niryuu/sni-each-converted huggingface dataset:
eq { count { filter_eq { filter_eq { all_rows ; outcome ; winner } ; surface ; clay } } ; 4 } = true
Output: select the rows whose outcome record fuzzily matches to winner . among these rows , select the rows whose surface record fuzzily matches to clay . the number of such rows is 4 .
round_eq { sum { all_rows ; attendance } ; 791,469 } = true
Output: the sum of the attendance record of all rows is 791,469 .
and { only { filter_eq { all_rows ; college ; southwest texas state } } ; eq { hop { filter_eq { all_rows ; college ; southwest texas state } ; name } ; clenton ballard } } = true
Output: select the rows whose college record fuzzily matches to southwest texas state . there is only one such row in the table . the name record of this unqiue row is clenton ballard .
most_eq { all_rows ; nba draft ; has not yet declared for the nba draft } = true
Output: for the nba draft records of all rows , most of them fuzzily match to has not yet declared for the nba draft .
greater { hop { filter_eq { all_rows ; province ; santiago de cuba } ; density } ; hop { filter_eq { all_rows ; province ; pinar del río } ; density } } = true
Output: select the rows whose province record fuzzily matches to santiago de cuba . take the density record of this row . select the rows whose province record fuzzily matches to pinar del río . take the density record of this row . the first record is greater than the second record .
eq { hop { nth_argmin { all_rows ; home team score ; 2 } ; home team } ; essendon } = true
Output: select the row whose home team score record of all rows is 2nd minimum . the home team record of this row is essendon .
round_eq { sum { all_rows ; quantity } ; 10 } = true
Output: the sum of the quantity record of all rows is 10 .
greater { hop { filter_eq { all_rows ; player ; jarron collins } ; years for jazz } ; hop { filter_eq { all_rows ; player ; dell curry } ; years for jazz } } = true
Output: select the rows whose player record fuzzily matches to jarron collins . take the years for jazz record of this row . select the rows whose player record fuzzily matches to dell curry . take the years for jazz record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; date ; 2 } ; tournament } ; cincinnati , united states } = true
Output: select the row whose date record of all rows is 2nd maximum . the tournament record of this row is cincinnati , united states .
eq { hop { argmax { all_rows ; years for grizzlies } ; player } ; chris johnson } = true
Output: select the row whose years for grizzlies record of all rows is maximum . the player record of this row is chris johnson .
and { less { hop { filter_eq { all_rows ; couple ; jason & edyta } ; score } ; hop { filter_eq { all_rows ; couple ; kristi & mark } ; score } } ; and { eq { hop { filter_eq { all_rows ; couple ; jason & edyta } ; score } ; 23 ( 8 , 7 , 8 ) } ; eq { hop { filter_eq { all_rows ; couple ; kristi & mark } ; score } ; 27 ( 9 , 9 , 9 ) } } } = true
Output: select the rows whose couple record fuzzily matches to jason  edyta . take the score record of this row . select the rows whose couple record fuzzily matches to kristi  mark . take the score record of this row . the first record is less than the second record . the score record of the first row is 23  8 , 7 , 8  . the score record of the second row is 27  9 , 9 , 9  .
most_eq { filter_eq { all_rows ; score ; 70 } ; country ; united states } = true
Output: select the rows whose score record is equal to 70 . for the country records of these rows , most of them fuzzily match to united states .
and { only { filter_greater { all_rows ; ratings ; 2.00 million } } ; eq { hop { filter_greater { all_rows ; ratings ; 2.00 million } ; episode } ; 1 } } = true
Output: select the rows whose ratings record is greater than 2.00 million . there is only one such row in the table . the episode record of this unqiue row is 1 .
eq { count { filter_eq { all_rows ; performer 3 ; ryan stiles } } ; 5 } = true
Output: select the rows whose performer 3 record fuzzily matches to ryan stiles . the number of such rows is 5 .
eq { hop { argmax { all_rows ; black knights points } ; date } ; oct 13 } = true
Output: select the row whose black knights points record of all rows is maximum . the date record of this row is oct 13 .
eq { count { filter_eq { all_rows ; arena ; jobingcom arena } } ; 2 } = true
Output: select the rows whose arena record fuzzily matches to jobingcom arena . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; date } ; april 7 } = true
Output: select the row whose location attendance record of all rows is 1st maximum . the date record of this row is april 7 .
