niryuu/sni-each-converted huggingface dataset:
round_eq { avg { filter_eq { all_rows ; team ; america } ; average fencers rank } ; 6.58 } = true
Output: select the rows whose team record fuzzily matches to america . the average of the average fencers rank record of these rows is 6.58 .
all_eq { all_rows ; city of license ; texas } = true
Output: for the city of license records of all rows , all of them fuzzily match to texas .
round_eq { avg { all_rows ; earnings } ; 424466 } = true
Output: the average of the earnings record of all rows is 424466 .
eq { hop { argmax { all_rows ; yards } ; player } ; lonnie ford } = true
Output: select the row whose yards record of all rows is maximum . the player record of this row is lonnie ford .
eq { diff { hop { filter_eq { all_rows ; date ; nov 15 } ; cowboys points } ; hop { filter_eq { all_rows ; date ; nov 8 } ; cowboys points } } ; 2 } = true
Output: select the rows whose date record fuzzily matches to nov 15 . take the cowboys points record of this row . select the rows whose date record fuzzily matches to nov 8 . take the cowboys points record of this row . the first record is 2 larger than the second record .
round_eq { avg { all_rows ; crowd } ; 22,800 } = true
Output: the average of the crowd record of all rows is 22,800 .
most_eq { all_rows ; type ; end of contract } = true
Output: for the type records of all rows , most of them fuzzily match to end of contract .
eq { count { filter_greater { all_rows ; members lost ; 0 } } ; 3 } = true
Output: select the rows whose members lost record is greater than 0 . the number of such rows is 3 .
less { hop { filter_eq { all_rows ; minister ; rocco buttiglione } ; took office } ; hop { filter_eq { all_rows ; minister ; mario baccini } ; took office } } = true
Output: select the rows whose minister record fuzzily matches to rocco buttiglione . take the took office record of this row . select the rows whose minister record fuzzily matches to mario baccini . take the took office record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; nationality ; australia } } ; eq { hop { filter_eq { all_rows ; nationality ; australia } ; name } ; michael klim } } = true
Output: select the rows whose nationality record fuzzily matches to australia . there is only one such row in the table . the name record of this unqiue row is michael klim .
eq { count { filter_all { all_rows ; school } } ; 9 } = true
Output: select the rows whose school record is arbitrary . the number of such rows is 9 .
and { only { filter_eq { all_rows ; wins ; 1 } } ; eq { hop { filter_eq { all_rows ; wins ; 1 } ; tournament } ; the open championship } } = true
Output: select the rows whose wins record is equal to 1 . there is only one such row in the table . the tournament record of this unqiue row is the open championship .
and { only { filter_eq { all_rows ; status ; test match } } ; eq { hop { filter_eq { all_rows ; status ; test match } ; opposing teams } ; south africa } } = true
Output: select the rows whose status record fuzzily matches to test match . there is only one such row in the table . the opposing teams record of this unqiue row is south africa .
eq { hop { argmin { all_rows ; position } ; club } ; sporting de gijón } = true
Output: select the row whose position record of all rows is minimum . the club record of this row is sporting de gijón .
eq { hop { nth_argmin { all_rows ; years as tallest ; 1 } ; name } ; lomas & nettleton building } = true
Output: select the row whose years as tallest record of all rows is 1st minimum . the name record of this row is lomas  nettleton building .
most_eq { all_rows ; location attendance ; amway arena } = true
Output: for the location attendance records of all rows , most of them fuzzily match to amway arena .
and { only { filter_eq { all_rows ; college ; colo school of mines } } ; eq { hop { filter_eq { all_rows ; college ; colo school of mines } ; name } ; mike flater } } = true
Output: select the rows whose college record fuzzily matches to colo school of mines . there is only one such row in the table . the name record of this unqiue row is mike flater .
round_eq { avg { all_rows ; pts } ; 0 } = true
Output: the average of the pts record of all rows is 0 .
eq { count { filter_eq { filter_eq { all_rows ; club ; kfc breakers } ; date of birth ; 1974 } } ; 2 } = true
Output: select the rows whose club record fuzzily matches to kfc breakers . among these rows , select the rows whose date of birth record fuzzily matches to 1974 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; attendance } ; date } ; 19 may 2007 } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is 19 may 2007 .
eq { count { filter_eq { all_rows ; surface ; clay } } ; 5 } = true
Output: select the rows whose surface record fuzzily matches to clay . the number of such rows is 5 .
eq { hop { argmax { all_rows ; attendance } ; opponent } ; new york jets } = true
Output: select the row whose attendance record of all rows is maximum . the opponent record of this row is new york jets .
eq { diff { hop { filter_eq { all_rows ; organization ; omicron epsilon pi } ; founding date } ; hop { filter_eq { all_rows ; organization ; alpha lambda zeta } ; founding date } } ; -6 years } = true
Output: select the rows whose organization record fuzzily matches to omicron epsilon pi . take the founding date record of this row . select the rows whose organization record fuzzily matches to alpha lambda zeta . take the founding date record of this row . the second record is 6 years larger than the first record .
and { only { filter_eq { filter_eq { all_rows ; score ; w } ; high points ; alvan adams } } ; eq { hop { filter_eq { filter_eq { all_rows ; score ; w } ; high points ; alvan adams } ; date } ; april 15 } } = true
Output: select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high points record fuzzily matches to alvan adams . there is only one such row in the table . the date record of this unqiue row is april 15 .
