niryuu/sni-each-converted huggingface dataset:
and { only { filter_eq { all_rows ; round of 16 ; 15-8 } } ; eq { hop { filter_eq { all_rows ; round of 16 ; 15-8 } ; athlete } ; alexander achten } } = true
Output: select the rows whose round of 16 record fuzzily matches to 158 . there is only one such row in the table . the athlete record of this unqiue row is alexander achten .
eq { hop { nth_argmin { all_rows ; competition ; 1 } ; goal } ; 1 } = true
Output: select the row whose competition record of all rows is 1st minimum . the goal record of this row is 1 .
eq { hop { nth_argmax { all_rows ; release price ( usd ) ; 3 } ; model number } ; core i7 - 820qm } = true
Output: select the row whose release price  usd  record of all rows is 3rd maximum . the model number record of this row is core i7  820qm .
eq { hop { argmax { all_rows ; weight } ; name } ; justin jeffries } = true
Output: select the row whose weight record of all rows is maximum . the name record of this row is justin jeffries .
and { only { filter_greater { all_rows ; gross ; 300000000 } } ; eq { hop { filter_greater { all_rows ; gross ; 300000000 } ; title } ; fatal attraction } } = true
Output: select the rows whose gross record is greater than 300000000 . there is only one such row in the table . the title record of this unqiue row is fatal attraction .
round_eq { avg { all_rows ; electors } ; 50.85 } = true
Output: the average of the electors record of all rows is 50.85 .
most_greater { filter_eq { all_rows ; date ; december } ; attendance ; 60000 } = true
Output: select the rows whose date record fuzzily matches to december . for the attendance records of these rows , most of them are greater than 60000 .
eq { count { filter_less { filter_eq { all_rows ; nation ; kenya } ; time ; 30:34 } } ; 2 } = true
Output: select the rows whose nation record fuzzily matches to kenya . among these rows , select the rows whose time record is less than 30:34 . the number of such rows is 2 .
eq { diff { hop { filter_eq { all_rows ; date ; november 25 } ; rank } ; hop { filter_eq { all_rows ; date ; november 18 } ; rank } } ; -1 } = true
Output: select the rows whose date record fuzzily matches to november 25 . take the rank record of this row . select the rows whose date record fuzzily matches to november 18 . take the rank record of this row . the second record is 1 larger than the first record .
eq { hop { nth_argmax { all_rows ; winnings ; 2 } ; year } ; 2003 } = true
Output: select the row whose winnings record of all rows is 2nd maximum . the year record of this row is 2003 .
eq { count { filter_greater { all_rows ; crowd ; 20000 } } ; 4 } = true
Output: select the rows whose crowd record is greater than 20000 . the number of such rows is 4 .
round_eq { sum { all_rows ; number of families } ; 30630 } = true
Output: the sum of the number of families record of all rows is 30630 .
greater { hop { filter_eq { all_rows ; player ; francisco elson } ; years } ; hop { filter_eq { all_rows ; player ; pervis ellison } ; years } } = true
Output: select the rows whose player record fuzzily matches to francisco elson . take the years record of this row . select the rows whose player record fuzzily matches to pervis ellison . take the years record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; points } ; team } ; corinthians } = true
Output: select the row whose points record of all rows is maximum . the team record of this row is corinthians .
and { only { filter_eq { all_rows ; content ; arte } } ; eq { hop { filter_eq { all_rows ; content ; arte } ; television service } ; diprè tv } } = true
Output: select the rows whose content record fuzzily matches to arte . there is only one such row in the table . the television service record of this unqiue row is diprè tv .
eq { count { filter_eq { all_rows ; champions ; laura montalvo } } ; 3 } = true
Output: select the rows whose champions record fuzzily matches to laura montalvo . the number of such rows is 3 .
and { only { filter_eq { filter_eq { all_rows ; away team ; melbourne } ; venue ; windy hill } } ; eq { hop { filter_eq { filter_eq { all_rows ; away team ; melbourne } ; venue ; windy hill } ; home team } ; essendon } } = true
Output: select the rows whose away team record fuzzily matches to melbourne . among these rows , select the rows whose venue record fuzzily matches to windy hill . there is only one such row in the table . the home team record of this unqiue row is essendon .
less { hop { filter_eq { all_rows ; outgoing manager ; karol marko } ; date of vacancy } ; hop { filter_eq { all_rows ; outgoing manager ; ivan vrabec } ; date of vacancy } } = true
Output: select the rows whose outgoing manager record fuzzily matches to karol marko . take the date of vacancy record of this row . select the rows whose outgoing manager record fuzzily matches to ivan vrabec . take the date of vacancy record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; top 10 } ; year } ; 2009 } = true
Output: select the row whose top 10 record of all rows is maximum . the year record of this row is 2009 .
eq { hop { nth_argmax { all_rows ; attendance ; 4 } ; date } ; december 13 } = true
Output: select the row whose attendance record of all rows is 4th maximum . the date record of this row is december 13 .
greater { hop { filter_eq { all_rows ; model ; stls2f } ; frequency } ; hop { filter_eq { all_rows ; model ; stls2e } ; frequency } } = true
Output: select the rows whose model record fuzzily matches to stls2f . take the frequency record of this row . select the rows whose model record fuzzily matches to stls2e . take the frequency record of this row . the first record is greater than the second record .
