ; driver / passenger }'}, 'daniel millard / joe millard'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; equipment ; husaberg - wht } ; driver / passenger } ; daniel millard / joe millard }', 'tointer': 'the driver / passenger record of this unqiue row is daniel millard / joe millard .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; equipment ; husaberg - wht } } ; eq { hop { filter_eq { all_rows ; equipment ; husaberg - wht } ; driver / passenger } ; daniel millard / joe millard } } = true', 'tointer': 'select the rows whose equipment record fuzzily matches to husaberg - wht . there is only one such row in the table . the driver / passenger record of this unqiue row is daniel millard / joe millard .'}

and { only { filter_eq { all_rows ; equipment ; husaberg - wht } } ; eq { hop { filter_eq { all_rows ; equipment ; husaberg - wht } ; driver / passenger } ; daniel millard / joe millard } } = true

select the rows whose equipment record fuzzily matches to husaberg - wht . there is only one such row in the table . the driver / passenger record of this unqiue row is daniel millard / joe millard .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'equipment_7': 7, 'husaberg - wht_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'driver / passenger_9': 9, 'daniel millard / joe millard_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'equipment_7': 'equipment', 'husaberg - wht_8': 'husaberg - wht', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'driver / passenger_9': 'driver / passenger', 'daniel millard / joe millard_10': 'daniel millard / joe millard'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'equipment_7': [0], 'husaberg - wht_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'driver / passenger_9': [2], 'daniel millard / joe millard_10': [3]}

['position', 'driver / passenger', 'equipment', 'bike no', 'points']

[['1', 'daniãl willemsen / sven verbrugge 1', 'zabel - wsp', '1', '487'], ['2', 'janis daiders / lauris daiders', 'zabel - vmc', '8', '478'], ['3', 'jan hendrickx / tim smeuninx', 'zabel - vmc', '3', '405'], ['4', 'maris rupeiks / kaspars stupelis 2', 'zabel - wsp', '5', '349'], ['5', 'etienne bax / ben van den bogaart', 'zabel - vmc', '4', '347'], ['6', 'ben adriaenssen / guennady auvray', 'ktm - vmc', '6', '346'], ['7', 'ewgeny scherbinin / haralds kurpnieks', 'zabel - wsp', '20', '321'], ['8', 'marko happich / meinrad schelbert', 'zabel - vmc', '15', '317'], ['9', 'joris hendrickx / kaspars liepins', 'ktm - vmc', '2', '315'], ['10', 'daniel millard / joe millard', 'husaberg - wht', '14', '268']]

1997 - 98 philadelphia flyers season

https://en.wikipedia.org/wiki/1997%E2%80%9398_Philadelphia_Flyers_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14344681-14.html.csv

comparative

in the 1997-98 philadelphia flyers season , jordon flodell was drafted one round before todd fedoruk .

{'row_1': '5', 'row_2': '6', 'col': '1', 'col_other': '2', 'relation': 'diff', 'record_mentioned': 'yes', 'diff_result': {'diff_value': '1', 'bigger': 'row2'}}

{'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'jordon flodell'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to jordon flodell .', 'tostr': 'filter_eq { all_rows ; player ; jordon flodell }'}, 'round'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; jordon flodell } ; round }', 'tointer': 'select the rows whose player record fuzzily matches to jordon flodell . take the round record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'todd fedoruk'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to todd fedoruk .', 'tostr': 'filter_eq { all_rows ; player ; todd fedoruk }'}, 'round'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; todd fedoruk } ; round }', 'tointer': 'select the rows whose player record fuzzily matches to todd fedoruk . take the round record of this row .'}], 'result': '-1', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; player ; jordon flodell } ; round } ; hop { filter_eq { all_rows ; player ; todd fedoruk } ; round } }'}, '-1'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; player ; jordon flodell } ; round } ; hop { filter_eq { all_rows ; player ; todd fedoruk } ; round } } ; -1 }', 'tointer': 'select the rows whose player record fuzzily matches to jordon flodell . take the round record of this row . select the rows whose player record fuzzily matches to todd fedoruk . take the round record of this row . the second record is 1 larger than the first record .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'jordon flodell'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to jordon flodell .', 'tostr': 'filter_eq { all_rows ; player ; jordon flodell }'}, 'round'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; jordon flodell } ; round }', 'tointer': 'select the rows whose player record fuzzily matches to jordon flodell . take the round record of this row .'}, '6'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; player ; jordon flodell } ; round } ; 6 }', 'tointer': 'the round record of the first row is 6 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'todd fedoruk'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to todd fedoruk .', 'tostr': 'filter_eq { all_rows ; player ; todd fedoruk }'}, 'round'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; todd fedoruk } ; round }', 'tointer': 'select the rows whose player record fuzzily matches to todd fedoruk . take the round record of this row .'}, '7'], 'result': True, 'ind': 7, 'tostr': 'eq { hop { filter_eq { all_rows ; player ; todd fedoruk } ; round } ; 7 }', 'tointer': 'the round record of the second row is 7 .'}], 'result': True, 'ind': 8, 'tostr': 'and { eq { hop { filter_eq { all_rows ; player ; jordon flodell } ; round } ; 6 } ; eq { hop { filter_eq { all_rows ; player ; todd fedoruk } ; round } ; 7 } }', 'tointer': 'the round record of the first row is 6 . the round record of the second row is 7 .'}], 'result': True, 'ind': 9, 'tostr': 'and { eq { diff { hop { filter_eq { all_rows ; player ; jordon flodell } ; round } ; hop { filter_eq { all_rows ; player ; todd fedoruk } ; round } } ; -1 } ; and { eq { hop { filter_eq { all_rows ; player ; jordon flodell } ; round } ; 6 } ; eq { hop { filter_eq { all_rows ; player ; todd fedoruk } ; round } ; 7 } } } = true', 'tointer': 'select the rows whose player record fuzzily matches to jordon flodell . take the round record of this row . select the rows whose player record fuzzily matches to todd fedoruk . take the round record of this row . the second record is 1 larger than the first record . the round record of the first row is 6 . the round record of the second row is 7 .'}

and { eq { diff { hop { filter_eq { all_rows ; player ; jordon flodell } ; round } ; hop { filter_eq { all_rows ; player ; todd fedoruk } ; round } } ; -1 } ; and { eq { hop { filter_eq { all_rows ; player ; jordon flodell } ; round } ; 6 } ; eq { hop { filter_eq { all_rows ; player ; todd fedoruk } ; round } ; 7 } } } = true

select the rows whose player record fuzzily matches to jordon flodell . take the round record of this row . select the rows whose player record fuzzily matches to todd fedoruk . take the round record of this row . the second record is 1 larger than the first record . the round record of the first row is 6 . the round record of the second row is 7 .

14

10

{'and_9': 9, 'result_10': 10, 'eq_5': 5, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_11': 11, 'player_12': 12, 'jordon flodell_13': 13, 'round_14': 14, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_15': 15, 'player_16': 16, 'todd fedoruk_17': 17, 'round_18': 18, '-1_19': 19, 'and_8': 8, 'eq_6': 6, '6_20': 20, 'eq_7': 7, '7_21': 21}

{'and_9': 'and', 'result_10': 'true', 'eq_5': 'eq', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_11': 'all_rows', 'player_12': 'player', 'jordon flodell_13': 'jordon flodell', 'round_14': 'round', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_15': 'all_rows', 'player_16': 'player', 'todd fedoruk_17': 'todd fedoruk', 'round_18': 'round', '-1_19': '-1', 'and_8': 'and', 'eq_6': 'eq', '6_20': '6', 'eq_7': 'eq', '7_21': '7'}

{'and_9': [10], 'result_10': [], 'eq_5': [9], 'diff_4': [5], 'num_hop_2': [4, 6], 'filter_str_eq_0': [2], 'all_rows_11': [0], 'player_12': [0], 'jordon flodell_13': [0], 'round_14': [2], 'num_hop_3': [4, 7], 'filter_str_eq_1': [3], 'all_rows_15': [1], 'player_16': [1], 'todd fedoruk_17': [1], 'round_18': [3], '-1_19': [5], 'and_8': [9], 'eq_6': [8], '6_20': [6], 'eq_7': [8], '7_21': [7]}

['round', 'player', 'position', 'nationality', 'college / junior / club team ( league )']

[['2', 'jean - marc pelletier', 'goaltender', 'united states', 'cornell big red ( ecac )'], ['2', 'pat kavanagh', 'right wing', 'canada', 'peterborough petes ( ohl )'], ['3', 'kris mallette', 'defense', 'canada', 'kelowna rockets ( whl )'], ['4', 'mikhail chernov', 'defense', 'russia', 'torpedo yaroslavl ( rus )'], ['6', 'jordon flodell', 'defense', 'canada', 'moose jaw warriors ( whl )'], ['7', 'todd fedoruk', 'left wing', 'canada', 'kelowna rockets ( whl )'], ['8', 'marko kauppinen', 'defense', 'finland', 'jyp ht juniors ( fin )'], ['9', 'par styf', 'defense', 'sweden', 'modo jrs ( swe )']]

1931 vfl season

https://en.wikipedia.org/wiki/1931_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10789881-9.html.csv

majority

most of the games on july 4 , 1931 had a crowd of over 10,000 .

{'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '10000', 'subset': None}

{'func': 'most_greater', 'args': ['all_rows', 'crowd', '10000'], 'result': True, 'ind': 0, 'tointer': 'for the crowd records of all rows , most of them are greater than 10000 .', 'tostr': 'most_greater { all_rows ; crowd ; 10000 } = true'}

most_greater { all_rows ; crowd ; 10000 } = true

for the crowd records of all rows , most of them are greater than 10000 .

1

1

{'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'crowd_3': 3, '10000_4': 4}

{'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'crowd_3': 'crowd', '10000_4': '10000'}

{'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'crowd_3': [0], '10000_4': [0]}

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

[['melbourne', '4.12 ( 36 )', 'st kilda', '9.9 ( 63 )', 'mcg', '15826', '4 july 1931'], ['geelong', '13.9 ( 87 )', 'hawthorn', '9.7 ( 61 )', 'corio oval', '9500', '4 july 1931'], ['fitzroy', '8.13 ( 61 )', 'richmond', '14.17 ( 101 )', 'brunswick street oval', '15000', '4 july 1931'], ['south melbourne', '10.13 ( 73 )', 'essendon', '9.9 ( 63 )', 'lake oval', '11000', '4 july 1931'], ['footscray', '4.16 ( 40 )', 'collingwood', '6.8 ( 44 )', 'western oval', '21500', '4 july 1931'], ['north melbourne', '7.8 ( 50 )', 'carlton', '20.10 ( 130 )', 'arden street oval', '10000', '4 july 1931']]

1997 cfl draft

https://en.wikipedia.org/wiki/1997_CFL_Draft

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28059992-6.html.csv

unique

chris hardy was the only player who played the qb position .

{'scope': 'all', 'row': '6', 'col': '4', 'col_other': '3', 'criterion': 'equal', 'value': 'qb', 'subset': None}

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

and { only { filter_eq { all_rows ; position ; qb } } ; eq { hop { filter_eq { all_rows ; position ; qb } ; player } ; chris hardy } } = true

select the rows whose position record fuzzily matches to qb . there is only one such row in the table . the player record of this unqiue row is chris hardy .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'position_7': 7, 'qb_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'chris hardy_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'position_7': 'position', 'qb_8': 'qb', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'chris hardy_10': 'chris hardy'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'position_7': [0], 'qb_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'chris hardy_10': [3]}

['pick', 'cfl team', 'player', 'position', 'college']

[['42', 'saskatchewan', 'dan comiskey', 'ol', 'windsor'], ['43', 'bc', 'kelly lochbaum', 'lb', 'northern arizona'], ['44', 'winnipeg', 'wayne weathers', 'de', 'manitoba'], ['45', 'montreal', 'francis bellefroid', 'lb', "bishop 's"], ['46', 'calgary', 'paul donkersley', 'rb', 'acadia'], ['47', 'edmonton', 'chris hardy', 'qb', 'manitoba']]

list of schools in the auckland region

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12017602-20.html.csv

unique

st mary 's catholic school is the only school in the auckland region that has a state integrated authority .

{'scope': 'all', 'row': '15', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'state integrated', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'authority', 'state integrated'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose authority record fuzzily matches to state integrated .', 'tostr': 'filter_eq { all_rows ; authority ; state integrated }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; authority ; state integrated } }', 'tointer': 'select the rows whose authority record fuzzily matches to state integrated . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'authority', 'state integrated'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose authority record fuzzily matches to state integrated .', 'tostr': 'filter_eq { all_rows ; authority ; state integrated }'}, 'name'], 'result': "st mary 's catholic school", 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; authority ; state integrated } ; name }'}, "st mary 's catholic school"], 'result': True, 'ind': 3, 'tostr': "eq { hop { filter_eq { all_rows ; authority ; state integrated } ; name } ; st mary 's catholic school }", 'tointer': "the name record of this unqiue row is st mary 's catholic school ."}], 'result': True, 'ind': 4, 'tostr': "and { only { filter_eq { all_rows ; authority ; state integrated } } ; eq { hop { filter_eq { all_rows ; authority ; state integrated } ; name } ; st mary 's catholic school } } = true", 'tointer': "select the rows whose authority record fuzzily matches to state integrated . there is only one such row in the table . the name record of this unqiue row is st mary 's catholic school ."}

and { only { filter_eq { all_rows ; authority ; state integrated } } ; eq { hop { filter_eq { all_rows ; authority ; state integrated } ; name } ; st mary 's catholic school } } = true

select the rows whose authority record fuzzily matches to state integrated . there is only one such row in the table . the name record of this unqiue row is st mary 's catholic school .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'authority_7': 7, 'state integrated_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, "st mary 's catholic school_10": 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'authority_7': 'authority', 'state integrated_8': 'state integrated', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', "st mary 's catholic school_10": "st mary 's catholic school"}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'authority_7': [0], 'state integrated_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], "st mary 's catholic school_10": [3]}

['name', 'years', 'gender', 'area', 'authority', 'decile', 'roll']

[['conifer grove school', '1 - 8', 'coed', 'takanini', 'state', '7', '526'], ['cosgrove school', '1 - 6', 'coed', 'papakura', 'state', '2', '606'], ['drury school', '1 - 8', 'coed', 'drury', 'state', '8', '423'], ['edmund hillary school', '1 - 8', 'coed', 'papakura', 'state', '1', '146'], ['hingaia peninsula school', '1 - 8', 'coed', 'karaka', 'state', '9', '91'], ['kelvin road school', '1 - 6', 'coed', 'papakura', 'state', '1', '459'], ['kereru park campus', '1 - 8', 'coed', 'papakura', 'state', '1', '76'], ['mansell senior school', '7 - 8', 'coed', 'papakura', 'state', '1', '149'], ['opaheke school', '1 - 8', 'coed', 'papakura', 'state', '5', '605'], ['papakura central school', '1 - 6', 'coed', 'papakura', 'state', '5', '347'], ['papakura normal school', '1 - 8', 'coed', 'papakura', 'state', '3', '651'], ['park estate school', '1 - 6', 'coed', 'papakura', 'state', '1', '112'], ['redhill school', '1 - 8', 'coed', 'papakura', 'state', '1', '199'], ['rosehill intermediate', '7 - 8', 'coed', 'papakura', 'state', '4', '368'], ["st mary 's catholic school", '1 - 8', 'coed', 'papakura', 'state integrated', '4', '281'], ['takanini school', '1 - 8', 'coed', 'takanini', 'state', '1', '442']]

women 's british open

https://en.wikipedia.org/wiki/Women%27s_British_Open

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1520559-2.html.csv

count

karrie webb was the champion of the women 's british open two different times .