and { only { filter_eq { all_rows ; result ; won } } ; eq { hop { filter_eq { all_rows ; result ; won } ; category } ; choice summer tv star : male } } = true
Output: select the rows whose result record fuzzily matches to won . there is only one such row in the table . the category record of this unqiue row is choice summer tv star : male .
greater { hop { filter_eq { all_rows ; driver ; jeff simmons } ; laps } ; hop { filter_eq { all_rows ; driver ; sarah fisher } ; laps } } = true
Output: select the rows whose driver record fuzzily matches to jeff simmons . take the laps record of this row . select the rows whose driver record fuzzily matches to sarah fisher . take the laps record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; time } ; 45.16 } = true
Output: the average of the time record of all rows is 45.16 .
greater { hop { filter_eq { all_rows ; away team ; melbourne } ; away team score } ; hop { filter_eq { all_rows ; away team ; richmond } ; away team score } } = true
Output: select the rows whose away team record fuzzily matches to melbourne . take the away team score record of this row . select the rows whose away team record fuzzily matches to richmond . take the away team score record of this row . the first record is greater than the second record .
most_eq { all_rows ; type ; lake } = true
Output: for the type records of all rows , most of them fuzzily match to lake .
eq { count { filter_eq { all_rows ; ceased to be consort ; husband 's death } } ; 3 } = true
Output: select the rows whose ceased to be consort record fuzzily matches to husband s death . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; points ; 0 } } ; 3 } = true
Output: select the rows whose points record is equal to 0 . the number of such rows is 3 .
round_eq { avg { all_rows ; attendance } ; 46966 } = true
Output: the average of the attendance record of all rows is 46966 .
eq { hop { nth_argmax { all_rows ; money raised , 2q ; 3 } ; candidate } ; john edwards } = true
Output: select the row whose money raised , 2q record of all rows is 3rd maximum . the candidate record of this row is john edwards .
less { hop { filter_eq { all_rows ; competition ; world junior championships } ; position } ; hop { filter_eq { all_rows ; competition ; world championships } ; position } } = true
Output: select the rows whose competition record fuzzily matches to world junior championships . take the position record of this row . select the rows whose competition record fuzzily matches to world championships . take the position record of this row . the first record is less than the second record .
less { hop { filter_eq { all_rows ; world record ; north american record } ; 1:25:41 } ; hop { filter_eq { all_rows ; world record ; south american record } ; 1:25:41 } } = true
Output: select the rows whose world record record fuzzily matches to north american record . take the 1:25:41 record of this row . select the rows whose world record record fuzzily matches to south american record . take the 1:25:41 record of this row . the first record is less than the second record .
eq { hop { nth_argmax { all_rows ; bronze ; 1 } ; nation } ; sweden } = true
Output: select the row whose bronze record of all rows is 1st maximum . the nation record of this row is sweden .
eq { count { filter_eq { all_rows ; clinical status ; fda - approved } } ; 6 } = true
Output: select the rows whose clinical status record fuzzily matches to fda  approved . the number of such rows is 6 .
eq { hop { nth_argmax { all_rows ; ratings ; 1 } ; broadcast date } ; 14 august 2012 } = true
Output: select the row whose ratings record of all rows is 1st maximum . the broadcast date record of this row is 14 august 2012 .
and { greater { hop { filter_eq { all_rows ; venue ; western oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; victoria park } ; crowd } } ; and { eq { hop { filter_eq { all_rows ; venue ; western oval } ; crowd } ; 23000 } ; eq { hop { filter_eq { all_rows ; venue ; victoria park } ; crowd } ; 17500 } } } = true
Output: select the rows whose venue record fuzzily matches to western oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to victoria park . take the crowd record of this row . the first record is greater than the second record . the crowd record of the first row is 23000 . the crowd record of the second row is 17500 .
eq { count { filter_eq { filter_eq { all_rows ; first broadcast ; october } ; seans team ; joe wilkinson } } ; 2 } = true
Output: select the rows whose first broadcast record fuzzily matches to october . among these rows , select the rows whose seans team record fuzzily matches to joe wilkinson . the number of such rows is 2 .
most_greater_eq { all_rows ; lead ; 10 } = true
Output: for the lead records of all rows , most of them are greater than or equal to 10 .