eq { diff { hop { filter_eq { all_rows ; name ; ray lindwall } ; wickets } ; hop { filter_eq { all_rows ; name ; clarrie grimmett } ; wickets } } ; 12 } = true
Output: select the rows whose name record fuzzily matches to ray lindwall . take the wickets record of this row . select the rows whose name record fuzzily matches to clarrie grimmett . take the wickets record of this row . the first record is 12 larger than the second record .
eq { count { filter_greater { all_rows ; total ; 2 } } ; 3 } = true
Output: select the rows whose total record is greater than 2 . the number of such rows is 3 .
eq { count { filter_all { all_rows ; school } } ; 6 } = true
Output: select the rows whose school record is arbitrary . the number of such rows is 6 .
eq { hop { nth_argmax { filter_less { all_rows ; area ( km 2 ) ; 50 } ; population ; 1 } ; common of } ; moncalieri } = true
Output: select the rows whose area  km 2  record is less than 50 . select the row whose population record of these rows is 1st maximum . the common of record of this row is moncalieri .
round_eq { avg { all_rows ; prize ( k ) } ; 219.6 } = true
Output: the average of the prize  k  record of all rows is 219.6 .
eq { hop { argmax { all_rows ; entries } ; driver } ; rubens barrichello } = true
Output: select the row whose entries record of all rows is maximum . the driver record of this row is rubens barrichello .
round_eq { avg { all_rows ; score } ; 279 } = true
Output: the average of the score record of all rows is 279 .
eq { hop { argmin { all_rows ; date } ; game site } ; waldstadion } = true
Output: select the row whose date record of all rows is minimum . the game site record of this row is waldstadion .
eq { hop { argmin { filter_eq { all_rows ; tv station ; fuji tv } ; average ratings } ; romaji title } ; rikon bengoshi ii ~ handsome woman ~ } = true
Output: select the rows whose tv station record fuzzily matches to fuji tv . select the row whose average ratings record of these rows is minimum . the romaji title record of this row is rikon bengoshi ii  handsome woman  .
eq { count { filter_eq { filter_eq { all_rows ; country ; united states } ; score ; 138 } } ; 4 } = true
Output: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose score record fuzzily matches to 138 . the number of such rows is 4 .
and { only { filter_eq { all_rows ; directed by ; alison maclean } } ; eq { hop { filter_eq { all_rows ; directed by ; alison maclean } ; title } ; the river } } = true
Output: select the rows whose directed by record fuzzily matches to alison maclean . there is only one such row in the table . the title record of this unqiue row is the river .
eq { hop { nth_argmax { all_rows ; goal difference ; 3 } ; club } ; real murcia } = true
Output: select the row whose goal difference record of all rows is 3rd maximum . the club record of this row is real murcia .
eq { count { filter_greater { filter_eq { all_rows ; college / junior / club team ; wchl } ; round ; 2 } } ; 2 } = true
Output: select the rows whose college  junior  club team record fuzzily matches to wchl . among these rows , select the rows whose round record is greater than 2 . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; october 8 , 2001 } = true
Output: select the row whose attendance record of all rows is 2nd maximum . the date record of this row is october 8 , 2001 .
eq { hop { argmax { all_rows ; home team score } ; home team } ; carlton } = true
Output: select the row whose home team score record of all rows is maximum . the home team record of this row is carlton .
eq { count { filter_eq { filter_eq { all_rows ; start source ; bbc sport } ; loan club ; dundee united } } ; 2 } = true
Output: select the rows whose start source record fuzzily matches to bbc sport . among these rows , select the rows whose loan club record fuzzily matches to dundee united . the number of such rows is 2 .
and { only { filter_eq { all_rows ; college ; san diego state } } ; eq { hop { filter_eq { all_rows ; college ; san diego state } ; name } ; freddie keiaho } } = true
Output: select the rows whose college record fuzzily matches to san diego state . there is only one such row in the table . the name record of this unqiue row is freddie keiaho .
and { only { filter_less { all_rows ; laps ; 20 } } ; eq { hop { filter_less { all_rows ; laps ; 20 } ; name } ; ryan dalziel } } = true
Output: select the rows whose laps record is less than 20 . there is only one such row in the table . the name record of this unqiue row is ryan dalziel .
most_greater { all_rows ; gross tonnage ; 2000 } = true
Output: for the gross tonnage records of all rows , most of them are greater than 2000 .
most_greater { filter_eq { all_rows ; location attendance ; liacouras center } ; location attendance ; 5000 } = true
Output: 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 .
eq { hop { argmax { all_rows ; population } ; name } ; chibougamau } = true
Output: select the row whose population record of all rows is maximum . the name record of this row is chibougamau .
eq { hop { argmin { all_rows ; year built } ; church name } ; kinnakyrkje } = true
Output: select the row whose year built record of all rows is minimum . the church name record of this row is kinnakyrkje .
eq { count { filter_eq { all_rows ; runner - up ; kanto gakuin university } } ; 4 } = true
Output: select the rows whose runner  up record fuzzily matches to kanto gakuin university . the number of such rows is 4 .