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

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

eq { count { filter_eq { all_rows ; champion ; karrie webb } } ; 2 } = true

select the rows whose champion record fuzzily matches to karrie webb . 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, 'champion_5': 5, 'karrie webb_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', 'champion_5': 'champion', 'karrie webb_6': 'karrie webb', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'champion_5': [0], 'karrie webb_6': [0], '2_7': [2]}

['year', 'date', 'venue', 'champion', 'country', 'score', 'to par', 'margin of victory', 'purse', "winner 's share"]

[['2000', 'aug 17 - 20', 'royal birkdale golf club', 'sophie gustafson', 'sweden', '282', '- 6', '2 strokes', '1250000', '178000'], ['1999', 'aug 12 - 15', 'woburn golf and country club', 'sherri steinhauer', 'united states', '283', '- 5', '1 stroke', '1000000', '160000'], ['1998', 'aug 13 - 16', 'royal lytham & st annes golf club', 'sherri steinhauer', 'united states', '292', '+ 4', '1 stroke', '1000000', '162000'], ['1997', 'aug 14 - 17', 'sunningdale golf club', 'karrie webb', 'australia', '269', '- 19', '8 strokes', '900000', '129938'], ['1996', 'aug 15 - 18', 'woburn golf and country club', 'emilee klein', 'united states', '277', '- 11', '7 strokes', '850000', '124000'], ['1995', 'aug 17 - 20', 'woburn golf and country club', 'karrie webb', 'australia', '278', '- 10', '6 strokes', '600000', '92400'], ['1994', 'aug 11 - 14', 'woburn golf and country club', 'liselotte neumann', 'sweden', '280', '- 8', '3 strokes', '500000', '80325']]

2005 open championship

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16225902-7.html.csv

unique

vijay singh was the only player to represent fiji at the 2005 open championship .

{'scope': 'all', 'row': '10', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'fiji', 'subset': None}

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

and { only { filter_eq { all_rows ; country ; fiji } } ; eq { hop { filter_eq { all_rows ; country ; fiji } ; player } ; vijay singh } } = true

select the rows whose country record fuzzily matches to fiji . there is only one such row in the table . the player record of this unqiue row is vijay singh .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'country_7': 7, 'fiji_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'vijay singh_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'country_7': 'country', 'fiji_8': 'fiji', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'vijay singh_10': 'vijay singh'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'country_7': [0], 'fiji_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'vijay singh_10': [3]}

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

[['1', 'tiger woods', 'united states', '66 + 67 + 71 + 70 = 274', '14', '720000'], ['2', 'colin montgomerie', 'scotland', '71 + 66 + 70 + 72 = 279', '9', '430000'], ['t3', 'fred couples', 'united states', '68 + 71 + 73 + 68 = 280', '8', '242350'], ['t3', 'josé maría olazábal', 'spain', '68 + 70 + 68 + 74 = 280', '8', '242350'], ['t5', 'michael campbell', 'new zealand', '69 + 72 + 68 + 72 = 281', '7', '122100'], ['t5', 'sergio garcía', 'spain', '70 + 69 + 69 + 73 = 281', '7', '122100'], ['t5', 'retief goosen', 'south africa', '68 + 73 + 66 + 74 = 281', '7', '122100'], ['t5', 'bernhard langer', 'germany', '71 + 69 + 70 + 71 = 281', '7', '122100'], ['t5', 'geoff ogilvy', 'australia', '71 + 74 + 67 + 69 = 281', '7', '122100'], ['t5', 'vijay singh', 'fiji', '69 + 69 + 71 + 72 = 281', '7', '122100']]

ohio high school athletic association

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2849652-2.html.csv

comparative

the soccer team of the ohio high school athletic association had his first tournament before the bowling team .

{'row_1': '3', 'row_2': '4', 'col': '5', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'sport', 'soccer'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose sport record fuzzily matches to soccer .', 'tostr': 'filter_eq { all_rows ; sport ; soccer }'}, '1st tournament'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; sport ; soccer } ; 1st tournament }', 'tointer': 'select the rows whose sport record fuzzily matches to soccer . take the 1st tournament record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'sport', 'bowling'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose sport record fuzzily matches to bowling .', 'tostr': 'filter_eq { all_rows ; sport ; bowling }'}, '1st tournament'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; sport ; bowling } ; 1st tournament }', 'tointer': 'select the rows whose sport record fuzzily matches to bowling . take the 1st tournament record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; sport ; soccer } ; 1st tournament } ; hop { filter_eq { all_rows ; sport ; bowling } ; 1st tournament } } = true', 'tointer': 'select the rows whose sport record fuzzily matches to soccer . take the 1st tournament record of this row . select the rows whose sport record fuzzily matches to bowling . take the 1st tournament record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; sport ; soccer } ; 1st tournament } ; hop { filter_eq { all_rows ; sport ; bowling } ; 1st tournament } } = true

select the rows whose sport record fuzzily matches to soccer . take the 1st tournament record of this row . select the rows whose sport record fuzzily matches to bowling . take the 1st tournament 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, 'sport_7': 7, 'soccer_8': 8, '1st tournament_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'sport_11': 11, 'bowling_12': 12, '1st tournament_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', 'sport_7': 'sport', 'soccer_8': 'soccer', '1st tournament_9': '1st tournament', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'sport_11': 'sport', 'bowling_12': 'bowling', '1st tournament_13': '1st tournament'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'sport_7': [0], 'soccer_8': [0], '1st tournament_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'sport_11': [1], 'bowling_12': [1], '1st tournament_13': [3]}

['season', 'sport', 'tournament structure', 'of divisions', '1st tournament', '2011 - 2012 state tournament location']

[['fall', 'cross country', 'district , regional , & state', '3', '1978', 'national trail raceway , hebron'], ['fall', 'field hockey', 'state qualifying & state', '1', '1979', 'upper arlington high school , upper arlington'], ['fall', 'soccer', 'sectional , district , regional & state', '3', '1985', 'crew stadium , columbus'], ['winter', 'bowling', 'sectional , district , & state', '2', '2007', "wayne webb 's columbus bowl , columbus"], ['winter', 'gymnastics', 'sectional , district , & state', '1', '1977', 'hilliard bradley high school , hilliard'], ['winter', 'swimming and diving', 'sectional , district , & state', '2', '1977', 'branin natatorium , canton']]

list of england national rugby union team results 1960 - 69

https://en.wikipedia.org/wiki/List_of_England_national_rugby_union_team_results_1960%E2%80%9369

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18179114-2.html.csv

majority

most of the games of the england national rugby union team in 1961 were held in twickenham , london .

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

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

most_eq { all_rows ; venue ; twickenham , london } = true

for the venue records of all rows , most of them fuzzily match to twickenham , london .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'venue_3': 3, 'twickenham , london_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'venue_3': 'venue', 'twickenham , london_4': 'twickenham , london'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'venue_3': [0], 'twickenham , london_4': [0]}

['opposing teams', 'against', 'date', 'venue', 'status']

[['south africa', '5', '07 / 01 / 1961', 'twickenham , london', 'test match'], ['wales', '6', '21 / 01 / 1961', 'cardiff arms park , cardiff', 'five nations'], ['ireland', '11', '11 / 02 / 1961', 'lansdowne road , dublin', 'five nations'], ['france', '5', '25 / 02 / 1961', 'twickenham , london', 'five nations'], ['scotland', '0', '18 / 03 / 1961', 'twickenham , london', 'five nations']]

1972 vfl season

https://en.wikipedia.org/wiki/1972_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10826385-21.html.csv

majority

all of the matches of the 1972 vfl season took place on 26 august 1972 .

{'scope': 'all', 'col': '7', 'most_or_all': 'all', 'criterion': 'equal', 'value': '26 august 1972', 'subset': None}

{'func': 'all_str_eq', 'args': ['all_rows', 'date', '26 august 1972'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to 26 august 1972 .', 'tostr': 'all_eq { all_rows ; date ; 26 august 1972 } = true'}

all_eq { all_rows ; date ; 26 august 1972 } = true

for the date records of all rows , all of them fuzzily match to 26 august 1972 .

1

1

{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, '26 august 1972_4': 4}

{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', '26 august 1972_4': '26 august 1972'}

{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], '26 august 1972_4': [0]}

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

[['melbourne', '17.22 ( 124 )', 'north melbourne', '14.7 ( 91 )', 'mcg', '11241', '26 august 1972'], ['footscray', '17.21 ( 123 )', 'richmond', '18.17 ( 125 )', 'western oval', '18117', '26 august 1972'], ['collingwood', '22.17 ( 149 )', 'south melbourne', '11.6 ( 72 )', 'victoria park', '19934', '26 august 1972'], ['carlton', '24.12 ( 156 )', 'hawthorn', '11.22 ( 88 )', 'princes park', '32048', '26 august 1972'], ['fitzroy', '14.20 ( 104 )', 'essendon', '18.11 ( 119 )', 'junction oval', '17252', '26 august 1972'], ['st kilda', '11.10 ( 76 )', 'geelong', '8.13 ( 61 )', 'vfl park', '25663', '26 august 1972']]

list of windmills in belgium

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18843333-3.html.csv

count

only three of the windmills are believed to have been built during the 18th century .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': '19th', 'result': '4', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'built', '19th'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose built record fuzzily matches to 19th .', 'tostr': 'filter_eq { all_rows ; built ; 19th }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; built ; 19th } }', 'tointer': 'select the rows whose built record fuzzily matches to 19th . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; built ; 19th } } ; 4 } = true', 'tointer': 'select the rows whose built record fuzzily matches to 19th . the number of such rows is 4 .'}

eq { count { filter_eq { all_rows ; built ; 19th } } ; 4 } = true

select the rows whose built record fuzzily matches to 19th . the number of such rows is 4 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'built_5': 5, '19th_6': 6, '4_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'built_5': 'built', '19th_6': '19th', '4_7': '4'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'built_5': [0], '19th_6': [0], '4_7': [2]}

['location', 'name of mill', 'type', 'built', 'notes']

[['andenne', 'molen de stud', 'grondzeiler', '18th century', 'molenechos ( dutch )'], ['belgrade', 'moulin massinon', 'grondzeiler', '1828', 'molenechos ( dutch )'], ['frasnes - lez - couvin', 'moulin tromcourt moulin de géronsart', 'grondzeiler', 'early 19th century', 'molenechos ( dutch )'], ['gembloux - sur - orneau', 'moulin staquet moulin créton', 'grondzeiler', '19th century', 'molenechos ( dutch )'], ['grand - leez', 'moulin defrenne', 'grondzeiler', '1830', 'molenechos ( dutch )'], ['grand leez', 'moulin lorge', 'grondzeiler', '19th century', 'molenechos ( dutch )'], ['mettet', 'moulin de scry vieux moulin', 'grondzeiler', '1792', 'molenechos ( dutch )'], ['sauvenière', 'moulin michaux', 'grondzeiler', '1802', 'molenechos ( dutch )'], ['tongrinne', 'moulin de tongrinne', 'grondzeiler', '19th century', 'molenechos ( dutch )'], ['velaine - sur - sambre', 'moulin des golettes', 'grondzeiler', 'late 18th century', 'molenechos ( dutch )']]

bobby riggs

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17188888-2.html.csv

ordinal

the first time bobby riggs lost a pro championship was in 1942 against don budge .

{'row': '1', 'col': '2', 'order': '1', 'col_other': '5', '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', 'year', '1'], 'result': '1942', 'ind': 0, 'tostr': 'nth_min { all_rows ; year ; 1 }', 'tointer': 'the 1st minimum year record of all rows is 1942 .'}, '1942'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; year ; 1 } ; 1942 }', 'tointer': 'the 1st minimum year record of all rows is 1942 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'year', '1'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; year ; 1 }'}, 'opponent in the final'], 'result': 'don budge', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; year ; 1 } ; opponent in the final }'}, 'don budge'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; year ; 1 } ; opponent in the final } ; don budge }', 'tointer': 'the opponent in the final record of the row with 1st minimum year record is don budge .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { nth_min { all_rows ; year ; 1 } ; 1942 } ; eq { hop { nth_argmin { all_rows ; year ; 1 } ; opponent in the final } ; don budge } } = true', 'tointer': 'the 1st minimum year record of all rows is 1942 . the opponent in the final record of the row with 1st minimum year record is don budge .'}

and { eq { nth_min { all_rows ; year ; 1 } ; 1942 } ; eq { hop { nth_argmin { all_rows ; year ; 1 } ; opponent in the final } ; don budge } } = true

the 1st minimum year record of all rows is 1942 . the opponent in the final record of the row with 1st minimum year record is don budge .

6

6

{'and_5': 5, 'result_6': 6, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_7': 7, 'year_8': 8, '1_9': 9, '1942_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_11': 11, 'year_12': 12, '1_13': 13, 'opponent in the final_14': 14, 'don budge_15': 15}

{'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_7': 'all_rows', 'year_8': 'year', '1_9': '1', '1942_10': '1942', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_11': 'all_rows', 'year_12': 'year', '1_13': '1', 'opponent in the final_14': 'opponent in the final', 'don budge_15': 'don budge'}

{'and_5': [6], 'result_6': [], 'eq_1': [5], 'nth_min_0': [1], 'all_rows_7': [0], 'year_8': [0], '1_9': [0], '1942_10': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'nth_argmin_2': [3], 'all_rows_11': [2], 'year_12': [2], '1_13': [2], 'opponent in the final_14': [3], 'don budge_15': [4]}

['outcome', 'year', 'pro slam championship', 'surface', 'opponent in the final', 'score in the final']

[['runner - up', '1942', 'us pro', 'grass', 'don budge', '2 - 6 , 2 - 6 , 2 - 6'], ['winner', '1946', 'us pro', 'grass', 'don budge', '6 - 3 , 6 - 1 , 6 - 1'], ['winner', '1947', 'us pro', 'grass', 'don budge', '3 - 6 , 6 - 3 , 10 - 8 , 4 - 6 , 6 - 3'], ['runner - up', '1948', 'us pro', 'grass', 'jack kramer', '12 - 14 , 2 - 6 , 6 - 3 , 3 - 6'], ['runner - up', '1949', 'wembley pro', 'indoor', 'jack kramer', '6 - 2 , 4 - 6 , 3 - 6 , 4 - 6'], ['winner', '1949', 'us pro', 'grass', 'don budge', '9 - 7 , 3 - 6 , 6 - 3 , 7 - 5']]

1984 senior pga tour

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11622840-4.html.csv

majority

most of the players in the 1984 pga tour earned over 300,000 dollars .

