niryuu/sni-each-converted huggingface dataset:
and { only { filter_eq { all_rows ; status ; alive after exiting the cube } } ; eq { hop { filter_eq { all_rows ; status ; alive after exiting the cube } ; name } ; kazan } } = true
Output: select the rows whose status record fuzzily matches to alive after exiting the cube . there is only one such row in the table . the name record of this unqiue row is kazan .
and { only { filter_eq { filter_eq { all_rows ; event ; championship test } ; horse ; donna dm } } ; eq { hop { filter_eq { filter_eq { all_rows ; event ; championship test } ; horse ; donna dm } ; athlete } ; sabine peters } } = true
Output: select the rows whose event record fuzzily matches to championship test . among these rows , select the rows whose horse record fuzzily matches to donna dm . there is only one such row in the table . the athlete record of this unqiue row is sabine peters .
eq { count { filter_greater { filter_eq { all_rows ; visitor ; carolina } ; attendance ; 19000 } } ; 3 } = true
Output: select the rows whose visitor record fuzzily matches to carolina . among these rows , select the rows whose attendance record is greater than 19000 . the number of such rows is 3 .
eq { count { filter_greater { all_rows ; attendance ; 20,000 } } ; 5 } = true
Output: select the rows whose attendance record is greater than 20,000 . the number of such rows is 5 .
eq { diff { hop { filter_eq { all_rows ; opponents ; anne hobbs andrew castle } ; year } ; hop { filter_eq { all_rows ; opponents ; gretchen magers kelly jones } ; year } } ; -1 year } = true
Output: select the rows whose opponents record fuzzily matches to anne hobbs andrew castle . take the year record of this row . select the rows whose opponents record fuzzily matches to gretchen magers kelly jones . take the year record of this row . the second record is 1 year larger than the first record .
eq { hop { argmax { all_rows ; rainfall by volume ( km 3 / year ) } ; region } ; huetar atlántico } = true
Output: select the row whose rainfall by volume  km 3  year  record of all rows is maximum . the region record of this row is huetar atlántico .
round_eq { avg { all_rows ; first quarter } ; 305221 } = true
Output: the average of the first quarter record of all rows is 305221 .
and { eq { nth_max { filter_not_eq { all_rows ; nationality ; ireland } ; goals ; 1 } ; 138 } ; eq { hop { nth_argmax { filter_not_eq { all_rows ; nationality ; ireland } ; goals ; 1 } ; name } ; johnny armstrong } } = true
Output: select the rows whose nationality record does not match to ireland . the 1st maximum goals record of these rows is 138 . the name record of the row with 1st maximum goals record is johnny armstrong .
eq { nth_min { filter_eq { all_rows ; nominated / won ; won } ; year ; 2 } ; 2008 } = true
Output: select the rows whose nominated  won record fuzzily matches to won . the 2nd minimum year record of these rows is 2008 .
eq { count { filter_eq { all_rows ; provider ( national government ) ; ministry of defense } } ; 2 } = true
Output: select the rows whose provider  national government  record fuzzily matches to ministry of defense . the number of such rows is 2 .
eq { hop { nth_argmin { all_rows ; date ; 5 } ; venue } ; guangzhou } = true
Output: select the row whose date record of all rows is 5th minimum . the venue record of this row is guangzhou .
eq { count { filter_eq { all_rows ; team ; tcu } } ; 5 } = true
Output: select the rows whose team record fuzzily matches to tcu . the number of such rows is 5 .
round_eq { avg { all_rows ; population ( 2011 ) } ; 4251 } = true
Output: the average of the population  2011  record of all rows is 4251 .
eq { hop { argmax { all_rows ; jump 3 } ; athlete name } ; gregory bell ( usa ) } = true
Output: select the row whose jump 3 record of all rows is maximum . the athlete name record of this row is gregory bell  usa  .
eq { count { filter_all { all_rows ; entrant } } ; 6 } = true
Output: select the rows whose entrant record is arbitrary . the number of such rows is 6 .
eq { hop { argmax { all_rows ; bush } ; county } ; fairfield } = true
Output: select the row whose bush record of all rows is maximum . the county record of this row is fairfield .
all_eq { all_rows ; type ; sovereign } = true
Output: for the type records of all rows , all of them fuzzily match to sovereign .
eq { count { filter_eq { all_rows ; opponent ; jimmy connors } } ; 3 } = true
Output: select the rows whose opponent record fuzzily matches to jimmy connors . the number of such rows is 3 .
round_eq { avg { all_rows ; clean electric grid california ( san francisco ) } ; 131.4 g / mile } = true
Output: the average of the clean electric grid california  san francisco  record of all rows is 131.4 g  mile .
most_eq { all_rows ; played ; 38 } = true
Output: for the played records of all rows , most of them are equal to 38 .
less { hop { filter_eq { all_rows ; winning driver ; elio de angelis } ; date } ; hop { filter_eq { all_rows ; winning driver ; niki lauda } ; date } } = true
Output: select the rows whose winning driver record fuzzily matches to elio de angelis . take the date record of this row . select the rows whose winning driver record fuzzily matches to niki lauda . take the date record of this row . the first record is less than the second record .
eq { nth_min { filter_eq { all_rows ; competition ; 2004 afc asian cup qualification } ; date ; 1 } ; september 26 , 2003 } = true
Output: select the rows whose competition record fuzzily matches to 2004 afc asian cup qualification . the 1st minimum date record of these rows is september 26 , 2003 .
eq { hop { nth_argmax { filter_eq { all_rows ; architect ; john mcarthur , jr } ; height ft ( m ) ; 1 } ; name } ; philadelphia city hall } = true
Output: select the rows whose architect record fuzzily matches to john mcarthur , jr . select the row whose height ft  m  record of these rows is 1st maximum . the name record of this row is philadelphia city hall .