and { only { filter_less { filter_eq { all_rows ; date ; october } ; attendance ; 70000 } } ; eq { hop { filter_less { filter_eq { all_rows ; date ; october } ; attendance ; 70000 } ; opponent } ; st louis rams } } = true
Output: select the rows whose date record fuzzily matches to october . among these rows , select the rows whose attendance record is less than 70000 . there is only one such row in the table . the opponent record of this unqiue row is st louis rams .
eq { hop { nth_argmin { all_rows ; attendance ; 2 } ; date } ; january 2 , 2005 } = true
Output: select the row whose attendance record of all rows is 2nd minimum . the date record of this row is january 2 , 2005 .
round_eq { avg { all_rows ; population } ; 65841 } = true
Output: the average of the population record of all rows is 65841 .
eq { hop { nth_argmax { all_rows ; c_z ( metre ) ; 3 } ; region } ; slovenia etrs89 } = true
Output: select the row whose c_z  metre  record of all rows is 3rd maximum . the region record of this row is slovenia etrs89 .
and { only { filter_eq { all_rows ; type ; occupational } } ; eq { hop { filter_eq { all_rows ; type ; occupational } ; surname } ; møller } } = true
Output: select the rows whose type record fuzzily matches to occupational . there is only one such row in the table . the surname record of this unqiue row is møller .
eq { count { filter_eq { filter_eq { all_rows ; laps ; 334 } ; manufacturer ; chevrolet } } ; 2 } = true
Output: select the rows whose laps record is equal to 334 . among these rows , select the rows whose manufacturer record fuzzily matches to chevrolet . the number of such rows is 2 .
round_eq { sum { filter_eq { all_rows ; date ; 1948 } ; round } ; 4 } = true
Output: select the rows whose date record fuzzily matches to 1948 . the sum of the round record of these rows is 4 .
eq { count { filter_eq { filter_eq { all_rows ; sport ; association football } ; country ; england } } ; 6 } = true
Output: select the rows whose sport record fuzzily matches to association football . among these rows , select the rows whose country record fuzzily matches to england . the number of such rows is 6 .
eq { hop { argmin { filter_eq { all_rows ; date ; 2006 } ; date } ; opponent } ; josipa bek } = true
Output: select the rows whose date record fuzzily matches to 2006 . select the row whose date record of these rows is minimum . the opponent record of this row is josipa bek .
greater { hop { filter_eq { all_rows ; opposing teams ; scotland } ; against } ; hop { filter_eq { all_rows ; opposing teams ; wales } ; against } } = true
Output: select the rows whose opposing teams record fuzzily matches to scotland . take the against record of this row . select the rows whose opposing teams record fuzzily matches to wales . take the against record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; height ft ( m ) ; 2 } ; name } ; fifth third center } = true
Output: select the row whose height ft  m  record of all rows is 2nd maximum . the name record of this row is fifth third center .
eq { hop { nth_argmin { all_rows ; released ; 2 } ; codename } ; sb700 } = true
Output: select the row whose released record of all rows is 2nd minimum . the codename record of this row is sb700 .
greater { hop { filter_eq { all_rows ; name ; padyak pinoy tour pilipinas } ; distance } ; hop { filter_eq { all_rows ; name ; fedex tour of calabarzon } ; distance } } = true
Output: select the rows whose name record fuzzily matches to padyak pinoy tour pilipinas . take the distance record of this row . select the rows whose name record fuzzily matches to fedex tour of calabarzon . take the distance record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; enrollment } ; institution } ; indiana university southeast } = true
Output: select the row whose enrollment record of all rows is maximum . the institution record of this row is indiana university southeast .
eq { hop { argmax { filter_eq { all_rows ; high rebounds ; a horford } ; location attendance } ; date } ; december 8 } = true
Output: select the rows whose high rebounds record fuzzily matches to a horford . select the row whose location attendance record of these rows is maximum . the date record of this row is december 8 .
eq { count { filter_eq { all_rows ; country of origin ; united kingdom } } ; 4 } = true
Output: select the rows whose country of origin record fuzzily matches to united kingdom . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; margin of victory ; playoff } } ; 3 } = true
Output: select the rows whose margin of victory record fuzzily matches to playoff . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; kiplinger 's top 100 values ; n / a } } ; 6 } = true
Output: select the rows whose kiplinger s top 100 values record fuzzily matches to n  a . the number of such rows is 6 .
eq { count { filter_greater { all_rows ; 2013 population ( july est ) ; 7000000 } } ; 2 } = true
Output: select the rows whose 2013 population  july est  record is greater than 7000000 . the number of such rows is 2 .
eq { nth_max { all_rows ; points ; 2 } ; 48 } = true
Output: the 2nd maximum points record of all rows is 48 .
eq { hop { filter_eq { all_rows ; incumbent ; george h tinkham } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; james a gallivan } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to george h tinkham . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to james a gallivan . take the first elected record of this row . the first record is equal to the second record .