round_eq { avg { filter_not_eq { all_rows ; us viewers ( millions ) ; n/a } ; us viewers ( millions ) } ; 2.59 } = true
Output: select the rows whose us viewers  millions  record does not match to na . the average of the us viewers  millions  record of these rows is 2.59 .
eq { count { filter_eq { all_rows ; position ; wide receiver } } ; 4 } = true
Output: select the rows whose position record fuzzily matches to wide receiver . the number of such rows is 4 .
and { only { filter_eq { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 ; milan } } ; eq { hop { filter_eq { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 ; milan } ; team 1 } ; arsenal } } = true
Output: select the rows whose 1st leg record fuzzily matches to 00 . among these rows , select the rows whose team 2 record fuzzily matches to milan . there is only one such row in the table . the team 1 record of this unqiue row is arsenal .
eq { hop { argmax { all_rows ; result } ; date } ; september 29 , 2003 } = true
Output: select the row whose result record of all rows is maximum . the date record of this row is september 29 , 2003 .
round_eq { sum { all_rows ; attendance } ; 476,381 } = true
Output: the sum of the attendance record of all rows is 476,381 .
eq { count { filter_eq { all_rows ; year premiered ; 2010 } } ; 3 } = true
Output: select the rows whose year premiered record is equal to 2010 . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; location attendance ; 2 } ; team } ; la lakers } = true
Output: select the row whose location attendance record of all rows is 2nd maximum . the team record of this row is la lakers .
and { only { filter_eq { all_rows ; nationality ; usa } } ; eq { hop { filter_eq { all_rows ; nationality ; usa } ; athlete } ; chaunté lowe } } = true
Output: select the rows whose nationality record fuzzily matches to usa . there is only one such row in the table . the athlete record of this unqiue row is chaunté lowe .
only { filter_eq { all_rows ; candidates ; l burd ( i ) 0.0 % } } = true
Output: select the rows whose candidates record fuzzily matches to l burd  i  0.0  . there is only one such row in the table .
less { hop { filter_eq { all_rows ; championship ; wimbledon } ; year } ; hop { filter_eq { all_rows ; championship ; australian championships } ; year } } = true
Output: select the rows whose championship record fuzzily matches to wimbledon . take the year record of this row . select the rows whose championship record fuzzily matches to australian championships . take the year record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; total pts } ; 142 } = true
Output: the average of the total pts record of all rows is 142 .
eq { count { filter_eq { all_rows ; state ( class ) ; south carolina } } ; 2 } = true
Output: select the rows whose state  class  record fuzzily matches to south carolina . the number of such rows is 2 .
and { only { filter_eq { all_rows ; result ; made january shortlist } } ; eq { hop { filter_eq { all_rows ; result ; made january shortlist } ; film title used in nomination } ; samson and delilah } } = true
Output: select the rows whose result record fuzzily matches to made january shortlist . there is only one such row in the table . the film title used in nomination record of this unqiue row is samson and delilah .
eq { hop { nth_argmin { all_rows ; first air date ; 2 } ; city } ; coffs harbour } = true
Output: select the row whose first air date record of all rows is 2nd minimum . the city record of this row is coffs harbour .
all_eq { all_rows ; reason for change ; died } = true
Output: for the reason for change records of all rows , all of them fuzzily match to died .
eq { count { filter_eq { filter_eq { all_rows ; score ; w } ; high points ; kevin martin } } ; 3 } = true
Output: select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high points record fuzzily matches to kevin martin . the number of such rows is 3 .
and { only { filter_eq { all_rows ; stadium ; heinz field } } ; eq { hop { filter_eq { all_rows ; stadium ; heinz field } ; week } ; 4 } } = true
Output: select the rows whose stadium record fuzzily matches to heinz field . there is only one such row in the table . the week record of this unqiue row is 4 .
eq { hop { argmax { all_rows ; passengers } ; city } ; atlanta , ga } = true
Output: select the row whose passengers record of all rows is maximum . the city record of this row is atlanta , ga .
eq { count { filter_all { all_rows ; team } } ; 12 } = true
Output: select the rows whose team record is arbitrary . the number of such rows is 12 .
and { only { filter_eq { all_rows ; children together ; 1 son } } ; and { eq { hop { filter_eq { all_rows ; children together ; 1 son } ; name } ; norris church mailer } ; eq { hop { filter_eq { all_rows ; children together ; 1 son } ; deceased spouse } ; norman mailer } } } = true
Output: select the rows whose children together record fuzzily matches to 1 son . there is only one such row in the table . the name record of this unqiue row is norris church mailer . the deceased spouse record of this unqiue row is norman mailer .