eq { hop { nth_argmax { all_rows ; total ; 3 } ; club } ; maidstone united } = true
Output: select the row whose total record of all rows is 3rd maximum . the club record of this row is maidstone united .
and { eq { diff { hop { filter_eq { all_rows ; tournament ; hamburg } ; week } ; hop { filter_eq { all_rows ; tournament ; rome } ; week } } ; 7 days } ; and { eq { hop { filter_eq { all_rows ; tournament ; hamburg } ; week } ; may 14 } ; eq { hop { filter_eq { all_rows ; tournament ; rome } ; week } ; may 7 } } } = true
Output: select the rows whose tournament record fuzzily matches to hamburg . take the week record of this row . select the rows whose tournament record fuzzily matches to rome . take the week record of this row . the first record is 7 days larger than the second record . the week record of the first row is may 14 . the week record of the second row is may 7 .
eq { hop { nth_argmin { all_rows ; result ; 3 } ; year } ; 2007 } = true
Output: select the row whose result record of all rows is 3rd minimum . the year record of this row is 2007 .
eq { count { filter_less { filter_eq { all_rows ; date ; december } ; length of game ; 80:00 } } ; 2 } = true
Output: select the rows whose date record fuzzily matches to december . among these rows , select the rows whose length of game record is less than 80:00 . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; date ; 10 september } ; score } ; hop { filter_eq { all_rows ; date ; 20 august } ; score } } = true
Output: select the rows whose date record fuzzily matches to 10 september . take the score record of this row . select the rows whose date record fuzzily matches to 20 august . take the score record of this row . the first record is greater than the second record .
eq { hop { argmin { all_rows ; year erected } ; statue } ; general josé gervasio artigas } = true
Output: select the row whose year erected record of all rows is minimum . the statue record of this row is general josé gervasio artigas .
most_less { all_rows ; year ; 1960 } = true
Output: for the year records of all rows , most of them are less than 1960 .
eq { count { filter_eq { all_rows ; opponent ; jacksonville jaguars } } ; 2 } = true
Output: select the rows whose opponent record fuzzily matches to jacksonville jaguars . the number of such rows is 2 .
round_eq { sum { all_rows ; score } ; 30 } = true
Output: the sum of the score record of all rows is 30 .
eq { hop { nth_argmax { all_rows ; location attendance ; 2 } ; date } ; january 12 } = true
Output: select the row whose location attendance record of all rows is 2nd maximum . the date record of this row is january 12 .
only { filter_not_eq { all_rows ; date ; 14 july 1979 } } = true
Output: select the rows whose date record does not match to 14 july 1979 . there is only one such row in the table .
and { only { filter_eq { all_rows ; country ; fiji } } ; eq { hop { filter_eq { all_rows ; country ; fiji } ; player } ; vijay singh } } = true
Output: select the rows whose country record fuzzily matches to fiji . there is only one such row in the table . the player record of this unqiue row is vijay singh .
eq { hop { nth_argmax { all_rows ; erp w ; 2 } ; call sign } ; w300ac } = true
Output: select the row whose erp w record of all rows is 2nd maximum . the call sign record of this row is w300ac .
eq { hop { argmax { all_rows ; employees ( world ) } ; name } ; volkswagen ag } = true
Output: select the row whose employees  world  record of all rows is maximum . the name record of this row is volkswagen ag .
and { greater { hop { filter_eq { all_rows ; county ; coconino } ; hancock } ; hop { filter_eq { all_rows ; county ; graham } ; hancock } } ; and { eq { hop { filter_eq { all_rows ; county ; coconino } ; hancock } ; 1504 } ; eq { hop { filter_eq { all_rows ; county ; graham } ; hancock } ; 322 } } } = true
Output: select the rows whose county record fuzzily matches to coconino . take the hancock record of this row . select the rows whose county record fuzzily matches to graham . take the hancock record of this row . the first record is greater than the second record . the hancock record of the first row is 1504 . the hancock record of the second row is 322 .
round_eq { avg { all_rows ; height ( m ) } ; 2.01 } = true
Output: the average of the height  m  record of all rows is 2.01 .
eq { hop { argmax { all_rows ; points against } ; club } ; gorseinon rfc } = true
Output: select the row whose points against record of all rows is maximum . the club record of this row is gorseinon rfc .
most_eq { all_rows ; date ; friday } = true
Output: for the date records of all rows , most of them fuzzily match to friday .
eq { hop { nth_argmax { all_rows ; number of seasons in top division ; 3 } ; club } ; rijeka a , b } = true
Output: select the row whose number of seasons in top division record of all rows is 3rd maximum . the club record of this row is rijeka a , b .
eq { hop { nth_argmax { all_rows ; no ; 3 } ; player } ; terry dehere } = true
Output: select the row whose no record of all rows is 3rd maximum . the player record of this row is terry dehere .
and { only { filter_less { all_rows ; founded ; 1800 } } ; eq { hop { filter_less { all_rows ; founded ; 1800 } ; institution } ; university of louisville } } = true
Output: select the rows whose founded record is less than 1800 . there is only one such row in the table . the institution record of this unqiue row is university of louisville .
and { only { filter_not_eq { all_rows ; central bank ; bank } } ; eq { hop { filter_not_eq { all_rows ; central bank ; bank } ; currency } ; us dollar ( usd ) } } = true
Output: select the rows whose central bank record does not match to bank . there is only one such row in the table . the currency record of this unqiue row is us dollar  usd  .