{'scope': 'all', 'col': '4', 'most_or_all': 'all', 'criterion': 'greater_than', 'value': '300,000', 'subset': None}

{'func': 'all_greater', 'args': ['all_rows', 'earnings', '300,000'], 'result': True, 'ind': 0, 'tointer': 'for the earnings records of all rows , all of them are greater than 300,000 .', 'tostr': 'all_greater { all_rows ; earnings ; 300,000 } = true'}

all_greater { all_rows ; earnings ; 300,000 } = true

for the earnings records of all rows , all of them are greater than 300,000 .

1

1

{'all_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'earnings_3': 3, '300,000_4': 4}

{'all_greater_0': 'all_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'earnings_3': 'earnings', '300,000_4': '300,000'}

{'all_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'earnings_3': [0], '300,000_4': [0]}

['rank', 'player', 'country', 'earnings', 'wins']

[['1', 'don january', 'united states', '791990', '14'], ['2', 'miller barber', 'united states', '720134', '14'], ['3', 'arnold palmer', 'united states', '442974', '8'], ['4', 'billy casper', 'united states', '395386', '4'], ['5', 'gene littler', 'united states', '358770', '3']]

iberian peninsula

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14883-2.html.csv

superlative

the highest populated urban area on the iberian peninsula is madrid .

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

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

eq { hop { argmax { all_rows ; population } ; urban area } ; madrid } = true

select the row whose population record of all rows is maximum . the urban area record of this row is madrid .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'population_5': 5, 'urban area_6': 6, 'madrid_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'population_5': 'population', 'urban area_6': 'urban area', 'madrid_7': 'madrid'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'population_5': [0], 'urban area_6': [1], 'madrid_7': [2]}

['urban area', 'country', 'region', 'population', 'globalization index']

[['madrid', 'spain', 'community of madrid', '6321398', 'alpha'], ['barcelona', 'spain', 'catalonia', '4604000', 'alpha -'], ['lisbon', 'portugal', 'lisbon region', '3035000', 'alpha -'], ['porto', 'portugal', 'norte region', '1676848', 'gamma -'], ['valencia', 'spain', 'community of valencia', '1564145', 'gamma']]

2005 world weightlifting championships

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10929638-3.html.csv

comparative

in the 2005 world weightlifting championships , russia won less total medals than china .

{'row_1': '2', 'row_2': '1', 'col': '6', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'russia'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nation record fuzzily matches to russia .', 'tostr': 'filter_eq { all_rows ; nation ; russia }'}, 'total'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nation ; russia } ; total }', 'tointer': 'select the rows whose nation record fuzzily matches to russia . take the total record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'china'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nation record fuzzily matches to china .', 'tostr': 'filter_eq { all_rows ; nation ; china }'}, 'total'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; nation ; china } ; total }', 'tointer': 'select the rows whose nation record fuzzily matches to china . take the total record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; nation ; russia } ; total } ; hop { filter_eq { all_rows ; nation ; china } ; total } } = true', 'tointer': 'select the rows whose nation record fuzzily matches to russia . take the total record of this row . select the rows whose nation record fuzzily matches to china . take the total record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; nation ; russia } ; total } ; hop { filter_eq { all_rows ; nation ; china } ; total } } = true

select the rows whose nation record fuzzily matches to russia . take the total record of this row . select the rows whose nation record fuzzily matches to china . take the total 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, 'nation_7': 7, 'russia_8': 8, 'total_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'nation_11': 11, 'china_12': 12, 'total_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', 'nation_7': 'nation', 'russia_8': 'russia', 'total_9': 'total', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'nation_11': 'nation', 'china_12': 'china', 'total_13': 'total'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'nation_7': [0], 'russia_8': [0], 'total_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'nation_11': [1], 'china_12': [1], 'total_13': [3]}

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

[['1', 'china', '7', '4', '1', '12'], ['2', 'russia', '2', '4', '4', '10'], ['3', 'thailand', '1', '3', '1', '5'], ['4', 'south korea', '1', '2', '0', '2'], ['5', 'azerbaijan', '1', '0', '0', '1'], ['5', 'chinese taipei', '1', '0', '0', '1'], ['5', 'iran', '1', '0', '0', '1'], ['5', 'kazakhstan', '1', '0', '0', '1'], ['9', 'romania', '0', '1', '1', '2'], ['10', 'moldova', '0', '1', '0', '1'], ['11', 'qatar', '0', '0', '2', '2'], ['12', 'bulgaria', '0', '0', '1', '1'], ['12', 'dominican republic', '0', '0', '1', '1'], ['12', 'france', '0', '0', '1', '1'], ['12', 'slovakia', '0', '0', '1', '1'], ['12', 'united states', '0', '0', '1', '1'], ['12', 'vietnam', '0', '0', '1', '1'], ['total', 'total', '15', '15', '15', '45']]

list of ngc objects ( 5001 - 6000 )

https://en.wikipedia.org/wiki/List_of_NGC_objects_%285001%E2%80%936000%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11051845-9.html.csv

unique

only object 5877 has the object type " triple star . " .

{'scope': 'all', 'row': '9', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'triple star', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'object type', 'triple star'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose object type record fuzzily matches to triple star .', 'tostr': 'filter_eq { all_rows ; object type ; triple star }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; object type ; triple star } }', 'tointer': 'select the rows whose object type record fuzzily matches to triple star . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'object type', 'triple star'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose object type record fuzzily matches to triple star .', 'tostr': 'filter_eq { all_rows ; object type ; triple star }'}, 'ngc number'], 'result': '5877', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; object type ; triple star } ; ngc number }'}, '5877'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; object type ; triple star } ; ngc number } ; 5877 }', 'tointer': 'the ngc number record of this unqiue row is 5877 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; object type ; triple star } } ; eq { hop { filter_eq { all_rows ; object type ; triple star } ; ngc number } ; 5877 } } = true', 'tointer': 'select the rows whose object type record fuzzily matches to triple star . there is only one such row in the table . the ngc number record of this unqiue row is 5877 .'}

and { only { filter_eq { all_rows ; object type ; triple star } } ; eq { hop { filter_eq { all_rows ; object type ; triple star } ; ngc number } ; 5877 } } = true

select the rows whose object type record fuzzily matches to triple star . there is only one such row in the table . the ngc number record of this unqiue row is 5877 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'object type_7': 7, 'triple star_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'ngc number_9': 9, '5877_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'object type_7': 'object type', 'triple star_8': 'triple star', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'ngc number_9': 'ngc number', '5877_10': '5877'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'object type_7': [0], 'triple star_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'ngc number_9': [2], '5877_10': [3]}

['ngc number', 'object type', 'constellation', 'right ascension ( j2000 )', 'declination ( j2000 )']

[['5822', 'open cluster', 'lupus', '15h04 m', 'degree24 ′'], ['5823', 'open cluster', 'circinus', '15h05 m44 .8 s', 'degree37 ′ 30 ″'], ['5824', 'globular cluster', 'lupus', '15h03 m58 .5 s', 'degree04 ′ 04 ″'], ['5825', 'elliptical galaxy', 'boötes', '14h54 m31 .5 s', 'degree38 ′ 31 ″'], ['5838', 'lenticular galaxy', 'virgo', '15h05 m26 .3 s', 'degree05 ′ 57 ″'], ['5846', 'elliptical galaxy', 'virgo', '15h06 m29 .4 s', 'degree36 ′ 19 ″'], ['5850', 'spiral galaxy', 'virgo', '15h07 m07 .8 s', 'degree32 ′ 39 ″'], ['5866', 'lenticular galaxy', 'draco', '15h06 m29 .5 s', 'degree45 ′ 47 ″'], ['5877', 'triple star', 'lupus', '15h12 m53 .1 s', 'degree55 ′ 38 ″'], ['5879', 'galaxy', 'draco', '15h09 m46 .8 s', 'degree00 ′ 01 ″'], ['5882', 'planetary nebula', 'libra', '15h16 m49 .9 s', 'degree38 ′ 58 ″'], ['5885', 'barred spiral galaxy', 'libra', '15h15 m04 .1 s', 'degree05 ′ 10.0 ″'], ['5886', 'elliptical galaxy', 'boötes', '15h12 m45 .4 s', 'degree12 ′ 02 ″'], ['5888', 'barred spiral galaxy', 'boötes', '15h13 m07 .4 s', 'degree15 ′ 52 ″'], ['5890', 'lenticular galaxy', 'libra', '15h17 m51 .1 s', 'degree35 ′ 19 ″']]

2007 - 08 carolina hurricanes season

https://en.wikipedia.org/wiki/2007%E2%80%9308_Carolina_Hurricanes_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11772511-6.html.csv

count

in the 2007 - 08 carolina hurricanes season , among the games where carolina was a visitor , 3 of them had attendance over 19,000 .

{'scope': 'subset', 'criterion': 'greater_than', 'value': '19000', 'result': '3', 'col': '6', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'carolina'}}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'visitor', 'carolina'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; visitor ; carolina }', 'tointer': 'select the rows whose visitor record fuzzily matches to carolina .'}, 'attendance', '19000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose visitor record fuzzily matches to carolina . among these rows , select the rows whose attendance record is greater than 19000 .', 'tostr': 'filter_greater { filter_eq { all_rows ; visitor ; carolina } ; attendance ; 19000 }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_greater { filter_eq { all_rows ; visitor ; carolina } ; attendance ; 19000 } }', 'tointer': 'select the rows whose visitor record fuzzily matches to carolina . among these rows , select the rows whose attendance record is greater than 19000 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_eq { all_rows ; visitor ; carolina } ; attendance ; 19000 } } ; 3 } = true', 'tointer': 'select the rows whose visitor record fuzzily matches to carolina . among these rows , select the rows whose attendance record is greater than 19000 . the number of such rows is 3 .'}

eq { count { filter_greater { filter_eq { all_rows ; visitor ; carolina } ; attendance ; 19000 } } ; 3 } = true

select the rows whose visitor record fuzzily matches to carolina . among these rows , select the rows whose attendance record is greater than 19000 . the number of such rows is 3 .

4

4

{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'visitor_6': 6, 'carolina_7': 7, 'attendance_8': 8, '19000_9': 9, '3_10': 10}

{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_greater_1': 'filter_greater', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'visitor_6': 'visitor', 'carolina_7': 'carolina', 'attendance_8': 'attendance', '19000_9': '19000', '3_10': '3'}

{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'visitor_6': [0], 'carolina_7': [0], 'attendance_8': [1], '19000_9': [1], '3_10': [3]}

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

[['january 2', 'atlanta', '5 - 4', 'carolina', 'ward', '13506', '20 - 18 - 4'], ['january 4', 'carolina', '4 - 3', 'atlanta', 'leighton', '16097', '21 - 18 - 4'], ['january 5', 'carolina', '0 - 1', 'st louis', 'leighton', '19150', '21 - 19 - 4'], ['january 8', 'carolina', '1 - 0', 'boston', 'ward', '14549', '22 - 19 - 4'], ['january 10', 'new jersey', '4 - 1', 'carolina', 'ward', '17173', '22 - 20 - 4'], ['january 12', 'colorado', '5 - 4', 'carolina', 'ward', '18680', '22 - 21 - 4'], ['january 15', 'carolina', '4 - 5', 'toronto', 'ward', '19444', '22 - 22 - 4'], ['january 17', 'carolina', '1 - 5', 'ottawa', 'ward', '19720', '22 - 23 - 4'], ['january 18', 'edmonton', '2 - 7', 'carolina', 'ward', '16868', '23 - 23 - 4'], ['january 21', 'carolina', '3 - 2', 'ny islanders', 'ward', '16234', '24 - 23 - 4'], ['january 22', 'ny islanders', '6 - 3', 'carolina', 'ward', '15675', '24 - 24 - 4'], ['january 29', 'ny rangers', '1 - 3', 'carolina', 'ward', '17793', '25 - 24 - 4'], ['january 31', 'toronto', '2 - 3', 'carolina', 'ward', '15159', '26 - 24 - 4']]

1972 vfl season

https://en.wikipedia.org/wiki/1972_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10826385-8.html.csv

comparative

south melbourne had a higher home team score than north melbourne in the 1972 vfl season .

{'row_1': '3', 'row_2': '4', 'col': '2', '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', 'home team', 'south melbourne'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose home team record fuzzily matches to south melbourne .', 'tostr': 'filter_eq { all_rows ; home team ; south melbourne }'}, 'home team score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; home team ; south melbourne } ; home team score }', 'tointer': 'select the rows whose home team record fuzzily matches to south melbourne . take the home team score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'north melbourne'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home team record fuzzily matches to north melbourne .', 'tostr': 'filter_eq { all_rows ; home team ; north melbourne }'}, 'home team score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score }', 'tointer': 'select the rows whose home team record fuzzily matches to north melbourne . take the home team score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; home team ; south melbourne } ; home team score } ; hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score } } = true', 'tointer': 'select the rows whose home team record fuzzily matches to south melbourne . take the home team score record of this row . select the rows whose home team record fuzzily matches to north melbourne . take the home team score record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; home team ; south melbourne } ; home team score } ; hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score } } = true

select the rows whose home team record fuzzily matches to south melbourne . take the home team score record of this row . select the rows whose home team record fuzzily matches to north melbourne . take the home team score record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'home team_7': 7, 'south melbourne_8': 8, 'home team score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'home team_11': 11, 'north melbourne_12': 12, 'home team score_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'home team_7': 'home team', 'south melbourne_8': 'south melbourne', 'home team score_9': 'home team score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'home team_11': 'home team', 'north melbourne_12': 'north melbourne', 'home team score_13': 'home team score'}

{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'home team_7': [0], 'south melbourne_8': [0], 'home team score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'home team_11': [1], 'north melbourne_12': [1], 'home team score_13': [3]}

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

[['collingwood', '7.14 ( 56 )', 'footscray', '11.14 ( 80 )', 'victoria park', '25986', '20 may 1972'], ['melbourne', '20.14 ( 134 )', 'geelong', '14.17 ( 101 )', 'mcg', '19023', '20 may 1972'], ['south melbourne', '9.7 ( 61 )', 'fitzroy', '18.11 ( 119 )', 'lake oval', '12421', '20 may 1972'], ['north melbourne', '8.13 ( 61 )', 'essendon', '14.12 ( 96 )', 'arden street oval', '14091', '20 may 1972'], ['st kilda', '10.12 ( 72 )', 'carlton', '14.15 ( 99 )', 'moorabbin oval', '31547', '20 may 1972'], ['richmond', '11.25 ( 91 )', 'hawthorn', '13.6 ( 84 )', 'vfl park', '25845', '20 may 1972']]

2007 - 08 detroit pistons season

https://en.wikipedia.org/wiki/2007%E2%80%9308_Detroit_Pistons_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11960944-11.html.csv

majority

all games of the detroit pistons ' in the 2007 - 08 season were played in the month of may .