eq { count { filter_greater_eq { filter_eq { all_rows ; position ; forward } ; years in orlando ; 2000 } } ; 3 } = true
Output: select the rows whose position record fuzzily matches to forward . among these rows , select the rows whose years in orlando record is greater than or equal to 2000 . the number of such rows is 3 .
and { only { filter_eq { all_rows ; score ; 0-0 } } ; eq { hop { filter_eq { all_rows ; score ; 0-0 } ; attendance } ; 14 february 1999 } } = true
Output: select the rows whose score record fuzzily matches to 00 . there is only one such row in the table . the attendance record of this unqiue row is 14 february 1999 .
eq { hop { nth_argmax { all_rows ; debut ; 3 } ; ring name } ; kyokushūhō kōki } = true
Output: select the row whose debut record of all rows is 3rd maximum . the ring name record of this row is kyokushūhō kōki .
eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; s otis bland } = true
Output: select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is s otis bland .
eq { hop { nth_argmax { all_rows ; silver ; 2 } ; nation } ; hungary } = true
Output: select the row whose silver record of all rows is 2nd maximum . the nation record of this row is hungary .
round_eq { sum { all_rows ; points } ; 0 } = true
Output: the sum of the points record of all rows is 0 .
eq { hop { argmax { all_rows ; casualties } ; date } ; 2009 - 09 - 16 } = true
Output: select the row whose casualties record of all rows is maximum . the date record of this row is 2009  09  16 .
eq { hop { argmax { all_rows ; location attendance } ; date } ; march 16 } = true
Output: select the row whose location attendance record of all rows is maximum . the date record of this row is march 16 .
eq { hop { argmax { all_rows ; attendance } ; week } ; 8 } = true
Output: select the row whose attendance record of all rows is maximum . the week record of this row is 8 .
and { only { filter_eq { all_rows ; host ; hopton - on - sea } } ; eq { hop { filter_eq { all_rows ; host ; hopton - on - sea } ; season } ; 1998 / 99 } } = true
Output: select the rows whose host record fuzzily matches to hopton  on  sea . there is only one such row in the table . the season record of this unqiue row is 1998  99 .
eq { hop { argmax { all_rows ; duration ( days ) } ; expedition } ; salyut 7 - eo - 3 } = true
Output: select the row whose duration  days  record of all rows is maximum . the expedition record of this row is salyut 7  eo  3 .
most_greater { all_rows ; viewing figure ; 7 } = true
Output: for the viewing figure records of all rows , most of them are greater than 7 .
round_eq { avg { all_rows ; first elected } ; 1819 } = true
Output: the average of the first elected record of all rows is 1819 .
eq { hop { nth_argmax { all_rows ; overall ; 2 } ; name } ; wayne millner } = true
Output: select the row whose overall record of all rows is 2nd maximum . the name record of this row is wayne millner .
all_eq { filter_eq { all_rows ; episodes ; 24 } ; timeslot ( est ) ; tuesday 8:00 pm } = true
Output: select the rows whose episodes record is equal to 24 . for the timeslot  est  records of these rows , all of them fuzzily match to tuesday 8:00 pm .
eq { hop { nth_argmin { all_rows ; result ; 3 } ; year } ; 1996 } = true
Output: select the row whose result record of all rows is 3rd minimum . the year record of this row is 1996 .
eq { hop { nth_argmin { all_rows ; win % ; 2 } ; manager } ; horacio casarin } = true
Output: select the row whose win  record of all rows is 2nd minimum . the manager record of this row is horacio casarin .
round_eq { avg { filter_greater { all_rows ; founded ; 1850 } ; enrollment } ; 2939 } = true
Output: select the rows whose founded record is greater than 1850 . the average of the enrollment record of these rows is 2939 .
eq { hop { nth_argmin { all_rows ; average ; 2 } ; goalkeeper } ; jacobo } = true
Output: 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 ; episode number ; 1 } ; musical guest ( song performed ) } ; the courteeners ( no you did n't , no you do n't ) } = true
Output: select the row whose episode number record of all rows is 1st minimum . the musical guest  song performed  record of this row is the courteeners  no you did nt , no you do nt  .
most_eq { all_rows ; year ; 1996 } = true
Output: for the year records of all rows , most of them are equal to 1996 .
greater { hop { filter_eq { all_rows ; year ; 2000 } ; position } ; hop { filter_eq { all_rows ; year ; 2004 } ; position } } = true
Output: select the rows whose year record fuzzily matches to 2000 . take the position record of this row . select the rows whose year record fuzzily matches to 2004 . take the position record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; enrollment } ; 1432 } = true
Output: the average of the enrollment record of all rows is 1432 .
round_eq { avg { filter_eq { all_rows ; division ; southeast } ; pts } ; 75.5 } = true
Output: select the rows whose division record fuzzily matches to southeast . the average of the pts record of these rows is 75.5 .
and { only { filter_eq { filter_eq { all_rows ; original air date ; march } ; directed by ; andy wolk } } ; eq { hop { filter_eq { filter_eq { all_rows ; original air date ; march } ; directed by ; andy wolk } ; episode title } ; all the world 's a stage } } = true
Output: select the rows whose original air date record fuzzily matches to march . among these rows , select the rows whose directed by record fuzzily matches to andy wolk . there is only one such row in the table . the episode title record of this unqiue row is all the world s a stage .