eq { hop { argmax { all_rows ; attendance } ; date } ; november 10 , 2002 } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is november 10 , 2002 .
eq { hop { argmin { all_rows ; episodes } ; character } ; aiden burn csi detective } = true
Output: select the row whose episodes record of all rows is minimum . the character record of this row is aiden burn csi detective .
most_eq { all_rows ; platform ( s ) ; playstation 3 } = true
Output: for the platform  s  records of all rows , most of them fuzzily match to playstation 3 .
round_eq { sum { all_rows ; list votes } ; 66617 } = true
Output: the sum of the list votes record of all rows is 66617 .
round_eq { sum { filter_eq { all_rows ; year ; 2013 } ; result } ; over 50 } = true
Output: select the rows whose year record is equal to 2013 . the sum of the result record of these rows is over 50 .
and { only { filter_eq { all_rows ; directed by ; debbie allen } } ; eq { hop { filter_eq { all_rows ; directed by ; debbie allen } ; title } ; adjustment period } } = true
Output: select the rows whose directed by record fuzzily matches to debbie allen . there is only one such row in the table . the title record of this unqiue row is adjustment period .
and { eq { max { filter_eq { all_rows ; played ; 27 } ; position } ; 12th } ; eq { hop { argmax { filter_eq { all_rows ; played ; 27 } ; position } ; competition } ; super league xiv } } = true
Output: select the rows whose played record is equal to 27 . the maximum position record of these rows is 12th . the competition record of the row with superlative position record is super league xiv .
eq { hop { nth_argmax { all_rows ; no in series ; 1 } ; family / families } ; the williams family } = true
Output: select the row whose no in series record of all rows is 1st maximum . the family  families record of this row is the williams family .
all_greater { filter_eq { all_rows ; champion ; aktobe } ; season ; 2004 } = true
Output: select the rows whose champion record fuzzily matches to aktobe . for the season records of these rows , all of them are greater than 2004 .
eq { count { filter_eq { filter_eq { all_rows ; score ; 0 } ; date ; 19 february } } ; 4 } = true
Output: select the rows whose score record fuzzily matches to 0 . among these rows , select the rows whose date record fuzzily matches to 19 february . the number of such rows is 4 .
greater { hop { filter_eq { all_rows ; club ; real oviedo } ; wins } ; hop { filter_eq { all_rows ; club ; ca osasuna } ; wins } } = true
Output: select the rows whose club record fuzzily matches to real oviedo . take the wins record of this row . select the rows whose club record fuzzily matches to ca osasuna . take the wins record of this row . the first record is greater than the second record .
eq { hop { argmin { all_rows ; time } ; name } ; massimiliano rosolino } = true
Output: select the row whose time record of all rows is minimum . the name record of this row is massimiliano rosolino .
and { only { filter_eq { all_rows ; winning score ; - 7 } } ; eq { hop { filter_eq { all_rows ; winning score ; - 7 } ; tournament } ; us women 's open } } = true
Output: select the rows whose winning score record fuzzily matches to  7 . there is only one such row in the table . the tournament record of this unqiue row is us women s open .
less { hop { filter_eq { all_rows ; tournament ; christchurch , new zealand } ; year } ; hop { filter_eq { all_rows ; tournament ; gstaad , switzerland } ; year } } = true
Output: select the rows whose tournament record fuzzily matches to christchurch , new zealand . take the year record of this row . select the rows whose tournament record fuzzily matches to gstaad , switzerland . take the year record of this row . the first record is less than the second record .
eq { hop { argmax { filter_eq { all_rows ; tournament ; dakar } ; date } ; opponent in the final } ; rudy coco } = true
Output: select the rows whose tournament record fuzzily matches to dakar . select the row whose date record of these rows is maximum . the opponent in the final record of this row is rudy coco .
eq { hop { argmax { all_rows ; uk viewers ( million ) } ; title } ; the sorcerer 's shadow } = true
Output: select the row whose uk viewers  million  record of all rows is maximum . the title record of this row is the sorcerer s shadow .
most_eq { all_rows ; outcome ; runner - up } = true
Output: for the outcome records of all rows , most of them fuzzily match to runner  up .
most_eq { all_rows ; population gender ; male } = true
Output: for the population gender records of all rows , most of them fuzzily match to male .
eq { hop { argmax { all_rows ; earnings } ; player } ; lee trevino } = true
Output: select the row whose earnings record of all rows is maximum . the player record of this row is lee trevino .
round_eq { sum { all_rows ; points } ; 16 } = true
Output: the sum of the points record of all rows is 16 .
eq { hop { nth_argmin { all_rows ; first elected ; 3 } ; incumbent } ; william huston natcher } = true
Output: select the row whose first elected record of all rows is 3rd minimum . the incumbent record of this row is william huston natcher .
greater { hop { filter_eq { all_rows ; club ; ud salamanca } ; goal difference } ; hop { filter_eq { all_rows ; club ; cp mérida } ; goal difference } } = true
Output: select the rows whose club record fuzzily matches to ud salamanca . take the goal difference record of this row . select the rows whose club record fuzzily matches to cp mérida . take the goal difference record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; home team score } ; home team } ; richmond } = true
Output: select the row whose home team score record of all rows is maximum . the home team record of this row is richmond .
round_eq { avg { all_rows ; crowd } ; 18,333.33 } = true
Output: the average of the crowd record of all rows is 18,333.33 .
round_eq { avg { all_rows ; winner 's share } ; 363889 } = true
Output: the average of the winner s share record of all rows is 363889 .
most_less { all_rows ; year opened ; 2000 } = true
Output: for the year opened records of all rows , most of them are less than 2000 .