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
Output: 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 .
round_eq { sum { all_rows ; try bonus } ; 44 } = true
Output: the sum of the try bonus record of all rows is 44 .
and { only { filter_eq { all_rows ; events ; 0 } } ; eq { hop { filter_eq { all_rows ; events ; 0 } ; tournament } ; us open } } = true
Output: select the rows whose events record is equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is us open .
and { only { filter_eq { all_rows ; transfer window ; winter } } ; eq { hop { filter_eq { all_rows ; transfer window ; winter } ; name } ; burke } } = true
Output: select the rows whose transfer window record fuzzily matches to winter . there is only one such row in the table . the name record of this unqiue row is burke .
all_less { filter_eq { all_rows ; away team ; melbourne } ; crowd ; 25000 } = true
Output: select the rows whose away team record fuzzily matches to melbourne . for the crowd records of these rows , all of them are less than 25000 .
less { hop { filter_eq { all_rows ; name ; kara lynn joyce } ; time } ; hop { filter_eq { all_rows ; name ; aliaksandra herasimenia } ; time } } = true
Output: select the rows whose name record fuzzily matches to kara lynn joyce . take the time record of this row . select the rows whose name record fuzzily matches to aliaksandra herasimenia . take the time record of this row . the first record is less than the second record .
all_eq { all_rows ; l2 cache ; 512 kb } = true
Output: for the l2 cache records of all rows , all of them fuzzily match to 512 kb .
eq { hop { argmin { all_rows ; issue date ( s ) } ; artist } ; eiffel 65 } = true
Output: select the row whose issue date  s  record of all rows is minimum . the artist record of this row is eiffel 65 .
eq { hop { argmax { all_rows ; purse } ; year } ; 2008 } = true
Output: select the row whose purse record of all rows is maximum . the year record of this row is 2008 .
eq { hop { argmax { all_rows ; laps } ; year } ; 2003 } = true
Output: select the row whose laps record of all rows is maximum . the year record of this row is 2003 .
and { only { filter_greater { all_rows ; attendance ; 20000 } } ; and { eq { hop { filter_greater { all_rows ; attendance ; 20000 } ; acc team } ; 10 north carolina } ; eq { hop { filter_greater { all_rows ; attendance ; 20000 } ; big ten team } ; 9 michigan state } } } = true
Output: select the rows whose attendance record is greater than 20000 . there is only one such row in the table . the acc team record of this unqiue row is 10 north carolina . the big ten team record of this unqiue row is 9 michigan state .
round_eq { sum { all_rows ; t20 matches } ; 15 } = true
Output: the sum of the t20 matches record of all rows is 15 .
round_eq { avg { all_rows ; height } ; 198.77 } = true
Output: the average of the height record of all rows is 198.77 .
eq { hop { argmax { all_rows ; location attendance } ; date } ; february 21 } = true
Output: select the row whose location attendance record of all rows is maximum . the date record of this row is february 21 .
most_eq { all_rows ; leading scorer ; kevin martin } = true
Output: for the leading scorer records of all rows , most of them fuzzily match to kevin martin .
eq { count { filter_eq { all_rows ; first elected ; 197 } } ; 3 } = true
Output: select the rows whose first elected record fuzzily matches to 197 . the number of such rows is 3 .
most_greater { all_rows ; year ; 2010 } = true
Output: for the year records of all rows , most of them are greater than 2010 .
and { only { filter_eq { all_rows ; current affiliation ; nbc affiliate owned by gannett company } } ; eq { hop { filter_eq { all_rows ; current affiliation ; nbc affiliate owned by gannett company } ; station } ; kyw - tv ( now wkyc - tv ) } } = true
Output: select the rows whose current affiliation record fuzzily matches to nbc affiliate owned by gannett company . there is only one such row in the table . the station record of this unqiue row is kyw  tv  now wkyc  tv  .
and { only { filter_eq { all_rows ; position ; quarterback } } ; eq { hop { filter_eq { all_rows ; position ; quarterback } ; name } ; bob gagliano } } = true
Output: select the rows whose position record fuzzily matches to quarterback . there is only one such row in the table . the name record of this unqiue row is bob gagliano .
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
Output: 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 .
greater { hop { filter_eq { all_rows ; song ; endless love } ; weeks on top } ; hop { filter_eq { all_rows ; song ; morning train } ; weeks on top } } = true
Output: select the rows whose song record fuzzily matches to endless love . take the weeks on top record of this row . select the rows whose song record fuzzily matches to morning train . take the weeks on top record of this row . the first record is greater than the second record .
all_eq { filter_eq { all_rows ; date ; october } ; result ; w } = true
Output: select the rows whose date record fuzzily matches to october . for the result records of these rows , all of them fuzzily match to w .
and { only { filter_eq { all_rows ; tournament ; stuttgart , germany } } ; eq { hop { filter_eq { all_rows ; tournament ; stuttgart , germany } ; date } ; july 18 , 2005 } } = true
Output: select the rows whose tournament record fuzzily matches to stuttgart , germany . there is only one such row in the table . the date record of this unqiue row is july 18 , 2005 .
greater { hop { filter_eq { all_rows ; name ; mark boban } ; events won ( us series ) } ; hop { filter_eq { all_rows ; name ; corey rennell } ; events won ( us series ) } } = true
Output: select the rows whose name record fuzzily matches to mark boban . take the events won  us series  record of this row . select the rows whose name record fuzzily matches to corey rennell . take the events won  us series  record of this row . the first record is greater than the second record .
greater { hop { filter_eq { all_rows ; class ; i1 } ; no built } ; hop { filter_eq { all_rows ; class ; i2 } ; no built } } = true
Output: select the rows whose class record fuzzily matches to i1 . take the no built record of this row . select the rows whose class record fuzzily matches to i2 . take the no built record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; seasons ; 4 } } ; eq { hop { filter_eq { all_rows ; seasons ; 4 } ; country } ; south korea } } = true
Output: select the rows whose seasons record is equal to 4 . there is only one such row in the table . the country record of this unqiue row is south korea .
eq { count { filter_eq { all_rows ; runner - up ; fabrizio baldassari } } ; 1 } = true
Output: select the rows whose runner  up record fuzzily matches to fabrizio baldassari . the number of such rows is 1 .