eq { hop { nth_argmax { all_rows ; original air date ( uk ) ; 1 } ; episode title } ; loyalties ii } = true
Output: select the row whose original air date  uk  record of all rows is 1st maximum . the episode title record of this row is loyalties ii .
most_less { all_rows ; date made ; 1900 } = true
Output: for the date made records of all rows , most of them are less than 1900 .
eq { hop { nth_argmax { all_rows ; margin ; 2 } ; club } ; geelong } = true
Output: select the row whose margin record of all rows is 2nd maximum . the club record of this row is geelong .
round_eq { sum { filter_greater { all_rows ; points against ; 500 } ; lost } ; 44 } = true
Output: select the rows whose points against record is greater than 500 . the sum of the lost record of these rows is 44 .
round_eq { avg { all_rows ; revenues ( us billion ) } ; 7.9 billion } = true
Output: the average of the revenues  us billion  record of all rows is 7.9 billion .
greater { hop { filter_eq { all_rows ; driver / passenger ; daniãl willemsen / sven verbrugge 1 } ; points } ; hop { filter_eq { all_rows ; driver / passenger ; janis daiders / lauris daiders } ; points } } = true
Output: select the rows whose driver  passenger record fuzzily matches to daniãl willemsen  sven verbrugge 1 . take the points record of this row . select the rows whose driver  passenger record fuzzily matches to janis daiders  lauris daiders . take the points record of this row . the first record is greater than the second record .
round_eq { sum { all_rows ; crowd } ; 105,000 } = true
Output: the sum of the crowd record of all rows is 105,000 .
round_eq { avg { all_rows ; % of popular vote } ; 2.21 } = true
Output: the average of the  of popular vote record of all rows is 2.21 .
and { only { filter_eq { all_rows ; school / country ; santa clara } } ; eq { hop { filter_eq { all_rows ; school / country ; santa clara } ; player } ; dennis awtrey } } = true
Output: select the rows whose school  country record fuzzily matches to santa clara . there is only one such row in the table . the player record of this unqiue row is dennis awtrey .
and { only { filter_eq { all_rows ; episodes ; 18 } } ; eq { hop { filter_eq { all_rows ; episodes ; 18 } ; season } ; 8 } } = true
Output: select the rows whose episodes record is equal to 18 . there is only one such row in the table . the season record of this unqiue row is 8 .
greater { hop { filter_eq { all_rows ; mountain peak ; pikes peak } ; location } ; hop { filter_eq { all_rows ; mountain peak ; blanca peak } ; location } } = true
Output: select the rows whose mountain peak record fuzzily matches to pikes peak . take the location record of this row . select the rows whose mountain peak record fuzzily matches to blanca peak . take the location record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; north melbourne } = true
Output: select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is north melbourne .
only { filter_eq { filter_eq { all_rows ; event ; quiksilver pro } ; country ; australia } } = true
Output: select the rows whose event record fuzzily matches to quiksilver pro . among these rows , select the rows whose country record fuzzily matches to australia . there is only one such row in the table .
eq { count { filter_greater { all_rows ; points ; 20 } } ; 3 } = true
Output: select the rows whose points record is greater than 20 . the number of such rows is 3 .
and { only { filter_eq { all_rows ; acquisition via ; rookie draft } } ; eq { hop { filter_eq { all_rows ; acquisition via ; rookie draft } ; name } ; ronjay buenafe } } = true
Output: select the rows whose acquisition via record fuzzily matches to rookie draft . there is only one such row in the table . the name record of this unqiue row is ronjay buenafe .
eq { hop { argmax { all_rows ; popular votes } ; candidate } ; dennis peron } = true
Output: select the row whose popular votes record of all rows is maximum . the candidate record of this row is dennis peron .
greater { hop { filter_eq { all_rows ; player ; mitchell johnson } ; innings } ; hop { filter_eq { all_rows ; player ; shane lee } ; innings } } = true
Output: select the rows whose player record fuzzily matches to mitchell johnson . take the innings record of this row . select the rows whose player record fuzzily matches to shane lee . take the innings record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; opponent ; royals } } ; 7 } = true
Output: select the rows whose opponent record fuzzily matches to royals . the number of such rows is 7 .
round_eq { sum { all_rows ; points } ; 626 } = true
Output: the sum of the points record of all rows is 626 .
greater { hop { filter_eq { all_rows ; artist ; lola novaković } ; points } ; hop { filter_eq { all_rows ; artist ; claudio villa } ; points } } = true
Output: select the rows whose artist record fuzzily matches to lola novaković . take the points record of this row . select the rows whose artist record fuzzily matches to claudio villa . take the points record of this row . the first record is greater than the second record .
and { only { filter_greater { all_rows ; year ; 2005 } } ; eq { hop { filter_greater { all_rows ; year ; 2005 } ; version } ; live version ( recorded in 2006 ) } } = true
Output: select the rows whose year record is greater than 2005 . there is only one such row in the table . the version record of this unqiue row is live version  recorded in 2006  .
eq { count { filter_eq { all_rows ; country ; australia } } ; 3 } = true
Output: select the rows whose country record fuzzily matches to australia . the number of such rows is 3 .