{'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'fuzzily_match', 'value': 'may', 'subset': None}

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

all_eq { all_rows ; date ; may } = true

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

1

1

{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, 'may_4': 4}

{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', 'may_4': 'may'}

{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], 'may_4': [0]}

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

[['1', 'may 20', 'boston', 'l 88 - 79', 'prince ( 16 )', 'mcdyess ( 11 )', 'wallace ( 4 )', 'td banknorth garden 18624', '0 - 1'], ['2', 'may 22', 'boston', 'w 103 - 97', 'hamilton ( 25 )', 'wallace ( 10 )', 'billups ( 7 )', 'td banknorth garden 18624', '1 - 1'], ['3', 'may 24', 'boston', 'l 94 - 80', 'hamilton ( 26 )', 'mcdyess , wallace ( 8 )', 'billups , stuckey ( 4 )', 'the palace of auburn hills 22076', '1 - 2'], ['4', 'may 26', 'boston', 'w 94 - 75', 'mcdyess ( 21 )', 'mcdyess ( 17 )', 'hamilton ( 7 )', 'the palace of auburn hills 22076', '2 - 2'], ['5', 'may 28', 'boston', 'l 106 - 102', 'billups ( 26 )', 'billups , mcdyess ( 5 )', 'billups , hamilton ( 6 )', 'td banknorth garden 18624', '2 - 3']]

doctor who merchandise

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1713215-1.html.csv

unique

mission to venus is the only title in the doctor who merchandise series with an unknown isbn us value .

{'scope': 'all', 'row': '4', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'unknown', 'subset': None}

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

and { only { filter_eq { all_rows ; isbn us ; unknown } } ; eq { hop { filter_eq { all_rows ; isbn us ; unknown } ; title } ; mission to venus } } = true

select the rows whose isbn us record fuzzily matches to unknown . there is only one such row in the table . the title record of this unqiue row is mission to venus .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'isbn us_7': 7, 'unknown_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'title_9': 9, 'mission to venus_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'isbn us_7': 'isbn us', 'unknown_8': 'unknown', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'title_9': 'title', 'mission to venus_10': 'mission to venus'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'isbn us_7': [0], 'unknown_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'title_9': [2], 'mission to venus_10': [3]}

['title', 'author', 'isbn uk', 'isbn us', 'tv companions featured']

[['search for the doctor', 'dave martin', 'isbn 0 - 7278 - 2087 - 7', 'isbn 0 - 345 - 33224 - 5', 'k - 9'], ['crisis in space', 'michael holt', 'isbn 0 - 7278 - 2093 - 1', 'isbn 0 - 345 - 33225 - 3', 'peri brown , vislor turlough'], ['the garden of evil', 'dave martin', 'isbn 0 - 7278 - 2113 - x', 'n / a', 'none'], ['mission to venus', 'william emms', 'isbn 0 - 7278 - 2122 - 9', 'unknown', 'peri brown'], ['invasion of the ormazoids', 'philip martin', 'isbn 0 - 7278 - 2100 - 8', 'isbn 0 - 345 - 33231 - 8', 'none'], ['race against time', 'pip and jane baker', 'isbn 0 - 7278 - 2116 - 4', 'isbn 0 - 345 - 33228 - 8', 'peri brown']]

1973 ohio state buckeyes football team

https://en.wikipedia.org/wiki/1973_Ohio_State_Buckeyes_football_team

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17824926-1.html.csv

majority

the ohio state buckeyes team won all their games in the month of october during the 1973 football season .

{'scope': 'subset', 'col': '5', 'most_or_all': 'all', 'criterion': 'fuzzily_match', 'value': 'w', 'subset': {'col': '1', 'criterion': 'fuzzily_match', 'value': 'october'}}

{'func': 'all_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', 'w'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to october . for the result records of these rows , all of them fuzzily match to w .', 'tostr': 'all_eq { filter_eq { all_rows ; date ; october } ; result ; w } = true'}

all_eq { filter_eq { all_rows ; date ; october } ; result ; w } = true

select the rows whose date record fuzzily matches to october . for the result records of these rows , all of them fuzzily match to w .

2

2

{'all_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, 'w_7': 7}

{'all_str_eq_1': 'all_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', 'w_7': 'w'}

{'all_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'date_4': [0], 'october_5': [0], 'result_6': [1], 'w_7': [1]}

['date', 'opponent', 'rank', 'site', 'result', 'attendance']

[['september 15', 'minnesota', '3', 'ohio stadium columbus , oh', 'w56 - 7', '86005'], ['september 29', 'tcu', '3', 'ohio stadium columbus , oh', 'w37 - 3', '87439'], ['october 6', 'washington state', '1', 'ohio stadium columbus , oh', 'w27 - 3', '87425'], ['october 13', 'wisconsin', '1', 'camp randall stadium madison , wi', 'w24 - 0', '77413'], ['october 20', 'indiana', '1', 'memorial stadium bloomington , in', 'w37 - 7', '53183'], ['october 27', 'northwestern', '1', 'ohio stadium columbus , oh', 'w60 - 0', '87453'], ['november 3', 'illinois', '1', 'memorial stadium champaign , il', 'w30 - 0', '60707'], ['november 10', 'michigan state', '1', 'ohio stadium columbus , oh', 'w35 - 0', '87600'], ['november 17', 'iowa', '1', 'ohio stadium columbus , oh', 'w55 - 13', '87447'], ['november 24', '4 michigan', '1', 'michigan stadium ann arbor , mi', 't 10 - 10', '105223'], ['january 1', '7 usc', '4', 'rose bowl pasadena , ca ( rose bowl )', 'w42 - 21', '105267']]

richard gasquet

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1750635-8.html.csv

majority

a majority of richard gasquet 's matches took place on hard court .

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

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

most_eq { all_rows ; surface ; hard } = true

for the surface records of all rows , most of them fuzzily match to hard .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'hard_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'hard_4': 'hard'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'hard_4': [0]}

['outcome', 'date', 'championship', 'surface', 'opponent', 'score']

[['runner - up', '11 october 2004', 'open de moselle , metz , france', 'hard ( i )', 'jérôme haehnel', '6 - 7 ( 9 - 11 ) , 4 - 6'], ['runner - up', '9 may 2005', 'international german open , hamburg , germany', 'clay', 'roger federer', '3 - 6 , 5 - 7 , 6 - 7 ( 4 - 7 )'], ['winner', '13 june 2005', 'nottingham open , nottingham , united kingdom', 'grass', 'max mirnyi', '6 - 2 , 6 - 3'], ['winner', '19 june 2006', 'nottingham open , nottingham , united kingdom ( 2 )', 'grass', 'jonas björkman', '6 - 4 , 6 - 3'], ['winner', '10 july 2006', 'allianz suisse open gstaad , gstaad , switzerland', 'clay', 'feliciano lópez', '7 - 6 ( 7 - 4 ) , 6 - 7 ( 3 - 7 ) , 6 - 3'], ['runner - up', '7 august 2006', 'canada masters , toronto , canada', 'hard', 'roger federer', '6 - 2 , 3 - 6 , 2 - 6'], ['winner', '23 october 2006', 'grand prix de tennis de lyon , lyon , france', 'carpet ( i )', 'marc gicquel', '6 - 3 , 6 - 1'], ['runner - up', '29 april 2007', 'estoril open , estoril , portugal', 'clay', 'novak djokovic', '6 - 7 ( 7 - 9 ) , 6 - 0 , 1 - 6'], ['winner', '30 september 2007', 'atp mumbai , bombay , india', 'hard', 'olivier rochus', '6 - 3 , 6 - 4'], ['runner - up', '1 october 2007', 'aig japan open tennis championships , tokyo , japan', 'hard', 'david ferrer', '1 - 6 , 2 - 6'], ['runner - up', '13 july 2008', 'mercedes cup , stuttgart , germany', 'clay', 'juan martín del potro', '4 - 6 , 5 - 7'], ['runner - up', '16 january 2010', 'medibank international , sydney , australia', 'hard', 'marcos baghdatis', '4 - 6 , 6 - 7 ( 2 - 7 )'], ['winner', '22 may 2010', "open de nice côte d'azur , nice , france", 'clay', 'fernando verdasco', '6 - 3 , 5 - 7 , 7 - 6 ( 7 - 5 )'], ['runner - up', '1 august 2010', 'allianz suisse open gstaad , gstaad , switzerland', 'clay', 'nicolás almagro', '5 - 7 , 1 - 6'], ['runner - up', '6 may 2012', 'estoril open , estoril , portugal ( 2 )', 'clay', 'juan martín del potro', '4 - 6 , 2 - 6'], ['runner - up', '12 august 2012', 'toronto masters , toronto , canada ( 2 )', 'hard', 'novak djokovic', '3 - 6 , 2 - 6'], ['winner', '30 september 2012', 'ptt thailand open , bangkok , thailand', 'hard ( i )', 'gilles simon', '6 - 2 , 6 - 1'], ['winner', '5 january 2013', 'qatar open , doha , qatar', 'hard', 'nikolay davydenko', '3 - 6 , 7 - 6 ( 7 - 4 ) , 6 - 3'], ['winner', '10 february 2013', 'open sud de france , montpellier , france', 'hard ( i )', 'benoît paire', '6 - 2 , 6 - 3'], ['winner', '20 october 2013', 'kremlin cup , moscow , russia', 'hard ( i )', 'mikhail kukushkin', '4 - 6 , 6 - 4 , 6 - 4']]

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

unique

the only game johnson was the decision was november 6 .

{'scope': 'all', 'row': '4', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'johnson', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'decision', 'johnson'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose decision record fuzzily matches to johnson .', 'tostr': 'filter_eq { all_rows ; decision ; johnson }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; decision ; johnson } }', 'tointer': 'select the rows whose decision record fuzzily matches to johnson . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'decision', 'johnson'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose decision record fuzzily matches to johnson .', 'tostr': 'filter_eq { all_rows ; decision ; johnson }'}, 'date'], 'result': 'november 6', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; decision ; johnson } ; date }'}, 'november 6'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; decision ; johnson } ; date } ; november 6 }', 'tointer': 'the date record of this unqiue row is november 6 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; decision ; johnson } } ; eq { hop { filter_eq { all_rows ; decision ; johnson } ; date } ; november 6 } } = true', 'tointer': 'select the rows whose decision record fuzzily matches to johnson . there is only one such row in the table . the date record of this unqiue row is november 6 .'}

and { only { filter_eq { all_rows ; decision ; johnson } } ; eq { hop { filter_eq { all_rows ; decision ; johnson } ; date } ; november 6 } } = true

select the rows whose decision record fuzzily matches to johnson . there is only one such row in the table . the date record of this unqiue row is november 6 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'decision_7': 7, 'johnson_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, 'november 6_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'decision_7': 'decision', 'johnson_8': 'johnson', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', 'november 6_10': 'november 6'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'decision_7': [0], 'johnson_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], 'november 6_10': [3]}

['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']]

2007 - 08 fis ski jumping world cup

https://en.wikipedia.org/wiki/2007%E2%80%9308_FIS_Ski_Jumping_World_Cup

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14407512-16.html.csv

majority

most of the top five ski jumpers finished the fis world cup event with under 500 overall wc points .

{'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '500', 'subset': None}

{'func': 'most_less', 'args': ['all_rows', 'overall wc points ( rank )', '500'], 'result': True, 'ind': 0, 'tointer': 'for the overall wc points ( rank ) records of all rows , most of them are less than 500 .', 'tostr': 'most_less { all_rows ; overall wc points ( rank ) ; 500 } = true'}

most_less { all_rows ; overall wc points ( rank ) ; 500 } = true

for the overall wc points ( rank ) records of all rows , most of them are less than 500 .

1

1

{'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'overall wc points (rank)_3': 3, '500_4': 4}

{'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'overall wc points (rank)_3': 'overall wc points ( rank )', '500_4': '500'}

{'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'overall wc points (rank)_3': [0], '500_4': [0]}

['rank', 'name', 'nationality', '1st ( m )', 'points', 'overall wc points ( rank )']

[['1', 'anders bardal', 'nor', '137.0', '149.1', '461 ( 6 )'], ['2', 'thomas morgenstern', 'aut', '135.0', '145.0', '1255 ( 1 )'], ['3', 'simon ammann', 'sui', '136.5', '144.2', '448 ( 8 )'], ['4', 'adam małysz', 'pol', '133.5', '141.3', '386 ( 11 )'], ['5', 'andreas küttel', 'sui', '134.0', '141.2', '459 ( 7 )']]

edoardo piscopo

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15570607-1.html.csv

comparative

edoardo piscopo scored more points during the formula renault 2.0 italy than during the italian formula 3 .