eq { min { filter_eq { all_rows ; original air date ; august } ; us viewers ( million ) } ; 5.17 } = true
Output: select the rows whose original air date record fuzzily matches to august . the minimum us viewers  million  record of these rows is 5.17 .
most_eq { all_rows ; unit ; colorado } = true
Output: for the unit records of all rows , most of them fuzzily match to colorado .
eq { hop { nth_argmin { all_rows ; from ; 3 } ; player } ; jim krebs } = true
Output: select the row whose from record of all rows is 3rd minimum . the player record of this row is jim krebs .
eq { count { filter_eq { all_rows ; indigenous mining production 2006 ; 0 } } ; 4 } = true
Output: select the rows whose indigenous mining production 2006 record is equal to 0 . the number of such rows is 4 .
and { only { filter_eq { all_rows ; language ; norwegian } } ; eq { hop { filter_eq { all_rows ; language ; norwegian } ; song } ; sommer i palma } } = true
Output: select the rows whose language record fuzzily matches to norwegian . there is only one such row in the table . the song record of this unqiue row is sommer i palma .
eq { hop { argmax { all_rows ; total } ; player } ; jack nicklaus } = true
Output: select the row whose total record of all rows is maximum . the player record of this row is jack nicklaus .
eq { count { filter_eq { all_rows ; genre ; sitcom } } ; 2 } = true
Output: select the rows whose genre record fuzzily matches to sitcom . the number of such rows is 2 .
most_less { all_rows ; established ; 2005 } = true
Output: for the established records of all rows , most of them are less than 2005 .
eq { count { filter_less { all_rows ; total fat ; 100 g } } ; 3 } = true
Output: select the rows whose total fat record is less than 100 g . the number of such rows is 3 .
greater { hop { filter_eq { all_rows ; yard name ; delta shipbuilding co } ; total vessels built for usmc } ; hop { filter_eq { all_rows ; yard name ; pennsylvania shipyards } ; total vessels built for usmc } } = true
Output: select the rows whose yard name record fuzzily matches to delta shipbuilding co . take the total vessels built for usmc record of this row . select the rows whose yard name record fuzzily matches to pennsylvania shipyards . take the total vessels built for usmc record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; us viewers ( millions ) } ; original air date } ; november 7 , 2010 } = true
Output: select the row whose us viewers  millions  record of all rows is maximum . the original air date record of this row is november 7 , 2010 .
round_eq { sum { filter_less { all_rows ; total dismissals ; 10 } ; total dismissals } ; 8 } = true
Output: select the rows whose total dismissals record is less than 10 . the sum of the total dismissals record of these rows is 8 .
most_eq { all_rows ; 2008 status ; re - election } = true
Output: for the 2008 status records of all rows , most of them fuzzily match to re  election .
eq { hop { nth_argmax { all_rows ; frequency ; 1 } ; model number } ; athlon x2 5050e } = true
Output: select the row whose frequency record of all rows is 1st maximum . the model number record of this row is athlon x2 5050e .
less { hop { filter_eq { all_rows ; species ; borrelia garinii } ; genes } ; hop { filter_eq { all_rows ; species ; treponema denticola } ; genes } } = true
Output: select the rows whose species record fuzzily matches to borrelia garinii . take the genes record of this row . select the rows whose species record fuzzily matches to treponema denticola . take the genes record of this row . the first record is less than the second record .
eq { hop { nth_argmax { all_rows ; pick ; 2 } ; player } ; obed cetoute } = true
Output: select the row whose pick record of all rows is 2nd maximum . the player record of this row is obed cetoute .
eq { count { filter_greater_eq { all_rows ; laps ; 87 } } ; 8 } = true
Output: select the rows whose laps record is greater than or equal to 87 . the number of such rows is 8 .
eq { hop { nth_argmax { all_rows ; height ( m ) ; 2 } ; name } ; messeturm } = true
Output: select the row whose height  m  record of all rows is 2nd maximum . the name record of this row is messeturm .
greater { hop { filter_eq { all_rows ; street address ; 424 church street } ; floors } ; hop { filter_eq { all_rows ; street address ; 211 union street } ; floors } } = true
Output: select the rows whose street address record fuzzily matches to 424 church street . take the floors record of this row . select the rows whose street address record fuzzily matches to 211 union street . take the floors record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; events } ; 19.8 } = true
Output: the average of the events record of all rows is 19.8 .