most_eq { all_rows ; northumberland senior cup ; newcastle united reserves } = true
Output: for the northumberland senior cup records of all rows , most of them fuzzily match to newcastle united reserves .
eq { hop { argmax { all_rows ; total } ; location } ; plainview } = true
Output: select the row whose total record of all rows is maximum . the location record of this row is plainview .
greater { hop { filter_eq { all_rows ; vessel ; lublin class } ; in service } ; hop { filter_eq { all_rows ; vessel ; deba class } ; in service } } = true
Output: select the rows whose vessel record fuzzily matches to lublin class . take the in service record of this row . select the rows whose vessel record fuzzily matches to deba class . take the in service record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; type ; fraternity } } ; 4 } = true
Output: select the rows whose type record fuzzily matches to fraternity . the number of such rows is 4 .
eq { hop { argmax { all_rows ; points } ; driver } ; bruno junqueira } = true
Output: select the row whose points record of all rows is maximum . the driver record of this row is bruno junqueira .
eq { hop { nth_argmin { all_rows ; overall ; 2 } ; name } ; jerome pathon } = true
Output: select the row whose overall record of all rows is 2nd minimum . the name record of this row is jerome pathon .
eq { min { filter_eq { all_rows ; opponent ; baltimore colts } ; attendance } ; 57808 } = true
Output: select the rows whose opponent record fuzzily matches to baltimore colts . the minimum attendance record of these rows is 57808 .
most_greater { all_rows ; goodman ; 8 } = true
Output: for the goodman records of all rows , most of them are greater than 8 .
eq { hop { argmin { all_rows ; orbital period } ; planet } ; alpha centauri bb } = true
Output: select the row whose orbital period record of all rows is minimum . the planet record of this row is alpha centauri bb .
eq { count { filter_eq { all_rows ; score ; w } } ; 9 } = true
Output: select the rows whose score record fuzzily matches to w . the number of such rows is 9 .
and { only { filter_less { filter_greater_eq { all_rows ; total ( kg ) ; 330 } ; snatch ; 150 } } ; eq { hop { filter_less { filter_greater_eq { all_rows ; total ( kg ) ; 330 } ; snatch ; 150 } ; name } ; juan quiterio ( dom ) } } = true
Output: select the rows whose total  kg  record is greater than or equal to 330 . among these rows , select the rows whose snatch record is less than 150 . there is only one such row in the table . the name record of this unqiue row is juan quiterio  dom  .
round_eq { avg { all_rows ; points } ; 34 } = true
Output: the average of the points record of all rows is 34 .
eq { count { filter_eq { all_rows ; release price ( usd ) ; 999 } } ; 2 } = true
Output: select the rows whose release price  usd  record is equal to 999 . the number of such rows is 2 .
all_eq { all_rows ; date ; 26 august 1972 } = true
Output: for the date records of all rows , all of them fuzzily match to 26 august 1972 .
and { only { filter_less { all_rows ; points ; 100 } } ; eq { hop { filter_less { all_rows ; points ; 100 } ; artist } ; giorgos gavriel } } = true
Output: select the rows whose points record is less than 100 . there is only one such row in the table . the artist record of this unqiue row is giorgos gavriel .
eq { count { filter_eq { all_rows ; sixes ; 8 } } ; 2 } = true
Output: select the rows whose sixes record is equal to 8 . the number of such rows is 2 .
eq { diff { hop { filter_eq { all_rows ; title ; episode 4 } ; original air date } ; hop { filter_eq { all_rows ; title ; episode 5 } ; original air date } } ; -7 } = true
Output: select the rows whose title record fuzzily matches to episode 4 . take the original air date record of this row . select the rows whose title record fuzzily matches to episode 5 . take the original air date record of this row . the second record is 7 larger than the first record .
most_eq { all_rows ; played ; 14 } = true
Output: for the played records of all rows , most of them are equal to 14 .
all_eq { all_rows ; date ; 1 may 1926 } = true
Output: for the date records of all rows , all of them fuzzily match to 1 may 1926 .
eq { diff { hop { filter_eq { all_rows ; score ; 81 - 104 } ; date } ; hop { filter_eq { all_rows ; score ; 77 - 96 } ; date } } ; -3 days } = true
Output: select the rows whose score record fuzzily matches to 81  104 . take the date record of this row . select the rows whose score record fuzzily matches to 77  96 . take the date record of this row . the second record is 3 days larger than the first record .
most_less { all_rows ; issue price ; 100.0 } = true
Output: for the issue price records of all rows , most of them are less than 100.0 .
eq { count { filter_eq { all_rows ; college ; mcmaster } } ; 3 } = true
Output: select the rows whose college record fuzzily matches to mcmaster . the number of such rows is 3 .
round_eq { avg { all_rows ; drawn } ; 2.55 } = true
Output: the average of the drawn record of all rows is 2.55 .