{'row_1': '3', 'row_2': '8', 'col': '8', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'series', 'formula renault 2.0 italy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose series record fuzzily matches to formula renault 2.0 italy .', 'tostr': 'filter_eq { all_rows ; series ; formula renault 2.0 italy }'}, 'points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; series ; formula renault 2.0 italy } ; points }', 'tointer': 'select the rows whose series record fuzzily matches to formula renault 2.0 italy . take the points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'series', 'italian formula three'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose series record fuzzily matches to italian formula three .', 'tostr': 'filter_eq { all_rows ; series ; italian formula three }'}, 'points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; series ; italian formula three } ; points }', 'tointer': 'select the rows whose series record fuzzily matches to italian formula three . take the points record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; series ; formula renault 2.0 italy } ; points } ; hop { filter_eq { all_rows ; series ; italian formula three } ; points } } = true', 'tointer': 'select the rows whose series record fuzzily matches to formula renault 2.0 italy . take the points record of this row . select the rows whose series record fuzzily matches to italian formula three . take the points record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; series ; formula renault 2.0 italy } ; points } ; hop { filter_eq { all_rows ; series ; italian formula three } ; points } } = true

select the rows whose series record fuzzily matches to formula renault 2.0 italy . take the points record of this row . select the rows whose series record fuzzily matches to italian formula three . take the points 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, 'series_7': 7, 'formula renault 2.0 italy_8': 8, 'points_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'series_11': 11, 'italian formula three_12': 12, 'points_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', 'series_7': 'series', 'formula renault 2.0 italy_8': 'formula renault 2.0 italy', 'points_9': 'points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'series_11': 'series', 'italian formula three_12': 'italian formula three', 'points_13': 'points'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'series_7': [0], 'formula renault 2.0 italy_8': [0], 'points_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'series_11': [1], 'italian formula three_12': [1], 'points_13': [3]}

['season', 'series', 'team', 'races', 'wins', 'poles', 'podiums', 'points', 'position']

[['2005', 'formula bmw usa', 'eurointernational', '12', '3', '1', '5', '108', '5th'], ['2006', 'eurocup formula renault 2.0', 'cram competition', '14', '0', '0', '0', '34', '10th'], ['2006', 'formula renault 2.0 italy', 'cram competition', '12', '0', '1', '10', '216', '3rd'], ['2006 - 07', 'toyota racing series', 'mark petch motorsport', '5', '0', '0', '1', '78', '25th'], ['2007', 'formula 3 euro series', 'asl mücke motorsport', '20', '0', '0', '1', '8', '15th'], ['2007', 'masters of formula 3', 'asl mücke motorsport', '1', '0', '0', '0', 'n / a', '5th'], ['2007 - 08', 'a1 grand prix', 'a1 team italy', '14', '0', '0', '0', '12', '18th'], ['2008', 'italian formula three', 'team ghinzani', '16', '7', '8', '11', '127', '2nd'], ['2008', 'euroseries 3000', 'sighinolfi autoracing', '2', '0', '0', '2', '10', '14th'], ['2008', 'spanish formula three', 'gta motor competición', '2', '0', '0', '0', '0', 'nc'], ['2008 - 09', 'a1 grand prix', 'a1 team italy', '8', '0', '0', '0', '17', '16th'], ['2009', 'fia formula two championship', 'motorsport vision', '14', '0', '0', '0', '19', '12th'], ['2009', 'euroseries 3000', 'emmebi motorsport', '3', '0', '0', '1', '11', '9th'], ['2009 - 10', 'gp2 asia series', 'dams', '8', '0', '0', '0', '3', '16th'], ['2010', 'auto gp', 'dams', '12', '0', '0', '5', '42', '2nd'], ['2010', 'formula le mans', 'dams', '1', '0', '1', '0', '1', '15th'], ['2010', 'gp2 series', 'trident racing', '2', '0', '0', '0', '2', '26th']]

2009 - 10 a - league

https://en.wikipedia.org/wiki/2009%E2%80%9310_A-League

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18793550-6.html.csv

unique

the round 20 game was the only one that was played in december .

{'scope': 'all', 'row': '4', 'col': '3', 'col_other': '2', 'criterion': 'fuzzily_match', 'value': 'december', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'december'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to december .', 'tostr': 'filter_eq { all_rows ; date ; december }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; date ; december } }', 'tointer': 'select the rows whose date record fuzzily matches to december . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'december'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to december .', 'tostr': 'filter_eq { all_rows ; date ; december }'}, 'round'], 'result': '20', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; december } ; round }'}, '20'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; december } ; round } ; 20 }', 'tointer': 'the round record of this unqiue row is 20 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; date ; december } } ; eq { hop { filter_eq { all_rows ; date ; december } ; round } ; 20 } } = true', 'tointer': 'select the rows whose date record fuzzily matches to december . there is only one such row in the table . the round record of this unqiue row is 20 .'}

and { only { filter_eq { all_rows ; date ; december } } ; eq { hop { filter_eq { all_rows ; date ; december } ; round } ; 20 } } = true

select the rows whose date record fuzzily matches to december . there is only one such row in the table . the round record of this unqiue row is 20 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'date_7': 7, 'december_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'round_9': 9, '20_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'date_7': 'date', 'december_8': 'december', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'round_9': 'round', '20_10': '20'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'date_7': [0], 'december_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'round_9': [2], '20_10': [3]}

['attendance', 'round', 'date', 'home', 'score', 'away', 'venue', 'weekday', 'time of day']

[['44560', 'grand final', '20 march 2010', 'melbourne victory', '1 - 1', 'sydney fc', 'etihad stadium', 'saturday', 'evening'], ['32792', 'finals wk 2', '7 march 2010', 'wellington phoenix', '3 - 1', 'newcastle jets', 'westpac stadium', 'sunday', 'afternoon'], ['30668', '10', '9 october 2009', 'melbourne victory', '0 - 3', 'sydney fc', 'etihad stadium', 'friday', 'night'], ['27344', '20', '19 december 2009', 'melbourne victory', '0 - 0', 'sydney fc', 'etihad stadium', 'saturday', 'evening'], ['25407', '27', '14 february 2010', 'sydney fc', '2 - 0', 'melbourne victory', 'sydney football stadium', 'sunday', 'evening'], ['24278', 'finals wk 1', '21 february 2010', 'wellington phoenix', '1 - 1', 'perth glory', 'westpac stadium', 'sunday', 'evening'], ['23818', 'final wk 2', '7 march 2010', 'sydney fc', '2 - 2', 'melbourne victory', 'sydney football stadium', 'sunday', 'evening'], ['22726', '26', '5 february 2010', 'melbourne victory', '2 - 0', 'north queensland fury', 'etihad stadium', 'friday', 'night'], ['21182', '12', '24 october 2009', 'melbourne victory', '3 - 1', 'adelaide united', 'etihad stadium', 'saturday', 'evening'], ['20537', '16', '28 november 2009', 'melbourne victory', '4 - 0', 'gold coast united', 'etihad stadium', 'saturday', 'evening']]

südtirol

https://en.wikipedia.org/wiki/Politics_of_Trentino-Alto_Adige/S%C3%BCdtirol

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17305625-1.html.csv

majority

most of the mayors in the südtirol region are members of the democratic party .

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

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

most_eq { all_rows ; party ; democratic party } = true

for the party records of all rows , most of them fuzzily match to democratic party .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'party_3': 3, 'democratic party_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'party_3': 'party', 'democratic party_4': 'democratic party'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'party_3': [0], 'democratic party_4': [0]}

['municipality', 'inhabitants', 'mayor', 'party', 'election']

[['trento', '116298', 'alessandro andreatta', 'democratic party', '2009'], ['rovereto', '38167', 'andrea miorandi', 'democratic party', '2010'], ['pergine valsugana', '20582', 'silvano corradi', 'union for trentino', '2009'], ['arco', '16901', 'paolo mattei', 'democratic party', '2010'], ['riva del garda', '16170', 'adalberto mosaner', 'democratic party', '2010']]

india national under - 23 football team results

https://en.wikipedia.org/wiki/India_national_under-23_football_team_results

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25428629-1.html.csv

majority

all of the games in the india national under-23 football team had a team score of at least 1 .

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

{'func': 'all_str_eq', 'args': ['all_rows', 'score', '1'], 'result': True, 'ind': 0, 'tointer': 'for the score records of all rows , all of them fuzzily match to 1 .', 'tostr': 'all_eq { all_rows ; score ; 1 } = true'}

all_eq { all_rows ; score ; 1 } = true

for the score records of all rows , all of them fuzzily match to 1 .

1

1

{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'score_3': 3, '1_4': 4}

{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'score_3': 'score', '1_4': '1'}

{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'score_3': [0], '1_4': [0]}

['date', 'tournament', 'location', 'opponent', 'stadium', 'score', 'indian scorers']

[['23 february 2011', '2012 olympic qualifier', 'pune , india', 'myanmar', 'balewadi sports complex', '2 - 1', 'jeje lalpekhlua , malsawmfela'], ['9 march 2011', '2012 olympic qualifier', 'yangon , myanmar', 'myanmar', 'thuwunna stadium', '1 - 1', 'chinadorai sabeeth'], ['19 june 2011', '2012 olympic qualifier', 'doha , qatar', 'qatar', 'jassim bin hamad stadium', '1 - 3', 'jeje lalpekhlua'], ['23 june 2011', '2012 olympic qualifier', 'pune , india', 'qatar', 'balewadi sports complex', '1 - 1', 'own goal'], ['25 june 2012', '2014 afc u - 22 asian cup qualifiers', 'muscat , oman', 'iraq', 'royal oman police stadium', '1 - 2', 'alwyn george'], ['28 june 2012', '2014 afc u - 22 asian cup qualifiers', 'muscat , oman', 'united arab emirates', 'royal oman police stadium', '1 - 1', 'romeo fernandes']]

doves discography

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10537807-4.html.csv

unique

among the songs , here it comes is the only song that was released in august .

{'scope': 'all', 'row': '1', 'col': '2', 'col_other': '1', 'criterion': 'fuzzily_match', 'value': 'august', 'subset': None}

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

and { only { filter_eq { all_rows ; release date ; august } } ; eq { hop { filter_eq { all_rows ; release date ; august } ; song } ; here it comes } } = true

select the rows whose release date record fuzzily matches to august . there is only one such row in the table . the song record of this unqiue row is here it comes .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'release date_7': 7, 'august_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'song_9': 9, 'here it comes_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'release date_7': 'release date', 'august_8': 'august', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'song_9': 'song', 'here it comes_10': 'here it comes'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'release date_7': [0], 'august_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'song_9': [2], 'here it comes_10': [3]}

['song', 'release date', 'release info', 'formats', 'album']

[['here it comes', '2 august 1999', 'casino ( chip003 )', 'cd , 10 vinyl', 'here it comes ep'], ['the cedar room', '20 march 2000', 'heavenly ( hvn95 )', 'cd , 10 vinyl', 'lost souls'], ['catch the sun', '29 may 2000', 'heavenly ( hvn96 )', 'cd1 , cd2 , 10 vinyl', 'lost souls'], ['the man who told everything', '30 october 2000', 'heavenly ( hvn98 )', 'cd1 , cd2 , 7 vinyl', 'lost souls'], ['there goes the fear', '15 april 2002', 'heavenly ( hvn111 )', 'cd , 10 vinyl', 'the last broadcast'], ['pounding', '22 july 2002', 'heavenly ( hvn116 )', 'cd , dvd , 10 vinyl', 'the last broadcast'], ['caught by the river', '14 october 2002', 'heavenly ( hvn126 )', 'ecd , cd , 10 vinyl', 'the last broadcast'], ['black and white town', '7 february 2005', 'heavenly ( hvn145 )', 'cd1 , cd2 , 7 vinyl', 'some cities'], ['snowden', '9 may 2005', 'heavenly ( hvn150 )', 'cd1 , cd2 , 7 vinyl', 'some cities'], ['sky starts falling', '12 september 2005', 'heavenly ( hvn152 )', 'cd , dvd , 7 vinyl', 'some cities'], ['kingdom of rust', '30 march 2009', 'heavenly ( hvn189 )', 'cd , 7 vinyl , 3 x 12 vinyl', 'kingdom of rust'], ['winter hill', '20 july 2009', 'heavenly ( hvn192 )', '7 vinyl , 3 x 12 vinyl', 'kingdom of rust'], ['andalucia', '5 april 2010', 'heavenly ( hvn201 )', 'dl', 'the places between : the best of doves'], ['- denotes a release that did not chart', '- denotes a release that did not chart', '- denotes a release that did not chart', '- denotes a release that did not chart', '- denotes a release that did not chart']]

list of longest suspension bridge spans

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1604842-2.html.csv

unique

hålogaland bridge is the only norwegian bridge to appear on the list of longest suspension bridge spans .

{'scope': 'all', 'row': '6', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'norway', 'subset': None}

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

and { only { filter_eq { all_rows ; country ; norway } } ; eq { hop { filter_eq { all_rows ; country ; norway } ; name } ; hålogaland bridge } } = true

select the rows whose country record fuzzily matches to norway . there is only one such row in the table . the name record of this unqiue row is hålogaland bridge .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'country_7': 7, 'norway_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'hålogaland bridge_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'country_7': 'country', 'norway_8': 'norway', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'hålogaland bridge_10': 'hålogaland bridge'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'country_7': [0], 'norway_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'hålogaland bridge_10': [3]}

['name', 'main span metres', 'main span feet', 'year to open', 'country']

[['izmit bay bridge', '1550', '5085', '2017', 'turkey'], ['yavuz sultan selim bridge', '1408', '4619', '2015', 'turkey'], ['longjiang river bridge', '1196', '3924', '2015', 'china'], ['longmen bridge ( guangxi )', '1160', '3806', '2016', 'china'], ['ulsan bridge', '1150', '3773', '2014', 'south korea'], ['hålogaland bridge', '1145', '3757', '2017', 'norway'], ["ma'anshan bridge", '1080 ( x2 )', '3543 ( x2 )', '2013', 'china'], ['second namhae bridge', '890', '2920', '2016', 'south korea'], ['cuntan bridge', '880', '2887', '2016', 'china'], ['lishui bridge', '856', '2808', '2013', 'china'], ['jeokgeum bridge', '850', '2790', '2013', 'south korea'], ['yingwuzhou bridge', '850 ( x2 )', '2790 ( x2 )', '2015', 'china'], ['qincaobei bridge', '788', '2585', '2013', 'china'], ['puli bridge', '628', '2060', '2015', 'china'], ['dimuhe river bridge', '538', '1765', '2015', 'china'], ['taohuayu yellow river bridge', '406', '1332', '2013', 'china'], ['dandeung bridge', '400', '1312', '2014', 'south korea']]

the sunday night project

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1590967-2.html.csv

comparative

the episode with guest host jessie wallace aired 7 days before the episode with guest host trisha goddard .