and { only { filter_eq { all_rows ; game site ; olympic stadium } } ; eq { hop { filter_eq { all_rows ; game site ; olympic stadium } ; date } ; sunday , april 4 } } = true
Output: select the rows whose game site record fuzzily matches to olympic stadium . there is only one such row in the table . the date record of this unqiue row is sunday , april 4 .
round_eq { avg { all_rows ; first elected } ; 1953.5 } = true
Output: the average of the first elected record of all rows is 1953.5 .
eq { hop { argmin { all_rows ; rank } ; nation } ; soviet union } = true
Output: select the row whose rank record of all rows is minimum . the nation record of this row is soviet union .
round_eq { avg { all_rows ; bush % } ; 65-70 } = true
Output: the average of the bush  record of all rows is 6570 .
and { only { filter_eq { all_rows ; result ; 1st } } ; and { eq { hop { filter_eq { all_rows ; result ; 1st } ; tournament } ; european championships } ; eq { hop { filter_eq { all_rows ; result ; 1st } ; extra } ; 400 m } } } = true
Output: select the rows whose result record fuzzily matches to 1st . there is only one such row in the table . the tournament record of this unqiue row is european championships . the extra record of this unqiue row is 400 m .
eq { hop { argmax { filter_eq { all_rows ; venue ; away } ; attendance } ; opponent } ; luton town } = true
Output: select the rows whose venue record fuzzily matches to away . select the row whose attendance record of these rows is maximum . the opponent record of this row is luton town .
eq { hop { argmax { all_rows ; quantity } ; class ( old ) to 1868 } ; i b } = true
Output: select the row whose quantity record of all rows is maximum . the class  old  to 1868 record of this row is i b .
eq { hop { argmin { all_rows ; event } ; event } ; 1995 antholz } = true
Output: select the row whose event record of all rows is minimum . the event record of this row is 1995 antholz .
eq { diff { hop { filter_eq { all_rows ; voivodeship after 1569 ; samogitian eldership } ; year established } ; hop { filter_eq { all_rows ; voivodeship after 1569 ; trakai voivodeship } ; year established } } ; -2 years } = true
Output: select the rows whose voivodeship after 1569 record fuzzily matches to samogitian eldership . take the year established record of this row . select the rows whose voivodeship after 1569 record fuzzily matches to trakai voivodeship . take the year established record of this row . the second record is 2 years larger than the first record .
eq { hop { argmax { filter_eq { all_rows ; country ; australia } ; total } ; player } ; david graham } = true
Output: select the rows whose country record fuzzily matches to australia . select the row whose total record of these rows is maximum . the player record of this row is david graham .
and { only { filter_eq { filter_eq { all_rows ; previous conference ; northern indiana } ; mascot ; roughriders } } ; eq { hop { filter_eq { filter_eq { all_rows ; previous conference ; northern indiana } ; mascot ; roughriders } ; school } ; east chicago roosevelt } } = true
Output: select the rows whose previous conference record fuzzily matches to northern indiana . among these rows , select the rows whose mascot record fuzzily matches to roughriders . there is only one such row in the table . the school record of this unqiue row is east chicago roosevelt .
eq { count { filter_eq { all_rows ; format ; cd } } ; 7 } = true
Output: select the rows whose format record fuzzily matches to cd . the number of such rows is 7 .
eq { hop { argmax { all_rows ; caps } ; player } ; nick rimando } = true
Output: select the row whose caps record of all rows is maximum . the player record of this row is nick rimando .
greater { hop { filter_eq { all_rows ; year ; 2005 } ; points } ; hop { filter_eq { all_rows ; year ; 2007 } ; points } } = true
Output: select the rows whose year record fuzzily matches to 2005 . take the points record of this row . select the rows whose year record fuzzily matches to 2007 . take the points record of this row . the first record is greater than the second record .
eq { count { filter_greater { filter_eq { all_rows ; position ; running back } ; round ; 4 } } ; 3 } = true
Output: select the rows whose position record fuzzily matches to running back . among these rows , select the rows whose round record is greater than 4 . the number of such rows is 3 .
eq { count { filter_eq { filter_greater { all_rows ; school enrollment ( 200810 ) ; 70 } ; football ; y } } ; 4 } = true
Output: select the rows whose school enrollment  200810  record is greater than 70 . among these rows , select the rows whose football record fuzzily matches to y . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; runs ; 203 } } ; 2 } = true
Output: select the rows whose runs record is equal to 203 . the number of such rows is 2 .