greater { hop { filter_eq { all_rows ; opponent ; buffalo bills } ; result } ; hop { filter_eq { all_rows ; opponent ; miami dolphins } ; result } } = true
Output: select the rows whose opponent record fuzzily matches to buffalo bills . take the result record of this row . select the rows whose opponent record fuzzily matches to miami dolphins . take the result record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; capacity } ; team } ; dinamo minsk } = true
Output: select the row whose capacity record of all rows is maximum . the team record of this row is dinamo minsk .
eq { hop { argmax { all_rows ; crowd } ; venue } ; kardinia park } = true
Output: select the row whose crowd record of all rows is maximum . the venue record of this row is kardinia park .
less { hop { filter_eq { all_rows ; year ; 1969 } ; start } ; hop { filter_eq { all_rows ; year ; 1970 } ; start } } = true
Output: select the rows whose year record fuzzily matches to 1969 . take the start record of this row . select the rows whose year record fuzzily matches to 1970 . take the start record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; population ( 2011 ) } ; 706.73 } = true
Output: the average of the population  2011  record of all rows is 706.73 .
eq { hop { argmax { all_rows ; attendance } ; week } ; 15 } = true
Output: select the row whose attendance record of all rows is maximum . the week record of this row is 15 .
eq { hop { argmin { all_rows ; time } ; race } ; dance design ebf maiden } = true
Output: select the row whose time record of all rows is minimum . the race record of this row is dance design ebf maiden .
eq { hop { argmax { all_rows ; % of popular vote } ; election } ; 2001 } = true
Output: select the row whose  of popular vote record of all rows is maximum . the election record of this row is 2001 .
eq { count { filter_eq { all_rows ; winner ; russell ingall } } ; 3 } = true
Output: select the rows whose winner record fuzzily matches to russell ingall . the number of such rows is 3 .
round_eq { sum { filter_eq { all_rows ; place ; t8 } ; money } ; $ 379,000 } = true
Output: select the rows whose place record fuzzily matches to t8 . the sum of the money record of these rows is  379,000 .
all_eq { all_rows ; dominant religion ( 2002 ) ; orthodox christianity } = true
Output: for the dominant religion  2002  records of all rows , all of them fuzzily match to orthodox christianity .
all_eq { all_rows ; network ; abc } = true
Output: for the network records of all rows , all of them fuzzily match to abc .
eq { hop { nth_argmin { filter_eq { all_rows ; medal ; gold } ; games ; 2 } ; event } ; men 's épée } = true
Output: select the rows whose medal record fuzzily matches to gold . select the row whose games record of these rows is 2nd minimum . the event record of this row is men s épée .
eq { count { filter_all { all_rows ; name } } ; 12 } = true
Output: select the rows whose name record is arbitrary . the number of such rows is 12 .
only { filter_eq { all_rows ; winner ; ita } } = true
Output: select the rows whose winner record fuzzily matches to ita . there is only one such row in the table .
only { filter_eq { all_rows ; athlete ; tony geal } } = true
Output: select the rows whose athlete record fuzzily matches to tony geal . there is only one such row in the table .
eq { count { filter_eq { all_rows ; opponent ; copenhagen } } ; 2 } = true
Output: select the rows whose opponent record fuzzily matches to copenhagen . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; team ; senators } ; overall record } ; hop { filter_eq { all_rows ; team ; golden knights } ; overall record } } = true
Output: select the rows whose team record fuzzily matches to senators . take the overall record record of this row . select the rows whose team record fuzzily matches to golden knights . take the overall record record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; speed } ; country } ; italy } = true
Output: select the row whose speed record of all rows is maximum . the country record of this row is italy .
eq { diff { hop { filter_eq { all_rows ; opponent ; eva dyrberg } ; date } ; hop { filter_eq { all_rows ; opponent ; anna smashnova } ; date } } ; -2 days } = true
Output: select the rows whose opponent record fuzzily matches to eva dyrberg . take the date record of this row . select the rows whose opponent record fuzzily matches to anna smashnova . take the date record of this row . the second record is 2 days larger than the first record .
eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; home } ; calgary } = true
Output: select the row whose attendance record of all rows is 1st maximum . the home record of this row is calgary .
and { only { filter_eq { all_rows ; venue ; griffith stadium } } ; eq { hop { filter_eq { all_rows ; venue ; griffith stadium } ; date } ; october 19 , 1958 } } = true
Output: select the rows whose venue record fuzzily matches to griffith stadium . there is only one such row in the table . the date record of this unqiue row is october 19 , 1958 .
eq { hop { argmin { all_rows ; pick } ; nhl team } ; hartford whalers } = true
Output: select the row whose pick record of all rows is minimum . the nhl team record of this row is hartford whalers .
eq { hop { nth_argmax { all_rows ; worldwide gross ; 4 } ; title } ; the hobbit : an unexpected journey } = true
Output: select the row whose worldwide gross record of all rows is 4th maximum . the title record of this row is the hobbit : an unexpected journey .
eq { min { filter_eq { all_rows ; game site ; rfk stadium } ; date } ; september 13 , 1987 } = true
Output: select the rows whose game site record fuzzily matches to rfk stadium . the minimum date record of these rows is september 13 , 1987 .
eq { hop { nth_argmax { all_rows ; first elected ; 1 } ; incumbent } ; dale alford } = true
Output: select the row whose first elected record of all rows is 1st maximum . the incumbent record of this row is dale alford .