eq { count { filter_greater { all_rows ; average ; 10000 } } ; 3 } = true
Output: select the rows whose average record is greater than 10000 . the number of such rows is 3 .
and { only { filter_greater { all_rows ; games played ; 100 } } ; eq { hop { filter_greater { all_rows ; games played ; 100 } ; rival } ; georgia } } = true
Output: select the rows whose games played record is greater than 100 . there is only one such row in the table . the rival record of this unqiue row is georgia .
eq { count { filter_eq { all_rows ; authority ; state integrated } } ; 1 } = true
Output: select the rows whose authority record fuzzily matches to state integrated . the number of such rows is 1 .
eq { count { filter_eq { all_rows ; type of game ; friendly } } ; 5 } = true
Output: select the rows whose type of game record fuzzily matches to friendly . the number of such rows is 5 .
greater { hop { filter_eq { all_rows ; airport ; tenerife sur } ; 2012 } ; hop { filter_eq { all_rows ; airport ; kotoka international airport } ; 2012 } } = true
Output: select the rows whose airport record fuzzily matches to tenerife sur . take the 2012 record of this row . select the rows whose airport record fuzzily matches to kotoka international airport . take the 2012 record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; time / retired ; mechanical } } ; eq { hop { filter_eq { all_rows ; time / retired ; mechanical } ; driver } ; tiago monteiro } } = true
Output: select the rows whose time  retired record fuzzily matches to mechanical . there is only one such row in the table . the driver record of this unqiue row is tiago monteiro .
and { only { filter_less { all_rows ; points margin ; 10 } } ; and { eq { hop { filter_less { all_rows ; points margin ; 10 } ; proceed to quarter - final } ; montauban } ; eq { hop { filter_less { all_rows ; points margin ; 10 } ; eliminated from competition } ; borders } } } = true
Output: select the rows whose points margin record is less than 10 . there is only one such row in the table . the proceed to quarter  final record of this unqiue row is montauban . the eliminated from competition record of this unqiue row is borders .
eq { count { filter_eq { all_rows ; party ; whig } } ; 1 } = true
Output: select the rows whose party record fuzzily matches to whig . the number of such rows is 1 .
eq { count { filter_eq { all_rows ; laps ; 200 } } ; 6 } = true
Output: select the rows whose laps record is equal to 200 . the number of such rows is 6 .
eq { hop { argmax { all_rows ; apogee } ; mission } ; maxus 1b } = true
Output: select the row whose apogee record of all rows is maximum . the mission record of this row is maxus 1b .
eq { count { filter_eq { all_rows ; date ; 12 july 1930 } } ; 6 } = true
Output: select the rows whose date record fuzzily matches to 12 july 1930 . the number of such rows is 6 .
most_eq { all_rows ; location ; california , united states } = true
Output: for the location records of all rows , most of them fuzzily match to california , united states .
most_eq { all_rows ; high assists ; ambrose } = true
Output: for the high assists records of all rows , most of them fuzzily match to ambrose .
all_eq { all_rows ; party leader ; habib bourguiba } = true
Output: for the party leader records of all rows , all of them fuzzily match to habib bourguiba .
only { filter_eq { all_rows ; outcome ; runner - up } } = true
Output: select the rows whose outcome record fuzzily matches to runner  up . there is only one such row in the table .
and { only { filter_eq { all_rows ; result ; nominee } } ; eq { hop { filter_eq { all_rows ; result ; nominee } ; film title used in nomination } ; before the rain } } = true
Output: select the rows whose result record fuzzily matches to nominee . there is only one such row in the table . the film title used in nomination record of this unqiue row is before the rain .
round_eq { avg { all_rows ; attendance } ; 16962 } = true
Output: the average of the attendance record of all rows is 16962 .
eq { hop { argmin { all_rows ; age } ; candidate } ; samuel sam hooper } = true
Output: select the row whose age record of all rows is minimum . the candidate record of this row is samuel sam hooper .
eq { hop { argmin { all_rows ; year born } ; player } ; goran jagodnik } = true
Output: select the row whose year born record of all rows is minimum . the player record of this row is goran jagodnik .
and { only { filter_eq { all_rows ; riaa certification ; gold } } ; eq { hop { filter_eq { all_rows ; riaa certification ; gold } ; year } ; 1979 } } = true
Output: select the rows whose riaa certification record fuzzily matches to gold . there is only one such row in the table . the year record of this unqiue row is 1979 .
eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; lake oval } = true
Output: select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is lake oval .
eq { hop { nth_argmin { all_rows ; ngc number ; 2 } ; constellation } ; chamaeleon } = true
Output: select the row whose ngc number record of all rows is 2nd minimum . the constellation record of this row is chamaeleon .
round_eq { sum { all_rows ; wickets } ; 90 } = true
Output: the sum of the wickets record of all rows is 90 .
round_eq { sum { all_rows ; points } ; 490 } = true
Output: the sum of the points record of all rows is 490 .
most_eq { all_rows ; character ; craven } = true
Output: for the character records of all rows , most of them fuzzily match to craven .
less { hop { filter_eq { all_rows ; incumbent ; jerry huckaby } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; buddy roemer } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to jerry huckaby . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to buddy roemer . take the first elected record of this row . the first record is less than the second record .