eq { count { filter_eq { all_rows ; nominee ; anthony hopkins } } ; 2 } = true
Output: select the rows whose nominee record fuzzily matches to anthony hopkins . the number of such rows is 2 .
most_eq { all_rows ; position ; lb } = true
Output: for the position records of all rows , most of them fuzzily match to lb .
and { only { filter_less { all_rows ; crowd ; 10000 } } ; and { eq { hop { filter_less { all_rows ; crowd ; 10000 } ; home team } ; geelong } ; and { eq { hop { filter_less { all_rows ; crowd ; 10000 } ; away team } ; north melbourne } ; eq { hop { filter_less { all_rows ; crowd ; 10000 } ; venue } ; kardinia park } } } } = true
Output: select the rows whose crowd record is less than 10000 . there is only one such row in the table . the home team record of this unqiue row is geelong . the away team record of this unqiue row is north melbourne . the venue record of this unqiue row is kardinia park .
and { only { filter_eq { all_rows ; college ; indiana university of pennsylvania } } ; eq { hop { filter_eq { all_rows ; college ; indiana university of pennsylvania } ; player } ; leander jordan } } = true
Output: select the rows whose college record fuzzily matches to indiana university of pennsylvania . there is only one such row in the table . the player record of this unqiue row is leander jordan .
eq { nth_min { all_rows ; year completed ; 1 } ; 1955 } = true
Output: the 1st minimum year completed record of all rows is 1955 .
eq { hop { argmax { all_rows ; crowd } ; venue } ; vfl park } = true
Output: select the row whose crowd record of all rows is maximum . the venue record of this row is vfl park .
less { hop { filter_eq { all_rows ; performer ; kaliopi } ; rank } ; hop { filter_eq { all_rows ; performer ; marjan necak } ; rank } } = true
Output: select the rows whose performer record fuzzily matches to kaliopi . take the rank record of this row . select the rows whose performer record fuzzily matches to marjan necak . take the rank record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; unemployment rate } ; county } ; schoharie } = true
Output: select the row whose unemployment rate record of all rows is maximum . the county record of this row is schoharie .
most_eq { all_rows ; gold ; 0 } = true
Output: for the gold records of all rows , most of them are equal to 0 .
most_greater { all_rows ; laps ; 223 } = true
Output: for the laps records of all rows , most of them are greater than 223 .
most_eq { all_rows ; year ; 198 } = true
Output: for the year records of all rows , most of them fuzzily match to 198 .
less { hop { filter_eq { all_rows ; train no ; 5037 } ; arrival } ; hop { filter_eq { all_rows ; train no ; 4723 } ; arrival } } = true
Output: select the rows whose train no record fuzzily matches to 5037 . take the arrival record of this row . select the rows whose train no record fuzzily matches to 4723 . take the arrival record of this row . the first record is less than the second record .
only { filter_eq { all_rows ; result ; won } } = true
Output: select the rows whose result record fuzzily matches to won . there is only one such row in the table .
eq { hop { nth_argmin { all_rows ; season ; 5 } ; team } ; runcorn highfield } = true
Output: select the row whose season record of all rows is 5th minimum . the team record of this row is runcorn highfield .
eq { hop { nth_argmax { all_rows ; vote percentage ; 2 } ; party } ; social democratic party ( spd ) } = true
Output: select the row whose vote percentage record of all rows is 2nd maximum . the party record of this row is social democratic party  spd  .
eq { count { filter_eq { all_rows ; commissioned ; 1930 } } ; 7 } = true
Output: select the rows whose commissioned record fuzzily matches to 1930 . the number of such rows is 7 .
and { greater { hop { filter_eq { all_rows ; incumbent ; john kline } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; jim ramstad } ; first elected } } ; and { eq { hop { filter_eq { all_rows ; incumbent ; john kline } ; first elected } ; 2002 } ; eq { hop { filter_eq { all_rows ; incumbent ; jim ramstad } ; first elected } ; 1990 } } } = true
Output: select the rows whose incumbent record fuzzily matches to john kline . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to jim ramstad . take the first elected record of this row . the first record is greater than the second record . the first elected record of the first row is 2002 . the first elected record of the second row is 1990 .
eq { count { filter_eq { all_rows ; industry ; oil & gas } } ; 2 } = true
Output: select the rows whose industry record fuzzily matches to oil  gas . the number of such rows is 2 .
most_eq { all_rows ; event ; monsters of rock } = true
Output: for the event records of all rows , most of them fuzzily match to monsters of rock .
eq { count { filter_eq { all_rows ; high assists ; rafer alston } } ; 4 } = true
Output: select the rows whose high assists record fuzzily matches to rafer alston . the number of such rows is 4 .
and { only { filter_eq { all_rows ; opponent ; 23 south carolina } } ; eq { hop { filter_eq { all_rows ; opponent ; 23 south carolina } ; date } ; may 23 } } = true
Output: select the rows whose opponent record fuzzily matches to 23 south carolina . there is only one such row in the table . the date record of this unqiue row is may 23 .
and { only { filter_eq { all_rows ; score ; 2 - 0 } } ; eq { hop { filter_eq { all_rows ; score ; 2 - 0 } ; home team } ; eastern team a } } = true
Output: select the rows whose score record fuzzily matches to 2  0 . there is only one such row in the table . the home team record of this unqiue row is eastern team a .
most_eq { all_rows ; seaman ; petty officer } = true
Output: for the seaman records of all rows , most of them fuzzily match to petty officer .
greater { hop { filter_eq { all_rows ; single ; wild eyes } ; other details } ; hop { filter_eq { all_rows ; single ; tell the world } ; other details } } = true
Output: select the rows whose single record fuzzily matches to wild eyes . take the other details record of this row . select the rows whose single record fuzzily matches to tell the world . take the other details record of this row . the first record is greater than the second record .