eq { count { filter_eq { all_rows ; surface ; grass } } ; 2 } = true
Output: select the rows whose surface record fuzzily matches to grass . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; points ; 67 } } ; 2 } = true
Output: select the rows whose points record is equal to 67 . the number of such rows is 2 .
round_eq { sum { all_rows ; laps } ; 1279 } = true
Output: the sum of the laps record of all rows is 1279 .
most_eq { all_rows ; mlb draft ; draft } = true
Output: for the mlb draft records of all rows , most of them fuzzily match to draft .
eq { hop { argmax { all_rows ; attendance } ; date } ; 16 march 2008 } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is 16 march 2008 .
all_greater_eq { filter_eq { all_rows ; street address ; gold avenue sw } ; height ft / m ; 180 } = true
Output: select the rows whose street address record fuzzily matches to gold avenue sw . for the height ft  m records of these rows , all of them are greater than or equal to 180 .
eq { count { filter_eq { all_rows ; 1st member ; edmund mordaunt } } ; 2 } = true
Output: select the rows whose 1st member record fuzzily matches to edmund mordaunt . the number of such rows is 2 .
and { only { filter_eq { all_rows ; event ; senior race } } ; eq { hop { filter_eq { all_rows ; event ; senior race } ; year } ; 2008 } } = true
Output: select the rows whose event record fuzzily matches to senior race . there is only one such row in the table . the year record of this unqiue row is 2008 .
eq { hop { argmax { all_rows ; ratings ( millions ) } ; episode } ; episode 1 } = true
Output: select the row whose ratings  millions  record of all rows is maximum . the episode record of this row is episode 1 .
eq { hop { nth_argmax { all_rows ; erp w ; 2 } ; call sign } ; wriq } = true
Output: select the row whose erp w record of all rows is 2nd maximum . the call sign record of this row is wriq .
most_greater { filter_eq { all_rows ; result ; win } ; orangemen points ; 20 } = true
Output: select the rows whose result record fuzzily matches to win . for the orangemen points records of these rows , most of them are greater than 20 .
eq { count { filter_eq { all_rows ; directed by ; jon cassar } } ; 5 } = true
Output: select the rows whose directed by record fuzzily matches to jon cassar . the number of such rows is 5 .
and { only { filter_eq { all_rows ; city ; kelowna } } ; eq { hop { filter_eq { all_rows ; city ; kelowna } ; skip } ; bob ursel } } = true
Output: select the rows whose city record fuzzily matches to kelowna . there is only one such row in the table . the skip record of this unqiue row is bob ursel .
round_eq { sum { filter_eq { all_rows ; opponent ; toronto maple leafs } ; attendance } ; 38,002 } = true
Output: select the rows whose opponent record fuzzily matches to toronto maple leafs . the sum of the attendance record of these rows is 38,002 .
greater { hop { filter_eq { all_rows ; nation ; romania } ; bronze } ; hop { filter_eq { all_rows ; nation ; soviet union } ; bronze } } = true
Output: select the rows whose nation record fuzzily matches to romania . take the bronze record of this row . select the rows whose nation record fuzzily matches to soviet union . take the bronze record of this row . the first record is greater than the second record .
most_eq { all_rows ; transfer window ; summer } = true
Output: for the transfer window records of all rows , most of them fuzzily match to summer .
round_eq { avg { all_rows ; lost } ; 10.75 } = true
Output: the average of the lost record of all rows is 10.75 .
round_eq { avg { all_rows ; days with frost } ; 15.67 } = true
Output: the average of the days with frost record of all rows is 15.67 .
eq { count { filter_eq { all_rows ; air date ; july 2008 } } ; 2 } = true
Output: select the rows whose air date record fuzzily matches to july 2008 . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; opponent ; new york giants } ; attendance } ; hop { filter_eq { all_rows ; opponent ; new york jets } ; attendance } } = true
Output: select the rows whose opponent record fuzzily matches to new york giants . take the attendance record of this row . select the rows whose opponent record fuzzily matches to new york jets . take the attendance record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; team ; donlavey } } ; eq { hop { filter_eq { all_rows ; team ; donlavey } ; year } ; 1990 } } = true
Output: select the rows whose team record fuzzily matches to donlavey . there is only one such row in the table . the year record of this unqiue row is 1990 .
eq { count { filter_eq { all_rows ; venue ; glasgow } } ; 6 } = true
Output: select the rows whose venue record fuzzily matches to glasgow . the number of such rows is 6 .
and { only { filter_eq { all_rows ; country ; norway } } ; eq { hop { filter_eq { all_rows ; country ; norway } ; name } ; hålogaland bridge } } = true
Output: 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 .
eq { hop { nth_argmin { filter_eq { all_rows ; home ; philadelphia } ; date ; 2 } ; attendance } ; 19412 } = true
Output: select the rows whose home record fuzzily matches to philadelphia . select the row whose date record of these rows is 2nd minimum . the attendance record of this row is 19412 .
eq { hop { argmin { all_rows ; entry date } ; single } ; meet me halfway } = true
Output: select the row whose entry date record of all rows is minimum . the single record of this row is meet me halfway .