eq { min { filter_eq { all_rows ; result ; win } ; date } ; sept 23 } = true
Output: select the rows whose result record fuzzily matches to win . the minimum date record of these rows is sept 23 .
less { hop { filter_eq { all_rows ; version ; german radio edit } ; length } ; hop { filter_eq { all_rows ; version ; album version } ; length } } = true
Output: select the rows whose version record fuzzily matches to german radio edit . take the length record of this row . select the rows whose version record fuzzily matches to album version . take the length record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; nationality ; united states } } ; eq { hop { filter_eq { all_rows ; nationality ; united states } ; athlete } ; shawn crawford } } = true
Output: select the rows whose nationality record fuzzily matches to united states . there is only one such row in the table . the athlete record of this unqiue row is shawn crawford .
and { only { filter_eq { all_rows ; country ; canada } } ; eq { hop { filter_eq { all_rows ; country ; canada } ; year } ; 2011 } } = true
Output: select the rows whose country record fuzzily matches to canada . there is only one such row in the table . the year record of this unqiue row is 2011 .
eq { count { filter_greater { filter_eq { all_rows ; region ; central } ; pop density ; 1000 } } ; 3 } = true
Output: select the rows whose region record fuzzily matches to central . among these rows , select the rows whose pop density record is greater than 1000 . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; semifinalists ; 1 } } ; 3 } = true
Output: select the rows whose semifinalists record is equal to 1 . the number of such rows is 3 .
eq { hop { argmax { all_rows ; floors } ; name } ; commerzbank tower } = true
Output: select the row whose floors record of all rows is maximum . the name record of this row is commerzbank tower .
eq { count { filter_eq { filter_less { all_rows ; introduced ; 2000 } ; fleet size ; 2 } } ; 2 } = true
Output: select the rows whose introduced record is less than 2000 . among these rows , select the rows whose fleet size record is equal to 2 . the number of such rows is 2 .
round_eq { avg { all_rows ; secondary ( 14 - 17 years ) } ; 70.95 } = true
Output: the average of the secondary  14  17 years  record of all rows is 70.95 .
and { only { filter_eq { all_rows ; first elected ; 1801 } } ; eq { hop { filter_eq { all_rows ; first elected ; 1801 } ; incumbent } ; john smith } } = true
Output: select the rows whose first elected record is equal to 1801 . there is only one such row in the table . the incumbent record of this unqiue row is john smith .
eq { hop { nth_argmin { all_rows ; date ; 13 } ; race } ; italian grand prix } = true
Output: select the row whose date record of all rows is 13th minimum . the race record of this row is italian grand prix .
and { only { filter_eq { all_rows ; primary conference when joining the msfa ; whac } } ; eq { hop { filter_eq { all_rows ; primary conference when joining the msfa ; whac } ; institution } ; trine university } } = true
Output: select the rows whose primary conference when joining the msfa record fuzzily matches to whac . there is only one such row in the table . the institution record of this unqiue row is trine university .
eq { count { filter_eq { all_rows ; manner of departure ; resigned } } ; 2 } = true
Output: select the rows whose manner of departure record fuzzily matches to resigned . the number of such rows is 2 .
eq { hop { argmin { all_rows ; game } ; location attendance } ; pepsi center 14243 } = true
Output: select the row whose game record of all rows is minimum . the location attendance record of this row is pepsi center 14243 .
round_eq { avg { all_rows ; average } ; 5.5 } = true
Output: the average of the average record of all rows is 5.5 .
less { hop { filter_eq { all_rows ; player ; john daly } ; year ( s ) won } ; hop { filter_eq { all_rows ; player ; david toms } ; year ( s ) won } } = true
Output: select the rows whose player record fuzzily matches to john daly . take the year  s  won record of this row . select the rows whose player record fuzzily matches to david toms . take the year  s  won record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; viewers ( millions ) } ; airdate } ; sunday , june 1 , 2008 } = true
Output: select the row whose viewers  millions  record of all rows is maximum . the airdate record of this row is sunday , june 1 , 2008 .
only { filter_not_eq { all_rows ; competition ; friendly } } = true
Output: select the rows whose competition record does not match to friendly . there is only one such row in the table .
round_eq { avg { all_rows ; crowd } ; 12000 } = true
Output: the average of the crowd record of all rows is 12000 .
and { only { filter_greater { filter_eq { all_rows ; date ; 3 june 1935 } ; crowd ; 25000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; date ; 3 june 1935 } ; crowd ; 25000 } ; venue } ; windy hill } } = true
Output: select the rows whose date record fuzzily matches to 3 june 1935 . among these rows , select the rows whose crowd record is greater than 25000 . there is only one such row in the table . the venue record of this unqiue row is windy hill .
greater { hop { filter_eq { all_rows ; name ; arthur ashe } ; matches played } ; hop { filter_eq { all_rows ; name ; mark cox } ; matches played } } = true
Output: select the rows whose name record fuzzily matches to arthur ashe . take the matches played record of this row . select the rows whose name record fuzzily matches to mark cox . take the matches played record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; best score ; 30 } } ; 7 } = true
Output: select the rows whose best score record fuzzily matches to 30 . the number of such rows is 7 .
eq { count { filter_eq { all_rows ; challenge winner ; food } } ; 4 } = true
Output: select the rows whose challenge winner record fuzzily matches to food . the number of such rows is 4 .
most_less { all_rows ; remittances 2009 ; 23 } = true
Output: for the remittances 2009 records of all rows , most of them are less than 23 .