and { only { filter_eq { filter_eq { all_rows ; l3 cache ; 8 mb } ; frequency ; 3 ghz } } ; eq { hop { filter_eq { filter_eq { all_rows ; l3 cache ; 8 mb } ; frequency ; 3 ghz } ; model number } ; core i7 - 3940xm } } = true
Output: select the rows whose l3 cache record fuzzily matches to 8 mb . among these rows , select the rows whose frequency record fuzzily matches to 3 ghz . there is only one such row in the table . the model number record of this unqiue row is core i7  3940xm .
most_greater_eq { all_rows ; wins ; 3 } = true
Output: for the wins records of all rows , most of them are greater than or equal to 3 .
eq { count { filter_eq { all_rows ; surface ; carpet } } ; 5 } = true
Output: select the rows whose surface record fuzzily matches to carpet . the number of such rows is 5 .
and { only { filter_eq { all_rows ; manner of departure ; released from duties } } ; eq { hop { filter_eq { all_rows ; manner of departure ; released from duties } ; outgoing manager } ; uwe wolf ( interim ) } } = true
Output: select the rows whose manner of departure record fuzzily matches to released from duties . there is only one such row in the table . the outgoing manager record of this unqiue row is uwe wolf  interim  .
and { only { filter_eq { all_rows ; episode ; 77 } } ; eq { hop { filter_eq { all_rows ; episode ; 77 } ; netflix } ; s03e25 } } = true
Output: select the rows whose episode record is equal to 77 . there is only one such row in the table . the netflix record of this unqiue row is s03e25 .
round_eq { avg { all_rows ; result } ; 25.5 } = true
Output: the average of the result record of all rows is 25.5 .
round_eq { avg { all_rows ; attendance } ; 18728 } = true
Output: the average of the attendance record of all rows is 18728 .
round_eq { sum { all_rows ; wins } ; 11 } = true
Output: the sum of the wins record of all rows is 11 .
and { only { filter_eq { all_rows ; date successor seated ; march } } ; eq { hop { filter_eq { all_rows ; date successor seated ; march } ; district } ; kentucky 2nd } } = true
Output: select the rows whose date successor seated record fuzzily matches to march . there is only one such row in the table . the district record of this unqiue row is kentucky 2nd .
eq { max { all_rows ; frequency } ; 1000 mhz } = true
Output: the maximum frequency record of all rows is 1000 mhz .
eq { hop { argmin { all_rows ; date } ; location attendance } ; verizon center 20173 } = true
Output: select the row whose date record of all rows is minimum . the location attendance record of this row is verizon center 20173 .
eq { count { filter_eq { all_rows ; opponent ; dallas cowboys } } ; 2 } = true
Output: select the rows whose opponent record fuzzily matches to dallas cowboys . the number of such rows is 2 .
eq { hop { argmax { all_rows ; height metres / ft } ; name } ; shard london bridge } = true
Output: select the row whose height metres  ft record of all rows is maximum . the name record of this row is shard london bridge .
and { only { filter_eq { all_rows ; venue ; london , england } } ; eq { hop { filter_eq { all_rows ; venue ; london , england } ; date } ; 6 february 2008 } } = true
Output: select the rows whose venue record fuzzily matches to london , england . there is only one such row in the table . the date record of this unqiue row is 6 february 2008 .
eq { count { filter_eq { all_rows ; builder ; yarrow , scotstoun } } ; 2 } = true
Output: select the rows whose builder record fuzzily matches to yarrow , scotstoun . the number of such rows is 2 .
round_eq { sum { all_rows ; games played } ; 52 } = true
Output: the sum of the games played record of all rows is 52 .
and { greater { hop { filter_eq { all_rows ; player ; tim jones } ; pick } ; hop { filter_eq { all_rows ; player ; lew kamanu } ; pick } } ; and { eq { hop { filter_eq { all_rows ; player ; tim jones } ; position } ; quarterback } ; eq { hop { filter_eq { all_rows ; player ; lew kamanu } ; position } ; defensive end } } } = true
Output: select the rows whose player record fuzzily matches to tim jones . take the pick record of this row . select the rows whose player record fuzzily matches to lew kamanu . take the pick record of this row . the first record is greater than the second record . the position record of the first row is quarterback . the position record of the second row is defensive end .