eq { diff { hop { filter_eq { all_rows ; race ; german grand prix } ; date } ; hop { filter_eq { all_rows ; race ; british grand prix } ; date } } ; 15 days } = true
Output: select the rows whose race record fuzzily matches to german grand prix . take the date record of this row . select the rows whose race record fuzzily matches to british grand prix . take the date record of this row . the first record is 15 days larger than the second record .
eq { count { filter_all { all_rows ; team } } ; 7 } = true
Output: select the rows whose team record is arbitrary . the number of such rows is 7 .
eq { count { filter_eq { all_rows ; opponent team ; canada } } ; 2 } = true
Output: select the rows whose opponent team record fuzzily matches to canada . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; venue ; austria } } ; 3 } = true
Output: select the rows whose venue record fuzzily matches to austria . the number of such rows is 3 .
greater { hop { filter_eq { all_rows ; home team ; fitzroy } ; crowd } ; hop { filter_eq { all_rows ; home team ; north melbourne } ; crowd } } = true
Output: select the rows whose home team record fuzzily matches to fitzroy . take the crowd record of this row . select the rows whose home team record fuzzily matches to north melbourne . take the crowd record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; wards / branches in arkansas ; 3 } ; stake } ; springdale arkansas stake } = true
Output: select the row whose wards  branches in arkansas record of all rows is 3rd maximum . the stake record of this row is springdale arkansas stake .
and { only { filter_eq { all_rows ; visual arts ; sharon hayes } } ; eq { hop { filter_eq { all_rows ; visual arts ; sharon hayes } ; year } ; 2013 } } = true
Output: select the rows whose visual arts record fuzzily matches to sharon hayes . there is only one such row in the table . the year record of this unqiue row is 2013 .
most_eq { filter_less { all_rows ; year ; 2006 } ; post ; screenwriter } = true
Output: select the rows whose year record is less than 2006 . for the post records of these rows , most of them fuzzily match to screenwriter .
eq { hop { nth_argmax { all_rows ; population ( 1 july 2005 est ) ; 2 } ; country with flag } ; dominican republic } = true
Output: select the row whose population  1 july 2005 est  record of all rows is 2nd maximum . the country with flag record of this row is dominican republic .
and { only { filter_eq { all_rows ; tournament ; austria } } ; eq { hop { filter_eq { all_rows ; tournament ; austria } ; date } ; 25 october 1999 } } = true
Output: select the rows whose tournament record fuzzily matches to austria . there is only one such row in the table . the date record of this unqiue row is 25 october 1999 .
round_eq { avg { all_rows ; points } ; 32.75 } = true
Output: the average of the points record of all rows is 32.75 .
eq { hop { argmin { filter_eq { all_rows ; constructor ; cooper - climax } ; date } ; race name } ; vii glover trophy } = true
Output: select the rows whose constructor record fuzzily matches to cooper  climax . select the row whose date record of these rows is minimum . the race name record of this row is vii glover trophy .
and { only { filter_eq { all_rows ; college ; iowa state } } ; eq { hop { filter_eq { all_rows ; college ; iowa state } ; name } ; clyde shugart } } = true
Output: select the rows whose college record fuzzily matches to iowa state . there is only one such row in the table . the name record of this unqiue row is clyde shugart .
round_eq { sum { all_rows ; score } ; 14 } = true
Output: the sum of the score record of all rows is 14 .
most_eq { all_rows ; wins ; 0 } = true
Output: for the wins records of all rows , most of them are equal to 0 .
eq { hop { nth_argmax { all_rows ; original air date ; 1 } ; title } ; i 'll take you } = true
Output: select the row whose original air date record of all rows is 1st maximum . the title record of this row is i ll take you .
eq { count { filter_eq { all_rows ; location ; usa } } ; 6 } = true
Output: select the rows whose location record fuzzily matches to usa . the number of such rows is 6 .
round_eq { avg { all_rows ; crowd } ; 16470 } = true
Output: the average of the crowd record of all rows is 16470 .
all_eq { filter_eq { all_rows ; date ; september } ; result ; l } = true
Output: select the rows whose date record fuzzily matches to september . for the result records of these rows , all of them fuzzily match to l .
and { only { filter_eq { all_rows ; pos ; dnf } } ; eq { hop { filter_eq { all_rows ; pos ; dnf } ; year } ; 2004 } } = true
Output: select the rows whose pos record fuzzily matches to dnf . there is only one such row in the table . the year record of this unqiue row is 2004 .
most_not_eq { all_rows ; score ; 0:0 } = true
Output: for the score records of all rows , most of them do not match to 0:0 .
eq { hop { nth_argmax { all_rows ; capacity ; 2 } ; stadium } ; sydney sports ground } = true
Output: select the row whose capacity record of all rows is 2nd maximum . the stadium record of this row is sydney sports ground .
eq { diff { hop { filter_eq { all_rows ; name ; 9 ad } ; power } ; hop { filter_eq { all_rows ; name ; 9 adb } ; power } } ; -200 } = true
Output: select the rows whose name record fuzzily matches to 9 ad . take the power record of this row . select the rows whose name record fuzzily matches to 9 adb . take the power record of this row . the second record is 200 larger than the first record .
eq { hop { argmax { all_rows ; height ( in ) } ; contestant } ; happie ntelamo } = true
Output: select the row whose height  in  record of all rows is maximum . the contestant record of this row is happie ntelamo .