most_eq { all_rows ; partner ; nicolas mahut } = true
Output: for the partner records of all rows , most of them fuzzily match to nicolas mahut .
eq { count { filter_eq { all_rows ; high points ; elton brand } } ; 1 } = true
Output: select the rows whose high points record fuzzily matches to elton brand . the number of such rows is 1 .
eq { hop { nth_argmax { all_rows ; points ; 2 } ; song } ; für alle } = true
Output: select the row whose points record of all rows is 2nd maximum . the song record of this row is für alle .
most_eq { all_rows ; 1890 census ; na } = true
Output: for the 1890 census records of all rows , most of them fuzzily match to na .
eq { hop { argmax { all_rows ; sales } ; title } ; m } = true
Output: select the row whose sales record of all rows is maximum . the title record of this row is m .
most_eq { all_rows ; years ; 2001 - 2004 } = true
Output: for the years records of all rows , most of them fuzzily match to 2001  2004 .
eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; opponent } ; new york giants } = true
Output: select the row whose attendance record of all rows is 1st maximum . the opponent record of this row is new york giants .
eq { hop { argmax { all_rows ; attendance / g } ; season } ; 2004 } = true
Output: select the row whose attendance  g record of all rows is maximum . the season record of this row is 2004 .
and { only { filter_eq { all_rows ; format ; black gospel } } ; eq { hop { filter_eq { all_rows ; format ; black gospel } ; call letters } ; kees } } = true
Output: select the rows whose format record fuzzily matches to black gospel . there is only one such row in the table . the call letters record of this unqiue row is kees .
most_greater_eq { all_rows ; crowd ; 15000 } = true
Output: for the crowd records of all rows , most of them are greater than or equal to 15000 .
eq { hop { nth_argmax { all_rows ; founded ; 2 } ; school } ; coastal carolina university } = true
Output: select the row whose founded record of all rows is 2nd maximum . the school record of this row is coastal carolina university .
eq { hop { argmax { all_rows ; no of cu - o planes in unit cell } ; formula } ; tlba 2 ca 3 cu 4 o 11 } = true
Output: select the row whose no of cu  o planes in unit cell record of all rows is maximum . the formula record of this row is tlba 2 ca 3 cu 4 o 11 .
eq { max { all_rows ; drawn } ; 2 } = true
Output: the maximum drawn record of all rows is 2 .
and { only { filter_eq { all_rows ; location ; victoriaville , quebec , canada } } ; eq { hop { filter_eq { all_rows ; location ; victoriaville , quebec , canada } ; opponent } ; brian magee } } = true
Output: select the rows whose location record fuzzily matches to victoriaville , quebec , canada . there is only one such row in the table . the opponent record of this unqiue row is brian magee .
round_eq { sum { all_rows ; 1 usd = } ; 8429.43027 } = true
Output: the sum of the 1 usd = record of all rows is 8429.43027 .
most_eq { filter_less { all_rows ; capacity ; 10000 } ; 201112 season ; divisione b } = true
Output: select the rows whose capacity record is less than 10000 . for the 201112 season records of these rows , most of them fuzzily match to divisione b .
eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; carlton } = true
Output: select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is carlton .
less { hop { filter_eq { all_rows ; name ; frank borman } ; born } ; hop { filter_eq { all_rows ; name ; jim lovell } ; born } } = true
Output: select the rows whose name record fuzzily matches to frank borman . take the born record of this row . select the rows whose name record fuzzily matches to jim lovell . take the born record of this row . the first record is less than the second record .
and { eq { nth_min { all_rows ; year ; 2 } ; 1974 } ; eq { hop { nth_argmin { all_rows ; year ; 2 } ; partner } ; roscoe tanner } } = true
Output: the 2nd minimum year record of all rows is 1974 . the partner record of the row with 2nd minimum year record is roscoe tanner .
eq { hop { nth_argmin { all_rows ; maximum seating capacity ; 2 } ; arena / venue } ; quadricentennial pavilion } = true
Output: select the row whose maximum seating capacity record of all rows is 2nd minimum . the arena  venue record of this row is quadricentennial pavilion .
eq { hop { nth_argmax { all_rows ; total ; 2 } ; nation } ; argentina } = true
Output: select the row whose total record of all rows is 2nd maximum . the nation record of this row is argentina .
most_eq { all_rows ; engine ; i } = true
Output: for the engine records of all rows , most of them fuzzily match to i .
round_eq { avg { all_rows ; candidates } ; 57 % } = true
Output: the average of the candidates record of all rows is 57  .
and { only { filter_eq { all_rows ; language ; marathi } } ; eq { hop { filter_eq { all_rows ; language ; marathi } ; role } ; sonal } } = true
Output: select the rows whose language record fuzzily matches to marathi . there is only one such row in the table . the role record of this unqiue row is sonal .
and { eq { hop { argmax { all_rows ; crowd } ; home team } ; collingwood } ; eq { hop { argmax { all_rows ; crowd } ; away team } ; north melbourne } } = true
Output: select the row whose crowd record of all rows is maximum . the home team record of this row is collingwood . the away team record of this row is north melbourne .
eq { count { filter_eq { all_rows ; construction ; plastic } } ; 4 } = true
Output: select the rows whose construction record fuzzily matches to plastic . the number of such rows is 4 .
eq { hop { nth_argmax { all_rows ; height ; 3 } ; name } ; will hudson } = true
Output: select the row whose height record of all rows is 3rd maximum . the name record of this row is will hudson .