eq { count { filter_all { all_rows ; game } } ; 10 } = true
Output: select the rows whose game record is arbitrary . the number of such rows is 10 .
eq { hop { argmax { all_rows ; time } ; game } ; 1 } = true
Output: select the row whose time record of all rows is maximum . the game record of this row is 1 .
and { only { filter_eq { all_rows ; agg score ; 0 - 2 } } ; eq { hop { filter_eq { all_rows ; agg score ; 0 - 2 } ; team 1 } ; ilisiakos } } = true
Output: select the rows whose agg score record fuzzily matches to 0  2 . there is only one such row in the table . the team 1 record of this unqiue row is ilisiakos .
and { only { filter_eq { all_rows ; to par ; 11 } } ; eq { hop { filter_eq { all_rows ; to par ; 11 } ; player } ; tiger woods } } = true
Output: select the rows whose to par record is equal to 11 . there is only one such row in the table . the player record of this unqiue row is tiger woods .
eq { hop { nth_argmin { all_rows ; year ( ceremony ) ; 2 } ; director } ; humayun ahmed } = true
Output: select the row whose year  ceremony  record of all rows is 2nd minimum . the director record of this row is humayun ahmed .
eq { count { filter_eq { filter_eq { all_rows ; country ; norway } ; state ; finnmark } } ; 3 } = true
Output: select the rows whose country record fuzzily matches to norway . among these rows , select the rows whose state record fuzzily matches to finnmark . the number of such rows is 3 .
greater { hop { filter_eq { all_rows ; incumbent ; wilmer mizell } ; candidates } ; hop { filter_eq { all_rows ; incumbent ; earl b ruth } ; candidates } } = true
Output: select the rows whose incumbent record fuzzily matches to wilmer mizell . take the candidates record of this row . select the rows whose incumbent record fuzzily matches to earl b ruth . take the candidates record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; state ; new south wales } } ; 4 } = true
Output: select the rows whose state record fuzzily matches to new south wales . the number of such rows is 4 .
round_eq { avg { all_rows ; normal total } ; 88 } = true
Output: the average of the normal total record of all rows is 88 .
round_eq { sum { all_rows ; attendance } ; 203,967 } = true
Output: the sum of the attendance record of all rows is 203,967 .
eq { hop { argmax { all_rows ; wkts } ; player } ; brett lee } = true
Output: select the row whose wkts record of all rows is maximum . the player record of this row is brett lee .
round_eq { avg { filter_less { all_rows ; original air date ; january 1 , 2010 } ; us viewers ( million ) } ; 9.74 } = true
Output: select the rows whose original air date record is less than january 1 , 2010 . the average of the us viewers  million  record of these rows is 9.74 .
less { hop { filter_eq { all_rows ; name ; metro ag } ; revenue ( mil ) } ; hop { filter_eq { all_rows ; name ; daimler ag } ; revenue ( mil ) } } = true
Output: select the rows whose name record fuzzily matches to metro ag . take the revenue  mil  record of this row . select the rows whose name record fuzzily matches to daimler ag . take the revenue  mil  record of this row . the first record is less than the second record .
less { hop { filter_eq { all_rows ; opponent ; washington redskins } ; date } ; hop { filter_eq { all_rows ; opponent ; minnesota vikings } ; date } } = true
Output: select the rows whose opponent record fuzzily matches to washington redskins . take the date record of this row . select the rows whose opponent record fuzzily matches to minnesota vikings . take the date record of this row . the first record is less than the second record .
eq { max { all_rows ; qual } ; 225.496 } = true
Output: the maximum qual record of all rows is 225.496 .
most_eq { all_rows ; chassis ; talbot-lago t26c da } = true
Output: for the chassis records of all rows , most of them fuzzily match to talbotlago t26c da .
most_eq { all_rows ; taken into deptal stock ; 1963 } = true
Output: for the taken into deptal stock records of all rows , most of them are equal to 1963 .
and { only { filter_eq { all_rows ; written by ; jd walker } } ; eq { hop { filter_eq { all_rows ; written by ; jd walker } ; title } ; guess who 's coming to dinner } } = true
Output: select the rows whose written by record fuzzily matches to jd walker . there is only one such row in the table . the title record of this unqiue row is guess who s coming to dinner .
round_eq { avg { all_rows ; pick } ; 116.5 } = true
Output: the average of the pick record of all rows is 116.5 .