less { hop { filter_eq { all_rows ; metropolitan area ; new york , new york } ; since } ; hop { filter_eq { all_rows ; metropolitan area ; chicago , illinois } ; since } } = true
Output: select the rows whose metropolitan area record fuzzily matches to new york , new york . take the since record of this row . select the rows whose metropolitan area record fuzzily matches to chicago , illinois . take the since record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; opponent ; st louis cardinals } } ; 2 } = true
Output: select the rows whose opponent record fuzzily matches to st louis cardinals . the number of such rows is 2 .
eq { hop { argmax { all_rows ; average } ; team } ; celtic } = true
Output: select the row whose average record of all rows is maximum . the team record of this row is celtic .
round_eq { avg { all_rows ; composition } ; 99 } = true
Output: the average of the composition record of all rows is 99 .
and { only { filter_eq { filter_eq { all_rows ; class ; lmp900 } ; co - drivers ; jan magnussen marco werner } } ; eq { hop { filter_eq { filter_eq { all_rows ; class ; lmp900 } ; co - drivers ; jan magnussen marco werner } ; year } ; 2003 } } = true
Output: select the rows whose class record fuzzily matches to lmp900 . among these rows , select the rows whose co  drivers record fuzzily matches to jan magnussen marco werner . there is only one such row in the table . the year record of this unqiue row is 2003 .
round_eq { avg { all_rows ; enrollment } ; 519 } = true
Output: the average of the enrollment record of all rows is 519 .
eq { count { filter_eq { filter_greater_eq { all_rows ; year left ; 1970 } ; conference joined ; hoosier heartland } } ; 2 } = true
Output: select the rows whose year left record is greater than or equal to 1970 . among these rows , select the rows whose conference joined record fuzzily matches to hoosier heartland . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; premiers ; eastern suburbs } ; points } ; hop { filter_eq { all_rows ; premiers ; canberra raiders } ; points } } = true
Output: select the rows whose premiers record fuzzily matches to eastern suburbs . take the points record of this row . select the rows whose premiers record fuzzily matches to canberra raiders . take the points record of this row . the first record is greater than the second record .
eq { hop { argmin { all_rows ; score } ; player } ; tom lehman } = true
Output: select the row whose score record of all rows is minimum . the player record of this row is tom lehman .
and { only { filter_eq { all_rows ; previous conference ; three rivers } } ; eq { hop { filter_eq { all_rows ; previous conference ; three rivers } ; school } ; caston } } = true
Output: select the rows whose previous conference record fuzzily matches to three rivers . there is only one such row in the table . the school record of this unqiue row is caston .
most_less { all_rows ; year ; 2000 } = true
Output: for the year records of all rows , most of them are less than 2000 .
round_eq { sum { all_rows ; production in 2011 ( 1000 ton ) } ; 290,133 } = true
Output: the sum of the production in 2011  1000 ton  record of all rows is 290,133 .
eq { hop { argmin { all_rows ; overall } ; name } ; art monk } = true
Output: select the row whose overall record of all rows is minimum . the name record of this row is art monk .
only { filter_less { filter_eq { all_rows ; country ; united states } ; score ; 208 } } = true
Output: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose score record is less than 208 . there is only one such row in the table .
round_eq { avg { all_rows ; attendance } ; 45557.14 } = true
Output: the average of the attendance record of all rows is 45557.14 .
eq { count { filter_eq { all_rows ; 2nd leg ; 1 - 0 } } ; 2 } = true
Output: select the rows whose 2nd leg record fuzzily matches to 1  0 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; age } ; name } ; donte gamble } = true
Output: select the row whose age record of all rows is maximum . the name record of this row is donte gamble .
round_eq { sum { filter_eq { all_rows ; opponent ; toronto maple leafs } ; score } ; 12 } = true
Output: select the rows whose opponent record fuzzily matches to toronto maple leafs . the sum of the score record of these rows is 12 .
eq { hop { argmax { all_rows ; year ( s ) won } ; player } ; tiger woods } = true
Output: select the row whose year  s  won record of all rows is maximum . the player record of this row is tiger woods .
most_eq { all_rows ; nationality ; spain } = true
Output: for the nationality records of all rows , most of them fuzzily match to spain .
eq { count { filter_eq { all_rows ; goal difference ; -2 } } ; 2 } = true
Output: select the rows whose goal difference record is equal to 2 . the number of such rows is 2 .
round_eq { sum { all_rows ; charlton goals } ; 178 } = true
Output: the sum of the charlton goals record of all rows is 178 .
greater { hop { filter_eq { all_rows ; team ; vélez sársfield } ; points } ; hop { filter_eq { all_rows ; team ; newell 's old boys } ; points } } = true
Output: select the rows whose team record fuzzily matches to vélez sársfield . take the points record of this row . select the rows whose team record fuzzily matches to newell s old boys . take the points record of this row . the first record is greater than the second record .
eq { nth_min { all_rows ; year ; 4 } ; 1985 } = true
Output: the 4th minimum year record of all rows is 1985 .
eq { count { filter_greater_eq { filter_greater_eq { all_rows ; max height ( ft ) ; 20000 } ; m / v ft / s ; 2200 } } ; 3 } = true
Output: select the rows whose max height  ft  record is greater than or equal to 20000 . among these rows , select the rows whose m  v ft  s record is greater than or equal to 2200 . the number of such rows is 3 .
and { only { filter_eq { all_rows ; appointed ; resigned } } ; eq { hop { filter_eq { all_rows ; appointed ; resigned } ; candidate } ; link byfield } } = true
Output: select the rows whose appointed record fuzzily matches to resigned . there is only one such row in the table . the candidate record of this unqiue row is link byfield .