most_eq { all_rows ; decision ; esche } = true
Output: for the decision records of all rows , most of them fuzzily match to esche .
all_eq { all_rows ; air date ; 2006 } = true
Output: for the air date records of all rows , all of them fuzzily match to 2006 .
eq { count { filter_eq { all_rows ; wins ; 1 } } ; 2 } = true
Output: select the rows whose wins record is equal to 1 . the number of such rows is 2 .
eq { hop { nth_argmin { all_rows ; torque ; 2 } ; model } ; 320td ( diesel ) } = true
Output: select the row whose torque record of all rows is 2nd minimum . the model record of this row is 320td  diesel  .
all_eq { all_rows ; frequency ; am } = true
Output: for the frequency records of all rows , all of them fuzzily match to am .
eq { count { filter_eq { all_rows ; position ; rb } } ; 2 } = true
Output: select the rows whose position record fuzzily matches to rb . the number of such rows is 2 .
eq { hop { argmax { all_rows ; length } ; line color } ; orange } = true
Output: select the row whose length record of all rows is maximum . the line color record of this row is orange .
eq { hop { nth_argmax { all_rows ; points ; 3 } ; opponent } ; hartford whalers } = true
Output: select the row whose points record of all rows is 3rd maximum . the opponent record of this row is hartford whalers .
eq { hop { argmax { all_rows ; attendance } ; opponent } ; denver broncos } = true
Output: select the row whose attendance record of all rows is maximum . the opponent record of this row is denver broncos .
less { hop { filter_eq { all_rows ; club / team ; brisbane broncos } ; established } ; hop { filter_eq { all_rows ; club / team ; brisbane bandits } ; established } } = true
Output: select the rows whose club  team record fuzzily matches to brisbane broncos . take the established record of this row . select the rows whose club  team record fuzzily matches to brisbane bandits . take the established record of this row . the first record is less than the second record .
eq { count { filter_eq { filter_eq { all_rows ; film ; bullets over broadway } ; result ; nominated } } ; 2 } = true
Output: select the rows whose film record fuzzily matches to bullets over broadway . among these rows , select the rows whose result record fuzzily matches to nominated . the number of such rows is 2 .
eq { max { filter_greater_eq { all_rows ; year ; 1960 } ; rank } ; 17 } = true
Output: select the rows whose year record is greater than or equal to 1960 . the maximum rank record of these rows is 17 .
and { only { filter_eq { all_rows ; performer 2 ; nyima funk } } ; eq { hop { filter_eq { all_rows ; performer 2 ; nyima funk } ; original airdate } ; september 3 , 2013 } } = true
Output: select the rows whose performer 2 record fuzzily matches to nyima funk . there is only one such row in the table . the original airdate record of this unqiue row is september 3 , 2013 .
round_eq { avg { all_rows ; rank } ; 12.7 } = true
Output: the average of the rank record of all rows is 12.7 .
eq { hop { argmax { all_rows ; 2006 - 10 } ; state } ; bihar } = true
Output: select the row whose 2006  10 record of all rows is maximum . the state record of this row is bihar .
round_eq { avg { all_rows ; total } ; 152.8 } = true
Output: the average of the total record of all rows is 152.8 .
and { only { filter_eq { filter_eq { all_rows ; launch date ( ddmmyyyy ) ; 2005 } ; connection speed ; 236.8 kbit/s } } ; eq { hop { filter_eq { filter_eq { all_rows ; launch date ( ddmmyyyy ) ; 2005 } ; connection speed ; 236.8 kbit/s } ; carrier } ; moldcell } } = true
Output: select the rows whose launch date  ddmmyyyy  record fuzzily matches to 2005 . among these rows , select the rows whose connection speed record fuzzily matches to 236.8 kbits . there is only one such row in the table . the carrier record of this unqiue row is moldcell .
and { only { filter_eq { all_rows ; genre ; period drama } } ; eq { hop { filter_eq { all_rows ; genre ; period drama } ; english title ( chinese title ) } ; old time buddy - to catch a thief 難兄難弟之神探李奇 } } = true
Output: select the rows whose genre record fuzzily matches to period drama . there is only one such row in the table . the english title  chinese title  record of this unqiue row is old time buddy  to catch a thief 難兄難弟之神探李奇 .
most_eq { all_rows ; transfer fee ; free } = true
Output: for the transfer fee records of all rows , most of them fuzzily match to free .
greater { hop { filter_eq { all_rows ; driver ; clint bowyer } ; average speed ( mph ) } ; hop { filter_eq { all_rows ; driver ; greg biffle } ; average speed ( mph ) } } = true
Output: select the rows whose driver record fuzzily matches to clint bowyer . take the average speed  mph  record of this row . select the rows whose driver record fuzzily matches to greg biffle . take the average speed  mph  record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; result ; 5 } } ; 2 } = true
Output: select the rows whose result record fuzzily matches to 5 . the number of such rows is 2 .
and { only { filter_eq { all_rows ; country ; mexico } } ; eq { hop { filter_eq { all_rows ; country ; mexico } ; player } ; lorena ochoa } } = true
Output: select the rows whose country record fuzzily matches to mexico . there is only one such row in the table . the player record of this unqiue row is lorena ochoa .
eq { hop { argmax { all_rows ; multi lane } ; category wise } ; national highways } = true
Output: select the row whose multi lane record of all rows is maximum . the category wise record of this row is national highways .