eq { count { filter_eq { filter_greater { all_rows ; won ; 10 } ; lost ; 3 } } ; 2 } = true
Output: select the rows whose won record is greater than 10 . among these rows , select the rows whose lost record is equal to 3 . the number of such rows is 2 .
only { filter_eq { all_rows ; directed by ; tj scott } } = true
Output: select the rows whose directed by record fuzzily matches to tj scott . there is only one such row in the table .
and { only { filter_eq { all_rows ; original air date ; november } } ; eq { hop { filter_eq { all_rows ; original air date ; november } ; title } ; class action satisfaction } } = true
Output: select the rows whose original air date record fuzzily matches to november . there is only one such row in the table . the title record of this unqiue row is class action satisfaction .
eq { hop { argmax { all_rows ; attendance } ; week } ; 9 } = true
Output: select the row whose attendance record of all rows is maximum . the week record of this row is 9 .
most_greater_eq { all_rows ; result ; 10 } = true
Output: for the result records of all rows , most of them are greater than or equal to 10 .
round_eq { avg { all_rows ; purse } ; 2046154 } = true
Output: the average of the purse record of all rows is 2046154 .
eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; sam rayburn } = true
Output: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is sam rayburn .
eq { hop { argmax { all_rows ; high points } ; date } ; june 20 } = true
Output: select the row whose high points record of all rows is maximum . the date record of this row is june 20 .
most_eq { all_rows ; acronym ; - } = true
Output: for the acronym records of all rows , most of them fuzzily match to  .
round_eq { avg { all_rows ; capacity } ; 9037 } = true
Output: the average of the capacity record of all rows is 9037 .
and { eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; home team } ; carlton } ; and { eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; away team } ; richmond } ; eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; date } ; 10 may 1930 } } } = true
Output: select the row whose crowd record of all rows is 1st maximum . the home team record of this row is carlton . the away team record of this row is richmond . the date record of this row is 10 may 1930 .
eq { count { filter_greater { all_rows ; score ; 300 } } ; 3 } = true
Output: select the rows whose score record is greater than 300 . the number of such rows is 3 .
eq { hop { argmax { all_rows ; winnings } ; driver } ; mark martin } = true
Output: select the row whose winnings record of all rows is maximum . the driver record of this row is mark martin .
and { only { filter_eq { all_rows ; opponents conference ; mac } } ; eq { hop { filter_eq { all_rows ; opponents conference ; mac } ; year } ; 2008 } } = true
Output: select the rows whose opponents conference record fuzzily matches to mac . there is only one such row in the table . the year record of this unqiue row is 2008 .
eq { diff { hop { filter_eq { all_rows ; venue ; western oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; windy hill } ; crowd } } ; 3755 } = true
Output: select the rows whose venue record fuzzily matches to western oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to windy hill . take the crowd record of this row . the first record is 3755 larger than the second record .
greater { hop { filter_eq { all_rows ; nation ; great britain ( gbr ) } ; silver } ; hop { filter_eq { all_rows ; nation ; hungary ( hun ) } ; silver } } = true
Output: select the rows whose nation record fuzzily matches to great britain  gbr  . take the silver record of this row . select the rows whose nation record fuzzily matches to hungary  hun  . take the silver record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; capacity ; 3 } ; stadium } ; stadion gradski vrt } = true
Output: select the row whose capacity record of all rows is 3rd maximum . the stadium record of this row is stadion gradski vrt .
and { only { filter_eq { all_rows ; format ; sports } } ; eq { hop { filter_eq { all_rows ; format ; sports } ; station } ; wing - am } } = true
Output: select the rows whose format record fuzzily matches to sports . there is only one such row in the table . the station record of this unqiue row is wing  am .
less { hop { filter_eq { all_rows ; successor ; john parker hale ( r ) } ; date of successors formal installation } ; hop { filter_eq { all_rows ; successor ; william bigler ( d ) } ; date of successors formal installation } } = true
Output: select the rows whose successor record fuzzily matches to john parker hale  r  . take the date of successors formal installation record of this row . select the rows whose successor record fuzzily matches to william bigler  d  . take the date of successors formal installation record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; home team score } ; home team } ; sydney } = true
Output: select the row whose home team score record of all rows is maximum . the home team record of this row is sydney .