eq { hop { nth_argmax { all_rows ; package version ; 2 } ; carrier } ; vodafone au } = true
Output: select the row whose package version record of all rows is 2nd maximum . the carrier record of this row is vodafone au .
eq { count { filter_eq { all_rows ; outcome ; runner-up } } ; 5 } = true
Output: select the rows whose outcome record fuzzily matches to runnerup . the number of such rows is 5 .
most_eq { all_rows ; reason for change ; resigned } = true
Output: for the reason for change records of all rows , most of them fuzzily match to resigned .
and { only { filter_eq { all_rows ; first elected ; 1793 } } ; eq { hop { filter_eq { all_rows ; first elected ; 1793 } ; incumbent } ; john nicholas } } = true
Output: select the rows whose first elected record is equal to 1793 . there is only one such row in the table . the incumbent record of this unqiue row is john nicholas .
eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; george h pendleton } = true
Output: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is george h pendleton .
most_less { filter_eq { all_rows ; location attendance ; pepsi center } ; location attendance ; 19000 } = true
Output: select the rows whose location attendance record fuzzily matches to pepsi center . for the location attendance records of these rows , most of them are less than 19000 .
and { eq { nth_min { all_rows ; track ; 5 } ; 5 } ; eq { hop { nth_argmin { all_rows ; track ; 5 } ; song title } ; crazy arms } } = true
Output: the 5th minimum track record of all rows is 5 . the song title record of the row with 5th minimum track record is crazy arms .
round_eq { avg { all_rows ; population ( 2010 census ) } ; 187514 } = true
Output: the average of the population  2010 census  record of all rows is 187514 .
eq { diff { hop { filter_eq { all_rows ; player ; mike weir } ; place } ; hop { filter_eq { all_rows ; player ; scott verplank } ; place } } ; 2 } = true
Output: select the rows whose player record fuzzily matches to mike weir . take the place record of this row . select the rows whose player record fuzzily matches to scott verplank . take the place record of this row . the first record is 2 larger than the second record .
eq { count { filter_greater { filter_greater { all_rows ; votes ; 100000 } ; seats ; 20 } } ; 3 } = true
Output: select the rows whose votes record is greater than 100000 . among these rows , select the rows whose seats record is greater than 20 . the number of such rows is 3 .
round_eq { avg { all_rows ; floors } ; 21.43 } = true
Output: the average of the floors record of all rows is 21.43 .
less { hop { filter_eq { all_rows ; call sign ; k276dm } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; k296ds } ; frequency mhz } } = true
Output: select the rows whose call sign record fuzzily matches to k276dm . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to k296ds . take the frequency mhz record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; rank ; 20 } } ; 2 } = true
Output: select the rows whose rank record is equal to 20 . the number of such rows is 2 .
most_eq { filter_less_eq { all_rows ; date ; october 24 , 2004 } ; tv time ; cbs 1:00 pm } = true
Output: select the rows whose date record is less than or equal to october 24 , 2004 . for the tv time records of these rows , most of them fuzzily match to cbs 1:00 pm .
and { eq { max { all_rows ; term start } ; 11 january 2005 } ; eq { hop { argmax { all_rows ; term start } ; minister } ; eli ben - menachem } } = true
Output: the maximum term start record of all rows is 11 january 2005 . the minister record of the row with superlative term start record is eli ben  menachem .
eq { count { filter_eq { all_rows ; owner ; royal thai army } } ; 2 } = true
Output: select the rows whose owner record fuzzily matches to royal thai army . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; team 2 ; mp algiers } ; 1st leg } ; hop { filter_eq { all_rows ; team 2 ; fortior mahajanga } ; 1st leg } } = true
Output: select the rows whose team 2 record fuzzily matches to mp algiers . take the 1st leg record of this row . select the rows whose team 2 record fuzzily matches to fortior mahajanga . take the 1st leg record of this row . the first record is greater than the second record .
all_greater { filter_greater { all_rows ; elevation ( m ) ; 3000 } ; prominence ( m ) ; 2400 } = true
Output: select the rows whose elevation  m  record is greater than 3000 . for the prominence  m  records of these rows , all of them are greater than 2400 .
round_eq { sum { filter_eq { all_rows ; spacecraft ; venera } ; time elapsed } ; 205 } = true
Output: select the rows whose spacecraft record fuzzily matches to venera . the sum of the time elapsed record of these rows is 205 .
and { only { filter_less { filter_eq { all_rows ; round ; euro 2012 qualifying } ; spectators ; 40000 } } ; and { eq { hop { filter_less { filter_eq { all_rows ; round ; euro 2012 qualifying } ; spectators ; 40000 } ; date } ; 11 october 2011 } ; eq { hop { filter_less { filter_eq { all_rows ; round ; euro 2012 qualifying } ; spectators ; 40000 } ; team 2 } ; azerbaijan } } } = true
Output: select the rows whose round record fuzzily matches to euro 2012 qualifying . among these rows , select the rows whose spectators record is less than 40000 . there is only one such row in the table . the date record of this unqiue row is 11 october 2011 . the team 2 record of this unqiue row is azerbaijan .
eq { hop { argmax { all_rows ; year } ; chassis } ; ferrari 500 / 625 } = true
Output: select the row whose year record of all rows is maximum . the chassis record of this row is ferrari 500  625 .
eq { hop { filter_eq { all_rows ; name ; justin knox } ; height } ; hop { filter_eq { all_rows ; name ; jamychal green } ; height } } = true
Output: select the rows whose name record fuzzily matches to justin knox . take the height record of this row . select the rows whose name record fuzzily matches to jamychal green . take the height record of this row . the first record fuzzily matches to the second record .