{'row_1': '7', 'row_2': '8', 'col': '2', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'yes', 'diff_result': None}

{'func': 'and', 'args': [{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'guest host', 'jessie wallace'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose guest host record fuzzily matches to jessie wallace .', 'tostr': 'filter_eq { all_rows ; guest host ; jessie wallace }'}, 'air date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; guest host ; jessie wallace } ; air date }', 'tointer': 'select the rows whose guest host record fuzzily matches to jessie wallace . take the air date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'guest host', 'trisha goddard'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose guest host record fuzzily matches to trisha goddard .', 'tostr': 'filter_eq { all_rows ; guest host ; trisha goddard }'}, 'air date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; guest host ; trisha goddard } ; air date }', 'tointer': 'select the rows whose guest host record fuzzily matches to trisha goddard . take the air date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; guest host ; jessie wallace } ; air date } ; hop { filter_eq { all_rows ; guest host ; trisha goddard } ; air date } }', 'tointer': 'select the rows whose guest host record fuzzily matches to jessie wallace . take the air date record of this row . select the rows whose guest host record fuzzily matches to trisha goddard . take the air date record of this row . the first record is less than the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'guest host', 'jessie wallace'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose guest host record fuzzily matches to jessie wallace .', 'tostr': 'filter_eq { all_rows ; guest host ; jessie wallace }'}, 'air date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; guest host ; jessie wallace } ; air date }', 'tointer': 'select the rows whose guest host record fuzzily matches to jessie wallace . take the air date record of this row .'}, '17 february 2006'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; guest host ; jessie wallace } ; air date } ; 17 february 2006 }', 'tointer': 'the air date record of the first row is 17 february 2006 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'guest host', 'trisha goddard'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose guest host record fuzzily matches to trisha goddard .', 'tostr': 'filter_eq { all_rows ; guest host ; trisha goddard }'}, 'air date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; guest host ; trisha goddard } ; air date }', 'tointer': 'select the rows whose guest host record fuzzily matches to trisha goddard . take the air date record of this row .'}, '24 february 2006'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; guest host ; trisha goddard } ; air date } ; 24 february 2006 }', 'tointer': 'the air date record of the second row is 24 february 2006 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; guest host ; jessie wallace } ; air date } ; 17 february 2006 } ; eq { hop { filter_eq { all_rows ; guest host ; trisha goddard } ; air date } ; 24 february 2006 } }', 'tointer': 'the air date record of the first row is 17 february 2006 . the air date record of the second row is 24 february 2006 .'}], 'result': True, 'ind': 8, 'tostr': 'and { less { hop { filter_eq { all_rows ; guest host ; jessie wallace } ; air date } ; hop { filter_eq { all_rows ; guest host ; trisha goddard } ; air date } } ; and { eq { hop { filter_eq { all_rows ; guest host ; jessie wallace } ; air date } ; 17 february 2006 } ; eq { hop { filter_eq { all_rows ; guest host ; trisha goddard } ; air date } ; 24 february 2006 } } } = true', 'tointer': 'select the rows whose guest host record fuzzily matches to jessie wallace . take the air date record of this row . select the rows whose guest host record fuzzily matches to trisha goddard . take the air date record of this row . the first record is less than the second record . the air date record of the first row is 17 february 2006 . the air date record of the second row is 24 february 2006 .'}

and { less { hop { filter_eq { all_rows ; guest host ; jessie wallace } ; air date } ; hop { filter_eq { all_rows ; guest host ; trisha goddard } ; air date } } ; and { eq { hop { filter_eq { all_rows ; guest host ; jessie wallace } ; air date } ; 17 february 2006 } ; eq { hop { filter_eq { all_rows ; guest host ; trisha goddard } ; air date } ; 24 february 2006 } } } = true

select the rows whose guest host record fuzzily matches to jessie wallace . take the air date record of this row . select the rows whose guest host record fuzzily matches to trisha goddard . take the air date record of this row . the first record is less than the second record . the air date record of the first row is 17 february 2006 . the air date record of the second row is 24 february 2006 .

13

9

{'and_8': 8, 'result_9': 9, 'less_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'guest host_11': 11, 'jessie wallace_12': 12, 'air date_13': 13, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'guest host_15': 15, 'trisha goddard_16': 16, 'air date_17': 17, 'and_7': 7, 'str_eq_5': 5, '17 february 2006_18': 18, 'str_eq_6': 6, '24 february 2006_19': 19}

{'and_8': 'and', 'result_9': 'true', 'less_4': 'less', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'guest host_11': 'guest host', 'jessie wallace_12': 'jessie wallace', 'air date_13': 'air date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'guest host_15': 'guest host', 'trisha goddard_16': 'trisha goddard', 'air date_17': 'air date', 'and_7': 'and', 'str_eq_5': 'str_eq', '17 february 2006_18': '17 february 2006', 'str_eq_6': 'str_eq', '24 february 2006_19': '24 february 2006'}

{'and_8': [9], 'result_9': [], 'less_4': [8], 'str_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'guest host_11': [0], 'jessie wallace_12': [0], 'air date_13': [2], 'str_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'guest host_15': [1], 'trisha goddard_16': [1], 'air date_17': [3], 'and_7': [8], 'str_eq_5': [7], '17 february 2006_18': [5], 'str_eq_6': [7], '24 february 2006_19': [6]}

['episode number', 'air date', 'guest host', 'musical guest ( song performed )', 'who knows the most about the guest host panelists']

[['1', '6 january 2006', 'billie piper', 'texas ( sleep )', 'jade goody and kenzie'], ['2', '13 january 2006', 'lorraine kelly', 'editors ( munich )', 'myleene klass and phil tufnell'], ['3', '20 january 2006', 'christian slater', "the kooks ( you do n't love me )", 'lady isabella hervey and fearne cotton'], ['4', '27 january 2006', 'denise van outen', 'boy kill boy ( back again )', 'bez and nadia almada'], ['5', '3 february 2006', 'michael barrymore', 'the ordinary boys ( boys will be boys )', 'nancy sorrell and samia smith'], ['6', '10 february 2006', 'jamie oliver', 'kubb ( grow )', 'tara palmer - tomkinson and chantelle houghton'], ['7', '17 february 2006', 'jessie wallace', 'hard - fi ( hard to beat )', 'caprice bourret and hilda braid'], ['8', '24 february 2006', 'trisha goddard', 'the automatic ( raoul )', 'faria alam and pete burns']]

loonie

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18400-2.html.csv

aggregation

the total mintage of all the special loonies produced between 2002 - 2012 is 418918 coins .

{'scope': 'all', 'col': '4', 'type': 'sum', 'result': '418918', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'mintage'], 'result': '418918', 'ind': 0, 'tostr': 'sum { all_rows ; mintage }'}, '418918'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; mintage } ; 418918 } = true', 'tointer': 'the sum of the mintage record of all rows is 418918 .'}

round_eq { sum { all_rows ; mintage } ; 418918 } = true

the sum of the mintage record of all rows is 418918 .

2

2

{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'mintage_4': 4, '418918_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'mintage_4': 'mintage', '418918_5': '418918'}

{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'mintage_4': [0], '418918_5': [1]}

['year', 'theme', 'artist', 'mintage', 'issue price']

[['2002', '15th anniversary loonie', 'dora de pãdery - hunt', '67672', '39.95'], ['2004', 'jack miner bird sanctuary', 'susan taylor', '46493', '39.95'], ['2005', 'tufted puffin', 'n / a', '39818', '39.95'], ['2006', 'snowy owl', 'glen loates', '39935', '44.95'], ['2007', 'trumpeter swan', 'kerri burnett', '40000', '45.95'], ['2008', 'common eider', 'mark hobson', '40000', '47.95'], ['2009', 'great blue heron', 'chris jordison', '40000', '47.95'], ['2010', 'northern harrier', 'arnold nogy', '35000', '49.95'], ['2011', 'great gray owl', 'arnold nogy', '35000', '49.95'], ['2012', '25th anniversary loonie', 'arnold nogy', '35000', '49.95']]

ricardo páez

https://en.wikipedia.org/wiki/Ricardo_P%C3%A1ez

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14394530-1.html.csv

unique

the match on sept 4 , 2001 was the only one taking place in chile .

{'scope': 'all', 'row': '1', 'col': '3', 'col_other': '2', 'criterion': 'fuzzily_match', 'value': 'chile', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'chile'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to chile .', 'tostr': 'filter_eq { all_rows ; venue ; chile }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; venue ; chile } }', 'tointer': 'select the rows whose venue record fuzzily matches to chile . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'chile'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to chile .', 'tostr': 'filter_eq { all_rows ; venue ; chile }'}, 'date'], 'result': 'september 4 , 2001', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; venue ; chile } ; date }'}, 'september 4 , 2001'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; venue ; chile } ; date } ; september 4 , 2001 }', 'tointer': 'the date record of this unqiue row is september 4 , 2001 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; venue ; chile } } ; eq { hop { filter_eq { all_rows ; venue ; chile } ; date } ; september 4 , 2001 } } = true', 'tointer': 'select the rows whose venue record fuzzily matches to chile . there is only one such row in the table . the date record of this unqiue row is september 4 , 2001 .'}

and { only { filter_eq { all_rows ; venue ; chile } } ; eq { hop { filter_eq { all_rows ; venue ; chile } ; date } ; september 4 , 2001 } } = true

select the rows whose venue record fuzzily matches to chile . there is only one such row in the table . the date record of this unqiue row is september 4 , 2001 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'venue_7': 7, 'chile_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, 'september 4 , 2001_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'venue_7': 'venue', 'chile_8': 'chile', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', 'september 4 , 2001_10': 'september 4 , 2001'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'venue_7': [0], 'chile_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], 'september 4 , 2001_10': [3]}

['goal', 'date', 'venue', 'score', 'result', 'competition']

[['1', 'september 4 , 2001', 'estadio nacional de chile , santiago , chile', '0 - 1', '0 - 2', '2002 world cup qualification'], ['2', 'november 20 , 2002', 'brígido iriarte , caracas , venezuela', '1 - 0', '1 - 0', 'friendly'], ['3', 'april 2 , 2003', 'brígido iriarte , caracas , venezuela', '2 - 0', '2 - 0', 'friendly'], ['4', 'february 9 , 2005', 'josé pachencho romero , maracaibo , venezuela', '1 - 0', '3 - 0', 'friendly'], ['5', 'march 28 , 2007', 'josé pachencho romero , maracaibo , venezuela', '1 - 0', '5 - 0', 'friendly'], ['6', 'june 26 , 2007', 'pueblo nuevo , san cristóbal , venezuela', '2 - 1', '2 - 2', '2007 copa américa']]

scottish parliament general election , 2007

https://en.wikipedia.org/wiki/Scottish_Parliament_general_election%2C_2007

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11105214-1.html.csv

count

liberal democrats were the winning party in 2003 for 3 constituencies .

{'scope': 'all', 'criterion': 'equal', 'value': 'liberal democrats', 'result': '3', 'col': '3', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winning party 2003', 'liberal democrats'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winning party 2003 record fuzzily matches to liberal democrats .', 'tostr': 'filter_eq { all_rows ; winning party 2003 ; liberal democrats }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; winning party 2003 ; liberal democrats } }', 'tointer': 'select the rows whose winning party 2003 record fuzzily matches to liberal democrats . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; winning party 2003 ; liberal democrats } } ; 3 } = true', 'tointer': 'select the rows whose winning party 2003 record fuzzily matches to liberal democrats . the number of such rows is 3 .'}

eq { count { filter_eq { all_rows ; winning party 2003 ; liberal democrats } } ; 3 } = true

select the rows whose winning party 2003 record fuzzily matches to liberal democrats . 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, 'winning party 2003_5': 5, 'liberal democrats_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', 'winning party 2003_5': 'winning party 2003', 'liberal democrats_6': 'liberal democrats', '3_7': '3'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'winning party 2003_5': [0], 'liberal democrats_6': [0], '3_7': [2]}

['rank', 'constituency', 'winning party 2003', 'swing to gain', "labour 's place 2003", 'result']

[['1', 'dundee east', 'snp', '0.17', '2nd', 'snp hold'], ['2', 'edinburgh south', 'liberal democrats', '0.26', '2nd', 'ld hold'], ['3', 'ochil', 'snp', '0.49', '2nd', 'snp hold'], ['4', 'strathkelvin and bearsden', 'independent', '0.62', '2nd', 'lab gain'], ['5', 'aberdeen north', 'snp', '0.92', '2nd', 'snp hold'], ['6', 'inverness east , nairn and lochaber', 'snp', '1.51', '2nd', 'snp hold'], ['7', 'tweeddale , ettrick and lauderdale', 'liberal democrats', '2.70', '3rd', 'ld hold'], ['8', 'ayr', 'conservative', '2.99', '2nd', 'con hold'], ['9', 'edinburgh pentlands', 'conservative', '3.16', '2nd', 'con hold'], ['10', 'caithness , sutherland and easter ross', 'liberal democrats', '4.96', '2nd', 'ld hold']]

list of how it 's made episodes

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15187735-6.html.csv

unique

in netflix list of how it 's made s03e25 is the only tv series that has recorded 77 episodes .

{'scope': 'all', 'row': '12', 'col': '2', 'col_other': '3', 'criterion': 'equal', 'value': '77', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'episode', '77'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose episode record is equal to 77 .', 'tostr': 'filter_eq { all_rows ; episode ; 77 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; episode ; 77 } }', 'tointer': 'select the rows whose episode record is equal to 77 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'episode', '77'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose episode record is equal to 77 .', 'tostr': 'filter_eq { all_rows ; episode ; 77 }'}, 'netflix'], 'result': 's03e25', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; episode ; 77 } ; netflix }'}, 's03e25'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; episode ; 77 } ; netflix } ; s03e25 }', 'tointer': 'the netflix record of this unqiue row is s03e25 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; episode ; 77 } } ; eq { hop { filter_eq { all_rows ; episode ; 77 } ; netflix } ; s03e25 } } = true', 'tointer': 'select the rows whose episode record is equal to 77 . there is only one such row in the table . the netflix record of this unqiue row is s03e25 .'}

and { only { filter_eq { all_rows ; episode ; 77 } } ; eq { hop { filter_eq { all_rows ; episode ; 77 } ; netflix } ; s03e25 } } = true

select the rows whose episode record is equal to 77 . there is only one such row in the table . the netflix record of this unqiue row is s03e25 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'episode_7': 7, '77_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'netflix_9': 9, 's03e25_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'episode_7': 'episode', '77_8': '77', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'netflix_9': 'netflix', 's03e25_10': 's03e25'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'episode_7': [0], '77_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'netflix_9': [2], 's03e25_10': [3]}

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

[['6 - 01', '66', 's03e14', 'three wheeled vehicles', 'baseball bats', 'artificial bonsai', 's trombone'], ['6 - 02', '67', 's03e15', 's spring', 's paver', 's piano ( part 1 )', 's piano ( part 2 )'], ['6 - 03', '68', 's03e16', 's rope', 's billiard table', 's sailboard', 's cymbal'], ['6 - 04', '69', 's03e17', 's seatbelt', 's window', 'wax figurines', 'hot air balloons'], ['6 - 05', '70', 's03e18', 'air filters', 'billiard cues', 'ice sculptures', 's suit'], ['6 - 06', '71', 's03e19', 'escalator s handrail', 's highlighter', 'guitar s string', 'wigs'], ['6 - 07', '72', 's03e20', 'traditional bows', 's coffee machine', 's mascot', 's hammock'], ['6 - 08', '73', 's03e21', 'fibreglass insulation', 's wooden duck', 'gumball machines', 'exhaust systems'], ['6 - 09', '74', 's03e22', 's chain', 's bagel', 'vinyl records ( part 1 )', 'vinyl records ( part 2 )'], ['6 - 10', '75', 's03e23', 's windshield', 'english saddles', 'butter', 'post clocks'], ['6 - 11', '76', 's03e24', 'individual transporters', 'cedar canoes', 'electric guitars ( part 1 )', 'electric guitars ( part 2 )'], ['6 - 12', '77', 's03e25', 'residential water heaters', 'air bags', 'jelly beans', 'ice resurfacers']]

1959 toronto argonauts season

https://en.wikipedia.org/wiki/1959_Toronto_Argonauts_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24136814-3.html.csv

comparative

the game played on week 8 drew a higher attendance than the game played on week 10 .