eq { count { filter_eq { all_rows ; tries against ; 6 } } ; 2 } = true
Output: select the rows whose tries against record is equal to 6 . the number of such rows is 2 .
round_eq { avg { all_rows ; total } ; 146.4 } = true
Output: the average of the total record of all rows is 146.4 .
and { only { filter_eq { filter_eq { all_rows ; opponent ; detroit red wings } ; score ; 3-3 } } ; eq { hop { filter_eq { filter_eq { all_rows ; opponent ; detroit red wings } ; score ; 3-3 } ; december } ; 6 } } = true
Output: select the rows whose opponent record fuzzily matches to detroit red wings . among these rows , select the rows whose score record fuzzily matches to 33 . there is only one such row in the table . the december record of this unqiue row is 6 .
most_eq { all_rows ; sets lost ; 2 } = true
Output: for the sets lost records of all rows , most of them are equal to 2 .
eq { hop { nth_argmax { all_rows ; points ; 2 } ; club } ; chepstow rfc } = true
Output: select the row whose points record of all rows is 2nd maximum . the club record of this row is chepstow rfc .
round_eq { avg { all_rows ; mult } ; 3.90 90 repeating } = true
Output: the average of the mult record of all rows is 3.90 90 repeating .
eq { hop { nth_argmax { all_rows ; b score ; 1 } ; gymnast } ; ksenia semenova ( rus ) } = true
Output: select the row whose b score record of all rows is 1st maximum . the gymnast record of this row is ksenia semenova  rus  .
eq { hop { argmax { all_rows ; attendance } ; date } ; december 16 } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is december 16 .
most_eq { all_rows ; subregion ; baixo vouga } = true
Output: for the subregion records of all rows , most of them fuzzily match to baixo vouga .
eq { count { filter_eq { all_rows ; written by ; john sullivan } } ; 4 } = true
Output: select the rows whose written by record fuzzily matches to john sullivan . the number of such rows is 4 .
eq { count { filter_eq { filter_less { all_rows ; against ; 10 } ; status ; tour match } } ; 2 } = true
Output: select the rows whose against record is less than 10 . among these rows , select the rows whose status record fuzzily matches to tour match . the number of such rows is 2 .
eq { count { filter_eq { filter_eq { all_rows ; transfer fee ; free } ; moving from ; everton } } ; 1 } = true
Output: select the rows whose transfer fee record fuzzily matches to free . among these rows , select the rows whose moving from record fuzzily matches to everton . the number of such rows is 1 .
most_eq { all_rows ; rank ; 1st } = true
Output: for the rank records of all rows , most of them fuzzily match to 1st .
all_eq { all_rows ; round ; 1 } = true
Output: for the round records of all rows , all of them are equal to 1 .
less { hop { filter_eq { all_rows ; episode summary ; richard is made into boyfriend material } ; premier date } ; hop { filter_eq { all_rows ; episode summary ; lawryn is made into a bmx biker } ; premier date } } = true
Output: select the rows whose episode summary record fuzzily matches to richard is made into boyfriend material . take the premier date record of this row . select the rows whose episode summary record fuzzily matches to lawryn is made into a bmx biker . take the premier date record of this row . the first record is less than the second record .
eq { hop { nth_argmin { all_rows ; date ; 2 } ; opponent in the final } ; melinda czink } = true
Output: select the row whose date record of all rows is 2nd minimum . the opponent in the final record of this row is melinda czink .
round_eq { sum { all_rows ; wins } ; 18 } = true
Output: the sum of the wins record of all rows is 18 .
eq { hop { argmax { all_rows ; capacity ( mw ) } ; name } ; wairakei } = true
Output: select the row whose capacity  mw  record of all rows is maximum . the name record of this row is wairakei .
most_greater { all_rows ; crowd ; 3,000 } = true
Output: for the crowd records of all rows , most of them are greater than 3,000 .
round_eq { avg { all_rows ; td 's } ; 15 } = true
Output: the average of the td s record of all rows is 15 .
round_eq { sum { all_rows ; location attendance } ; 98934 } = true
Output: the sum of the location attendance record of all rows is 98934 .
greater { hop { filter_eq { all_rows ; away team ; richmond } ; away team score } ; hop { filter_eq { all_rows ; away team ; swans } ; away team score } } = true
Output: select the rows whose away team record fuzzily matches to richmond . take the away team score record of this row . select the rows whose away team record fuzzily matches to swans . take the away team score record of this row . the first record is greater than the second record .
greater { hop { filter_eq { all_rows ; team ; indians } ; division record } ; hop { filter_eq { all_rows ; team ; blue raiders } ; division record } } = true
Output: select the rows whose team record fuzzily matches to indians . take the division record record of this row . select the rows whose team record fuzzily matches to blue raiders . take the division record record of this row . the first record is greater than the second record .
most_eq { all_rows ; period ; 3rd } = true
Output: for the period records of all rows , most of them fuzzily match to 3rd .
greater { hop { filter_eq { all_rows ; name of county ; veszprém } ; area ( km square ) } ; hop { filter_eq { all_rows ; name of county ; tolna } ; area ( km square ) } } = true
Output: select the rows whose name of county record fuzzily matches to veszprém . take the area  km square  record of this row . select the rows whose name of county record fuzzily matches to tolna . take the area  km square  record of this row . the first record is greater than the second record .