eq { hop { argmax { all_rows ; weight ( lbs ) } ; name } ; joseph barksdale } = true
Output: select the row whose weight  lbs  record of all rows is maximum . the name record of this row is joseph barksdale .
eq { hop { nth_argmax { all_rows ; launch year ; 2 } ; model } ; x51 mid - range } = true
Output: select the row whose launch year record of all rows is 2nd maximum . the model record of this row is x51 mid  range .
all_greater { all_rows ; elevation ( m ) ; 1000 } = true
Output: for the elevation  m  records of all rows , all of them are greater than 1000 .
eq { min { filter_eq { all_rows ; oberbayern ; fc ingolstadt 04 } ; season } ; 2005 } = true
Output: select the rows whose oberbayern record fuzzily matches to fc ingolstadt 04 . the minimum season record of these rows is 2005 .
greater { hop { filter_eq { all_rows ; name ; andreas kofler } ; points } ; hop { filter_eq { all_rows ; name ; tom hilde } ; points } } = true
Output: select the rows whose name record fuzzily matches to andreas kofler . take the points record of this row . select the rows whose name record fuzzily matches to tom hilde . take the points record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; crowd } ; 19167 } = true
Output: the average of the crowd record of all rows is 19167 .
eq { hop { argmax { all_rows ; points } ; opponent } ; ottawa senators } = true
Output: select the row whose points record of all rows is maximum . the opponent record of this row is ottawa senators .
eq { hop { argmax { all_rows ; hard discounters } ; country } ; spain } = true
Output: select the row whose hard discounters record of all rows is maximum . the country record of this row is spain .
and { eq { hop { nth_argmin { filter_eq { all_rows ; date ; october } ; week ; 3 } ; opponent } ; san francisco 49ers } ; eq { hop { nth_argmin { filter_eq { all_rows ; date ; october } ; week ; 3 } ; result } ; w 31 - 17 } } = true
Output: select the rows whose date record fuzzily matches to october . select the row whose week record of these rows is 3rd minimum . the opponent record of this row is san francisco 49ers . the result record of this row is w 31  17 .
most_eq { all_rows ; played ; 114 } = true
Output: for the played records of all rows , most of them are equal to 114 .
most_greater_eq { all_rows ; monounsaturated fat ; 20 g } = true
Output: for the monounsaturated fat records of all rows , most of them are greater than or equal to 20 g .
eq { hop { nth_argmin { all_rows ; air date ( france ) ; 2 } ; english title } ; firemen or firewomen } = true
Output: select the row whose air date  france  record of all rows is 2nd minimum . the english title record of this row is firemen or firewomen .
greater { hop { filter_eq { all_rows ; date ; november 22 } ; attendance } ; hop { filter_eq { all_rows ; date ; november 29 } ; attendance } } = true
Output: select the rows whose date record fuzzily matches to november 22 . take the attendance record of this row . select the rows whose date record fuzzily matches to november 29 . take the attendance record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; island ; hawaii } } ; 2 } = true
Output: select the rows whose island record fuzzily matches to hawaii . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; week ; 1 } ; attendance } ; hop { filter_eq { all_rows ; week ; 18 } ; attendance } } = true
Output: select the rows whose week record fuzzily matches to 1 . take the attendance record of this row . select the rows whose week record fuzzily matches to 18 . take the attendance record of this row . the first record is greater than the second record .
and { only { filter_eq { filter_eq { all_rows ; years ; 1 - 8 } ; authority ; integrated } } ; eq { hop { filter_eq { filter_eq { all_rows ; years ; 1 - 8 } ; authority ; integrated } ; name } ; st teresa 's school } } = true
Output: select the rows whose years record fuzzily matches to 1  8 . among these rows , select the rows whose authority record fuzzily matches to integrated . there is only one such row in the table . the name record of this unqiue row is st teresa s school .
round_eq { avg { all_rows ; pop ( 2010 ) } ; 44 } = true
Output: the average of the pop  2010  record of all rows is 44 .
and { only { filter_greater { filter_less { all_rows ; played ; 114 } ; average ; 1.0 } } ; eq { hop { filter_greater { filter_less { all_rows ; played ; 114 } ; average ; 1.0 } ; team } ; huracán } } = true
Output: select the rows whose played record is less than 114 . among these rows , select the rows whose average record is greater than 1.0 . there is only one such row in the table . the team record of this unqiue row is huracán .
eq { hop { argmax { all_rows ; pts } ; year } ; 1995 } = true
Output: select the row whose pts record of all rows is maximum . the year record of this row is 1995 .
eq { count { filter_eq { all_rows ; nation ; australia } } ; 4 } = true
Output: select the rows whose nation record fuzzily matches to australia . the number of such rows is 4 .
most_greater { all_rows ; points ; 10 } = true
Output: for the points records of all rows , most of them are greater than 10 .
eq { hop { nth_argmax { all_rows ; total ; 2 } ; nation } ; japan ( jpn ) } = true
Output: select the row whose total record of all rows is 2nd maximum . the nation record of this row is japan  jpn  .
greater { hop { filter_eq { all_rows ; opponent ; dallas cowboys } ; attendance } ; hop { filter_eq { all_rows ; opponent ; philadelphia eagles } ; attendance } } = true
Output: select the rows whose opponent record fuzzily matches to dallas cowboys . take the attendance record of this row . select the rows whose opponent record fuzzily matches to philadelphia eagles . take the attendance record of this row . the first record is greater than the second record .
most_eq { all_rows ; result ; loss } = true
Output: for the result records of all rows , most of them fuzzily match to loss .
round_eq { avg { all_rows ; crowd } ; 14464 } = true
Output: the average of the crowd record of all rows is 14464 .