{'row_1': '10', 'row_2': '13', 'col': '6', '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', 'week', '8'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose week record fuzzily matches to 8 .', 'tostr': 'filter_eq { all_rows ; week ; 8 }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; week ; 8 } ; attendance }', 'tointer': 'select the rows whose week record fuzzily matches to 8 . take the attendance record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'week', '10'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose week record fuzzily matches to 10 .', 'tostr': 'filter_eq { all_rows ; week ; 10 }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; week ; 10 } ; attendance }', 'tointer': 'select the rows whose week record fuzzily matches to 10 . take the attendance record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; week ; 8 } ; attendance } ; hop { filter_eq { all_rows ; week ; 10 } ; attendance } } = true', 'tointer': 'select the rows whose week record fuzzily matches to 8 . take the attendance record of this row . select the rows whose week record fuzzily matches to 10 . take the attendance record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; week ; 8 } ; attendance } ; hop { filter_eq { all_rows ; week ; 10 } ; attendance } } = true

select the rows whose week record fuzzily matches to 8 . take the attendance record of this row . select the rows whose week record fuzzily matches to 10 . take the attendance record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'week_7': 7, '8_8': 8, 'attendance_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'week_11': 11, '10_12': 12, 'attendance_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'week_7': 'week', '8_8': '8', 'attendance_9': 'attendance', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'week_11': 'week', '10_12': '10', 'attendance_13': 'attendance'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'week_7': [0], '8_8': [0], 'attendance_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'week_11': [1], '10_12': [1], 'attendance_13': [3]}

['week', 'date', 'opponent', 'location', 'final score', 'attendance', 'record']

[['1', 'august 18', 'rough riders', 'landsdowne park', 'w 21 - 20', '20675', '1 - 0 - 0'], ['1', 'august 21', 'tiger - cats', 'exhibition stadium', 'l 16 - 7', '27554', '1 - 1 - 0'], ['2', 'august 28', 'alouettes', 'molson stadium', 'l 24 - 6', '23927', '1 - 2 - 0'], ['3', 'september 7', 'tiger - cats', 'civic stadium', 'l 37 - 3', '24245', '1 - 3 - 0'], ['4', 'september 13', 'rough riders', 'exhibition stadium', 'w 19 - 6', '25849', '2 - 3 - 0'], ['5', 'september 16', 'rough riders', 'landsdowne park', 'l 28 - 1', '13097', '2 - 4 - 0'], ['5', 'september 20', 'tiger - cats', 'exhibition stadium', 'l 34 - 17', '27883', '2 - 5 - 0'], ['6', 'september 26', 'alouettes', 'exhibition stadium', 'w 39 - 9', '20035', '3 - 5 - 0'], ['7', 'october 3', 'alouettes', 'molson stadium', 'w 37 - 14', '22152', '4 - 5 - 0'], ['8', 'october 10', 'tiger - cats', 'exhibition stadium', 'l 13 - 7', '26223', '4 - 6 - 0'], ['8', 'october 12', 'tiger - cats', 'civic stadium', 'l 20 - 7', '22068', '4 - 7 - 0'], ['9', 'october 17', 'alouettes', 'exhibition stadium', 'l 4 - 3', '19941', '4 - 8 - 0'], ['10', 'october 24', 'rough riders', 'landsdowne park', 'l 18 - 4', '14996', '4 - 9 - 0']]

1979 new york jets season

https://en.wikipedia.org/wiki/1979_New_York_Jets_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13834389-1.html.csv

comparative

the game against the new england patriots had higher attendance than the game against the cleveland browns .

{'row_1': '2', 'row_2': '1', 'col': '6', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'new england patriots'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to new england patriots .', 'tostr': 'filter_eq { all_rows ; opponent ; new england patriots }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; new england patriots } ; attendance }', 'tointer': 'select the rows whose opponent record fuzzily matches to new england patriots . take the attendance record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'cleveland browns'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to cleveland browns .', 'tostr': 'filter_eq { all_rows ; opponent ; cleveland browns }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; cleveland browns } ; attendance }', 'tointer': 'select the rows whose opponent record fuzzily matches to cleveland browns . take the attendance record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; opponent ; new england patriots } ; attendance } ; hop { filter_eq { all_rows ; opponent ; cleveland browns } ; attendance } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to new england patriots . take the attendance record of this row . select the rows whose opponent record fuzzily matches to cleveland browns . take the attendance record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; opponent ; new england patriots } ; attendance } ; hop { filter_eq { all_rows ; opponent ; cleveland browns } ; attendance } } = true

select the rows whose opponent record fuzzily matches to new england patriots . take the attendance record of this row . select the rows whose opponent record fuzzily matches to cleveland browns . take the attendance record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opponent_7': 7, 'new england patriots_8': 8, 'attendance_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'cleveland browns_12': 12, 'attendance_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opponent_7': 'opponent', 'new england patriots_8': 'new england patriots', 'attendance_9': 'attendance', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'cleveland browns_12': 'cleveland browns', 'attendance_13': 'attendance'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'new england patriots_8': [0], 'attendance_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'cleveland browns_12': [1], 'attendance_13': [3]}

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

[['1', '1979 - 09 - 02', 'cleveland browns', 'l 25 - 22 ( ot )', 'shea stadium', '48472'], ['2', '1979 - 09 - 09', 'new england patriots', 'l 56 - 3', 'schafer stadium', '53113'], ['3', '1979 - 09 - 16', 'detroit lions', 'w 31 - 10', 'shea stadium', '49612'], ['4', '1979 - 09 - 23', 'buffalo bills', 'l 46 - 31', 'rich stadium', '68731'], ['5', '1979 - 09 - 30', 'miami dolphins', 'w 33 - 27', 'shea stadium', '51496'], ['6', '1979 - 10 - 07', 'baltimore colts', 'l 10 - 8', 'memorial stadium', '32142'], ['7', '1979 - 10 - 15', 'minnesota vikings', 'w 14 - 7', 'shea stadium', '54479'], ['8', '1979 - 10 - 21', 'oakland raiders', 'w 28 - 19', 'shea stadium', '55802'], ['9', '1979 - 10 - 28', 'houston oilers', 'l 27 - 24 ( ot )', 'the astrodome', '45825'], ['10', '1979 - 11 - 04', 'green bay packers', 'w 27 - 22', 'lambeau field', '54201'], ['11', '1979 - 11 - 11', 'buffalo bills', 'l 14 - 12', 'shea stadium', '50647'], ['12', '1979 - 11 - 18', 'chicago bears', 'l 23 - 13', 'soldier field', '52635'], ['13', '1979 - 11 - 26', 'seattle seahawks', 'l 30 - 7', 'kingdome', '59977'], ['14', '1979 - 12 - 02', 'baltimore colts', 'w 30 - 17', 'shea stadium', '47744'], ['15', '1979 - 12 - 09', 'new england patriots', 'w 27 - 26', 'shea stadium', '45131'], ['16', '1979 - 12 - 15', 'miami dolphins', 'w 27 - 24', 'miami orange bowl', '49915']]

what a lemon

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16752996-2.html.csv

unique

the only version of what a lemon formatted in a double cd was the version from scandinavia in 2002 .

{'scope': 'all', 'row': '5', 'col': '4', 'col_other': '1,2', 'criterion': 'equal', 'value': 'double cd', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'format', 'double cd'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose format record fuzzily matches to double cd .', 'tostr': 'filter_eq { all_rows ; format ; double cd }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; format ; double cd } }', 'tointer': 'select the rows whose format record fuzzily matches to double cd . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'format', 'double cd'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose format record fuzzily matches to double cd .', 'tostr': 'filter_eq { all_rows ; format ; double cd }'}, 'region'], 'result': 'scandinavia', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; format ; double cd } ; region }'}, 'scandinavia'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; format ; double cd } ; region } ; scandinavia }', 'tointer': 'the region record of this unqiue row is scandinavia .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'format', 'double cd'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose format record fuzzily matches to double cd .', 'tostr': 'filter_eq { all_rows ; format ; double cd }'}, 'date'], 'result': '2002', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; format ; double cd } ; date }'}, '2002'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; format ; double cd } ; date } ; 2002 }', 'tointer': 'the date record of this unqiue row is 2002 .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; format ; double cd } ; region } ; scandinavia } ; eq { hop { filter_eq { all_rows ; format ; double cd } ; date } ; 2002 } }', 'tointer': 'the region record of this unqiue row is scandinavia . the date record of this unqiue row is 2002 .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; format ; double cd } } ; and { eq { hop { filter_eq { all_rows ; format ; double cd } ; region } ; scandinavia } ; eq { hop { filter_eq { all_rows ; format ; double cd } ; date } ; 2002 } } } = true', 'tointer': 'select the rows whose format record fuzzily matches to double cd . there is only one such row in the table . the region record of this unqiue row is scandinavia . the date record of this unqiue row is 2002 .'}

and { only { filter_eq { all_rows ; format ; double cd } } ; and { eq { hop { filter_eq { all_rows ; format ; double cd } ; region } ; scandinavia } ; eq { hop { filter_eq { all_rows ; format ; double cd } ; date } ; 2002 } } } = true

select the rows whose format record fuzzily matches to double cd . there is only one such row in the table . the region record of this unqiue row is scandinavia . the date record of this unqiue row is 2002 .

10

8

{'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'format_10': 10, 'double cd_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'region_12': 12, 'scandinavia_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'date_14': 14, '2002_15': 15}

{'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', 'format_10': 'format', 'double cd_11': 'double cd', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'region_12': 'region', 'scandinavia_13': 'scandinavia', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'date_14': 'date', '2002_15': '2002'}

{'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'format_10': [0], 'double cd_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'region_12': [2], 'scandinavia_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'date_14': [4], '2002_15': [5]}

['region', 'date', 'label', 'format', 'catalog']

[['united states', '1 august 1976', 'epic records', 'stereo lp', 'pe 34149'], ['europe', '1 august 1976', 'epic records', 'stereo lp', 'epc 81436'], ['australia', '15 november 1976', 'epic records', 'stereo lp', 'elps 3781'], ['japan', '1976', 'epic records', 'stereo lp', '25ap 301'], ['scandinavia', '2002', 'sony music entertainment', 'double cd', 'sm 2965 - 05']]

atlanta falcons draft history

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15198842-20.html.csv

comparative

mike gann was drafted in an earlier round by the atlanta falcons than brent martin .

{'row_1': '2', 'row_2': '8', 'col': '1', 'col_other': '4', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'mike gann'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to mike gann .', 'tostr': 'filter_eq { all_rows ; name ; mike gann }'}, 'round'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; mike gann } ; round }', 'tointer': 'select the rows whose name record fuzzily matches to mike gann . take the round record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'brent martin'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to brent martin .', 'tostr': 'filter_eq { all_rows ; name ; brent martin }'}, 'round'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; brent martin } ; round }', 'tointer': 'select the rows whose name record fuzzily matches to brent martin . take the round record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; name ; mike gann } ; round } ; hop { filter_eq { all_rows ; name ; brent martin } ; round } } = true', 'tointer': 'select the rows whose name record fuzzily matches to mike gann . take the round record of this row . select the rows whose name record fuzzily matches to brent martin . take the round record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; name ; mike gann } ; round } ; hop { filter_eq { all_rows ; name ; brent martin } ; round } } = true

select the rows whose name record fuzzily matches to mike gann . take the round record of this row . select the rows whose name record fuzzily matches to brent martin . take the round 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, 'name_7': 7, 'mike gann_8': 8, 'round_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'brent martin_12': 12, 'round_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', 'name_7': 'name', 'mike gann_8': 'mike gann', 'round_9': 'round', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'brent martin_12': 'brent martin', 'round_13': 'round'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'mike gann_8': [0], 'round_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'brent martin_12': [1], 'round_13': [3]}

['round', 'pick', 'overall', 'name', 'position', 'college']

[['1', '2', '2', 'bill fralic', 'guard', 'pittsburgh'], ['2', '17', '45', 'mike gann', 'defensive end', 'notre dame'], ['4', '5', '89', 'emile harry', 'wide receiver', 'stanford'], ['6', '12', '152', 'reggie pleasant', 'defensive back', 'clemson'], ['8', '5', '201', 'ashley lee', 'defensive back', 'virginia tech'], ['8', '19', '215', 'ronnie washington', 'linebacker', 'northeast louisiana'], ['9', '4', '228', 'micah moon', 'linebacker', 'north carolina'], ['10', '5', '257', 'brent martin', 'center', 'stanford'], ['11', '4', '284', 'john ayres', 'defensive back', 'illinois'], ['12', '5', '313', 'ken whisenhunt', 'tight end', 'georgia tech']]

2010 - 11 temple owls men 's basketball team

https://en.wikipedia.org/wiki/2010%E2%80%9311_Temple_Owls_men%27s_basketball_team

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29556461-8.html.csv

majority

the majority of games at liacouras center during the 2010 - 11 temple owls men 's basketball team season had an attendance over 5000 .

{'scope': 'subset', 'col': '8', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '5000', 'subset': {'col': '8', 'criterion': 'fuzzily_match', 'value': 'liacouras center'}}

{'func': 'most_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location attendance', 'liacouras center'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; location attendance ; liacouras center }', 'tointer': 'select the rows whose location attendance record fuzzily matches to liacouras center .'}, 'location attendance', '5000'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose location attendance record fuzzily matches to liacouras center . for the location attendance records of these rows , most of them are greater than 5000 .', 'tostr': 'most_greater { filter_eq { all_rows ; location attendance ; liacouras center } ; location attendance ; 5000 } = true'}

most_greater { filter_eq { all_rows ; location attendance ; liacouras center } ; location attendance ; 5000 } = true

select the rows whose location attendance record fuzzily matches to liacouras center . for the location attendance records of these rows , most of them are greater than 5000 .

2

2

{'most_greater_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'location attendance_4': 4, 'liacouras center_5': 5, 'location attendance_6': 6, '5000_7': 7}

{'most_greater_1': 'most_greater', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'location attendance_4': 'location attendance', 'liacouras center_5': 'liacouras center', 'location attendance_6': 'location attendance', '5000_7': '5000'}

{'most_greater_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'location attendance_4': [0], 'liacouras center_5': [0], 'location attendance_6': [1], '5000_7': [1]}

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

[['21', 'february 2', 'la salle', 'w 71 - 67', 'wyatt - 18', 'allen - 7', 'moore - 5', 'tom gola arena , philadelphia , pa ( 3121 )', '16 - 5 ( 6 - 2 )'], ['22', 'february 5', 'rhode island', 'w 80 - 67', 'randall - 27', 'allen - 10', 'moore / fernandez - 6', 'liacouras center , philadelphia , pa ( 8679 )', '17 - 5 ( 7 - 2 )'], ['23', 'february 9', 'fordham', 'w 77 - 66', 'moore - 22', 'allen - 14', 'allen / moore / wyatt - 4', 'liacouras center , philadelphia , pa ( 3858 )', '18 - 5 ( 8 - 2 )'], ['24', 'february 12', 'dayton', 'w 75 - 63', 'moore - 26', 'moore / eric - 9', 'fernandez - 9', 'university of dayton arena , dayton , oh ( 13117 )', '19 - 5 ( 9 - 2 )'], ['25', 'february 17', 'richmond', 'w 73 - 53', 'moore - 24', 'allen / jefferson - 7', 'allen - 4', 'liacouras center , philadelphia , pa ( 6078 )', '20 - 5 ( 10 - 2 )'], ['26', 'february 20', "saint joseph 's", 'w 66 - 52', 'moore - 17', 'allen - 12', 'fernandez - 6', 'liacouras center , philadelphia , pa ( 10206 )', '21 - 5 ( 11 - 2 )']]

wwfm

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12472016-2.html.csv

ordinal

w300ac has the 2nd highest erp w among all call signs .

{'row': '5', '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', 'erp w', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; erp w ; 2 }'}, 'call sign'], 'result': 'w300ac', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; erp w ; 2 } ; call sign }'}, 'w300ac'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; erp w ; 2 } ; call sign } ; w300ac } = true', 'tointer': 'select the row whose erp w record of all rows is 2nd maximum . the call sign record of this row is w300ac .'}

eq { hop { nth_argmax { all_rows ; erp w ; 2 } ; call sign } ; w300ac } = true

select the row whose erp w record of all rows is 2nd maximum . the call sign record of this row is w300ac .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'erp w_5': 5, '2_6': 6, 'call sign_7': 7, 'w300ac_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', 'erp w_5': 'erp w', '2_6': '2', 'call sign_7': 'call sign', 'w300ac_8': 'w300ac'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'erp w_5': [0], '2_6': [0], 'call sign_7': [1], 'w300ac_8': [2]}

['call sign', 'frequency mhz', 'city of license', 'erp w', 'class', 'fcc info']

[['k216fw', '91.1 fm', 'steamboat springs , colorado', '10', 'd', 'fcc'], ['w224au', '92.7 fm', 'allentown , pennsylvania', '8', 'd', 'fcc'], ['w226aa', '93.1 fm', 'easton , pennsylvania', '150', 'd', 'fcc'], ['w245ac', '96.9 fm', 'harmony township , new jersey', '10', 'd', 'fcc'], ['w300ac', '107.9 fm', 'chatsworth , new jersey', '35', 'd', 'fcc'], ['w230aa', '93.9 fm', 'atlantic city , new jersey', '27', 'd', 'fcc'], ['w284bw', '104.7 fm', 'franklin township , somerset county , new jersey', '13', 'd', 'fcc']]

mohammed nasser shakroun

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13607991-4.html.csv

majority

all of mohammed nasser shakroun 's international goals were scored in friendly matches .

{'scope': 'all', 'col': '5', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'friendly match', 'subset': None}

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

all_eq { all_rows ; competition ; friendly match } = true

for the competition records of all rows , all of them fuzzily match to friendly match .

1

1

{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'competition_3': 3, 'friendly match_4': 4}

{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'competition_3': 'competition', 'friendly match_4': 'friendly match'}

{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'competition_3': [0], 'friendly match_4': [0]}

['date', 'venue', 'score', 'result', 'competition']

[['26 march 2005', 'telstra stadium , sydney', '1 - 0', '1 - 2', 'friendly match'], ['7 august 2005', 'bahrain national stadium , manama', '1 - 2', '2 - 2', 'friendly match'], ['13 august 2005', 'tsirion stadium , limassol', '1 - 0', '2 - 1', 'friendly match'], ['15 march 2006', 'prince abdullah al - faisal stadium , jeddah', '2 - 0', '2 - 2', 'friendly match'], ['17 august 2006', 'king abdullah stadium , amman', '3 - 0', '3 - 0', 'friendly match'], ['27 december 2008', 'tahnoun bin mohamed stadium , al ain', '1 - 1', '2 - 2', 'friendly match']]

2007 georgia force season

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11710574-4.html.csv

count

there were four members of the georgia force that had at least three rushing touchdowns during the 2007 season .

{'scope': 'all', 'criterion': 'greater_than_eq', 'value': '3', 'result': '4', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', "td 's", '3'], 'result': None, 'ind': 0, 'tointer': "select the rows whose td 's record is greater than or equal to 3 .", 'tostr': "filter_greater_eq { all_rows ; td 's ; 3 }"}], 'result': '4', 'ind': 1, 'tostr': "count { filter_greater_eq { all_rows ; td 's ; 3 } }", 'tointer': "select the rows whose td 's record is greater than or equal to 3 . the number of such rows is 4 ."}, '4'], 'result': True, 'ind': 2, 'tostr': "eq { count { filter_greater_eq { all_rows ; td 's ; 3 } } ; 4 } = true", 'tointer': "select the rows whose td 's record is greater than or equal to 3 . the number of such rows is 4 ."}

eq { count { filter_greater_eq { all_rows ; td 's ; 3 } } ; 4 } = true

select the rows whose td 's record is greater than or equal to 3 . the number of such rows is 4 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_eq_0': 0, 'all_rows_4': 4, "td 's_5": 5, '3_6': 6, '4_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_4': 'all_rows', "td 's_5": "td 's", '3_6': '3', '4_7': '4'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_eq_0': [1], 'all_rows_4': [0], "td 's_5": [0], '3_6': [0], '4_7': [2]}

['player', 'car', 'yards', 'avg', "td 's", 'long']

[['matt huebner', '34', '122', '3.6', '5', '24'], ['troy bergeron', '10', '81', '8.1', '0', '19'], ['john ritcher', '20', '58', '2.9', '2', '21'], ['chris greisen', '14', '25', '1.8', '6', '12'], ['chris jackson', '9', '19', '2.1', '4', '8'], ['jarrick hillery', '11', '9', '8', '3', '4'], ['derek lee', '1', '2', '2', '0', '2'], ['bruce mcclure', '1', '1', '1', '1', '1'], ['james macpherson', '1', '1', '1', '1', '1']]

2007 - 08 segunda división

https://en.wikipedia.org/wiki/2007%E2%80%9308_Segunda_Divisi%C3%B3n

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11828307-4.html.csv

ordinal

out of all goalkeepers of the 2007-08 segunda division , jacobo had the 2nd best average .

{'row': '2', 'col': '4', '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', 'average', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; average ; 2 }'}, 'goalkeeper'], 'result': 'jacobo', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; average ; 2 } ; goalkeeper }'}, 'jacobo'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; average ; 2 } ; goalkeeper } ; jacobo } = true', 'tointer': 'select the row whose average record of all rows is 2nd minimum . the goalkeeper record of this row is jacobo .'}

eq { hop { nth_argmin { all_rows ; average ; 2 } ; goalkeeper } ; jacobo } = true

select the row whose average record of all rows is 2nd minimum . the goalkeeper record of this row is jacobo .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'average_5': 5, '2_6': 6, 'goalkeeper_7': 7, 'jacobo_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', 'average_5': 'average', '2_6': '2', 'goalkeeper_7': 'goalkeeper', 'jacobo_8': 'jacobo'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'average_5': [0], '2_6': [0], 'goalkeeper_7': [1], 'jacobo_8': [2]}

['goalkeeper', 'goals', 'matches', 'average', 'team']

[['carlos sánchez', '27', '33', '0.82', 'cd castellón'], ['jacobo', '29', '32', '0.91', 'cd numancia'], ['asier riesgo', '39', '42', '0.93', 'real sociedad'], ['roberto', '39', '41', '0.95', 'sporting de gijón'], ['iñaki goitia', '41', '40', '1.02', 'málaga cf'], ['pedro contreras', '37', '36', '1.03', 'cádiz cf'], ['wilfredo caballero', '41', '38', '1.08', 'elche cf'], ['bernardo', '42', '38', '1.11', 'deportivo alavés'], ['javier varas', '46', '41', '1.12', 'sevilla atlético'], ['juan pablo', '33', '29', '1.14', 'cd tenerife']]

list of post - secondary institutions in malaysia

https://en.wikipedia.org/wiki/List_of_post-secondary_institutions_in_Malaysia

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18144241-2.html.csv

majority

most of the post-secondary institutions in malaysia do not have an acronym .

{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': '-', 'subset': None}

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

most_eq { all_rows ; acronym ; - } = true

for the acronym records of all rows , most of them fuzzily match to - .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'acronym_3': 3, '-_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'acronym_3': 'acronym', '-_4': '-'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'acronym_3': [0], '-_4': [0]}

['name in english', 'name in malay', 'acronym', 'foundation', 'location']

[['alor setar professional institute', 'institut profesional alor setar', '-', '-', 'alor setar'], ['a n s technological institute , alor setar', 'institut teknologi a n s alor setar', '-', '-', 'alor setar'], ['bandar darulaman community college', 'kolej komuniti bandar darulaman', '-', '-', 'jitra'], ['daya ilmu technological institute , sungai petani', 'institut teknologi daya ilmu sungai petani', '-', '-', 'sungai petani'], ['hasani institute', 'institut hasani', '-', '-', 'sungai petani'], ['informatics institute , sungai petani', 'institut informatics , sungai petani', '-', '-', 'sungai petani'], ['international northern higher education institute', 'institut pengajian tinggi utara antarabangsa', '-', 'petua', 'alor setar'], ['kulim community college', 'kolej komuniti kulim', '-', '-', 'kulim'], ['kulim polytechnic', 'politeknik kulim', 'pku', '-', 'kulim'], ['northern management and technological institute', 'institut pengurusan dan teknologi utara', '-', 'iptura', 'alor setar'], ["sultan abdul halim mu'adzam shah", "politeknik sultan abdul halim mu'adzam shah", 'polimas', '1984', 'jitra'], ['sungai petani community college', 'kolej komuniti sungai petani', '2002', 'kkspe', 'sungai petani']]

monica niculescu

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

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15272343-4.html.csv

majority

the majority of the matches were played on a hard surface .

{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'hard', 'subset': None}

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

most_eq { all_rows ; surface ; hard } = true

for the surface records of all rows , most of them fuzzily match to hard .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'hard_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'hard_4': 'hard'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'hard_4': [0]}

['outcome', 'date', 'surface', 'partner', 'opponents', 'score']

[['runner - up', '17 august 2008', 'hard', 'sorana cîrstea', 'květa peschke lisa raymond', '6 - 4 , 5 - 7 ,'], ['winner', '12 july 2009', 'clay', 'alisa kleybanova', 'alona bondarenko kateryna bondarenko', '6 - 4 , 7 - 6 ( 7 - 5 )'], ['runner - up', '2 august 2009', 'hard', 'chan yung - jan', 'serena williams venus williams', '1 - 6 , 4 - 6'], ['runner - up', '16 january 2010', 'hard', 'chan yung - jan', 'květa peschke chuang chia - jung', '6 - 3 , 3 - 6 ,'], ['runner - up', '18 july 2010', 'clay', 'ágnes szávay', 'timea bacsinszky tathiana garbin', '5 - 7 , 6 - 7 ( 4 - 7 )'], ['runner - up', '23 july 2011', 'hard', 'galina voskoboeva', 'mariya koryttseva tatiana poutchek', '3 - 6 , 6 - 2 ,'], ['winner', '14 january 2012', 'hard', 'irina - camelia begu', 'chuang chia - jung marina erakovic', '6 - 7 ( 4 - 7 ) , 7 - 6 ( 7 - 4 ) ,'], ['runner - up', '22 september 2012', 'hard', 'jarmila gajdošová', 'tamarine tanasugarn zhang shuai', '6 - 2 , 2 - 6 ,'], ['runner - up', '21 october 2012', 'hard ( i )', 'irina - camelia begu', 'andrea hlaváčková lucie hradecká', '3 - 6 , 4 - 6'], ['runner - up', '22 june 2013', 'grass', 'klára zakopalová', 'nadia petrova katarina srebotnik', '3 - 6 , 3 - 6']]

1996 u.s. open ( golf )

https://en.wikipedia.org/wiki/1996_U.S._Open_%28golf%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17162199-5.html.csv

count

two of the players in the 1996 u.s. open golf tournament were from the country of scotland .

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

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

eq { count { filter_eq { all_rows ; country ; scotland } } ; 2 } = true

select the rows whose country record fuzzily matches to scotland . 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, 'country_5': 5, 'scotland_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', 'country_5': 'country', 'scotland_6': 'scotland', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'country_5': [0], 'scotland_6': [0], '2_7': [2]}

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

[['1', 'tom lehman', 'united states', '71 + 72 + 65 = 208', '- 2'], ['2', 'steve jones', 'united states', '74 + 66 + 69 = 209', '- 1'], ['t3', 'davis love iii', 'united states', '71 + 69 + 70 = 210', 'e'], ['t3', 'john morse', 'united states', '68 + 74 + 68 = 210', 'e'], ['t3', 'frank nobilo', 'new zealand', '69 + 71 + 70 = 210', 'e'], ['t6', 'woody austin', 'united states', '67 + 72 + 72 = 211', '+ 1'], ['t6', 'ernie els', 'south africa', '72 + 67 + 72 = 211', '+ 1'], ['t6', 'jim furyk', 'united states', '72 + 69 + 70 = 211', '+ 1'], ['t6', 'colin montgomerie', 'scotland', '70 + 72 + 69 = 211', '+ 1'], ['t6', 'sam torrance', 'scotland', '71 + 69 + 71 = 211', '+ 1']]

1977 - 78 coupe de france

https://en.wikipedia.org/wiki/1977%E2%80%9378_Coupe_de_France

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17905518-1.html.csv

superlative

fc nantes was the team that scored the highest number of goals in the 1977-78 coupe de france .

{'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': 'fc nantes ( d1 )', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; score } ; team 1 }'}, 'fc nantes ( d1 )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; score } ; team 1 } ; fc nantes ( d1 ) } = true', 'tointer': 'select the row whose score record of all rows is maximum . the team 1 record of this row is fc nantes ( d1 ) .'}

eq { hop { argmax { all_rows ; score } ; team 1 } ; fc nantes ( d1 ) } = true

select the row whose score record of all rows is maximum . the team 1 record of this row is fc nantes ( 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, 'fc nantes (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', 'fc nantes (d1)_7': 'fc nantes ( 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], 'fc nantes (d1)_7': [2]}

['team 1', 'score', 'team 2', '1st round', '2nd round']

[['stade de reims ( d1 )', '1 - 3', 'sc bastia ( d1 )', '0 - 1', '1 - 2'], ['fc metz ( d1 )', '0 - 5', 'ogc nice ( d1 )', '0 - 2', '0 - 3'], ['olympique de marseille ( d1 )', '3 - 0', 'girondins de bordeaux ( d1 )', '1 - 0', '2 - 0'], ['as nancy ( d1 )', '3 - 1', 'fc martigues ( d2 )', '2 - 0', '1 - 1'], ['lille osc ( d2 )', '3 - 4', 'as monaco ( d1 )', '1 - 1', '2 - 3'], ['as angoulême ( d2 )', '0 - 1', 'fc sochaux - montbéliard ( d1 )', '0 - 0', '0 - 1'], ['fc nantes ( d1 )', '7 - 0', 'usl dunkerque ( d2 )', '2 - 0', '5 - 0'], ['gazélec ajaccio ( d2 )', '4 - 6', 'us valenciennes ( d1 )', '4 - 1', '0 - 5']]