and { eq { max { filter_eq { all_rows ; event ; wc rio de janeiro } ; rank points } ; 10 } ; eq { hop { argmax { filter_eq { all_rows ; event ; wc rio de janeiro } ; rank points } ; shooter } ; leuris pupo ( cub ) } } = true
Output: select the rows whose event record fuzzily matches to wc rio de janeiro . the maximum rank points record of these rows is 10 . the shooter record of the row with superlative rank points record is leuris pupo  cub  .
most_greater { all_rows ; number of aircraft ; 40 } = true
Output: for the number of aircraft records of all rows , most of them are greater than 40 .
eq { count { filter_eq { all_rows ; matches ; 10 } } ; 3 } = true
Output: select the rows whose matches record is equal to 10 . the number of such rows is 3 .
eq { hop { nth_argmin { filter_eq { all_rows ; team ; trail blazers } ; date ; 2 } ; score } ; w 99 - 95 ( ot ) } = true
Output: select the rows whose team record fuzzily matches to trail blazers . select the row whose date record of these rows is 2nd minimum . the score record of this row is w 99  95  ot  .
most_eq { filter_eq { all_rows ; circuit / location ; mantorp park } ; fastest lap ; robert dahlgren } = true
Output: select the rows whose circuit  location record fuzzily matches to mantorp park . for the fastest lap records of these rows , most of them fuzzily match to robert dahlgren .
eq { hop { argmax { all_rows ; us air date } ; title } ; the tale of many faces } = true
Output: select the row whose us air date record of all rows is maximum . the title record of this row is the tale of many faces .
eq { count { filter_eq { all_rows ; top - 25 ; 0 } } ; 2 } = true
Output: select the rows whose top  25 record is equal to 0 . the number of such rows is 2 .
round_eq { avg { all_rows ; number of clubs } ; 13 } = true
Output: the average of the number of clubs record of all rows is 13 .
and { only { filter_eq { all_rows ; surface ; hard } } ; eq { hop { filter_eq { all_rows ; surface ; hard } ; tournament } ; westende } } = true
Output: select the rows whose surface record fuzzily matches to hard . there is only one such row in the table . the tournament record of this unqiue row is westende .
eq { hop { nth_argmax { all_rows ; runs ; 2 } ; fielding team } ; india } = true
Output: select the row whose runs record of all rows is 2nd maximum . the fielding team record of this row is india .
greater { hop { filter_eq { all_rows ; date ; sunday , november 16 } ; attendance } ; hop { filter_eq { all_rows ; date ; monday , december 22 } ; attendance } } = true
Output: select the rows whose date record fuzzily matches to sunday , november 16 . take the attendance record of this row . select the rows whose date record fuzzily matches to monday , december 22 . take the attendance record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; year ; 1959 } ; rank } ; hop { filter_eq { all_rows ; year ; 1958 } ; rank } } = true
Output: select the rows whose year record fuzzily matches to 1959 . take the rank record of this row . select the rows whose year record fuzzily matches to 1958 . take the rank record of this row . the first record is less than the second record .
eq { hop { nth_argmin { all_rows ; date of appointment ; 1 } ; replaced by } ; peter voets ( caretaker ) } = true
Output: select the row whose date of appointment record of all rows is 1st minimum . the replaced by record of this row is peter voets  caretaker  .
and { only { filter_eq { all_rows ; rank ; chief justice } } ; eq { hop { filter_eq { all_rows ; rank ; chief justice } ; name } ; federico hernández denton } } = true
Output: select the rows whose rank record fuzzily matches to chief justice . there is only one such row in the table . the name record of this unqiue row is federico hernández denton .
eq { hop { argmax { all_rows ; points } ; club } ; hércules cf } = true
Output: select the row whose points record of all rows is maximum . the club record of this row is hércules cf .
greater { hop { filter_eq { all_rows ; satellite ; echo 2 } ; mass ( kg ) } ; hop { filter_eq { all_rows ; satellite ; mylar balloon } ; mass ( kg ) } } = true
Output: select the rows whose satellite record fuzzily matches to echo 2 . take the mass  kg  record of this row . select the rows whose satellite record fuzzily matches to mylar balloon . take the mass  kg  record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; against } ; opposing teams } ; wales } = true
Output: select the row whose against record of all rows is maximum . the opposing teams record of this row is wales .
round_eq { avg { all_rows ; position } ; 100 } = true
Output: the average of the position record of all rows is 100 .
eq { hop { nth_argmax { all_rows ; jersey number ( s ) ; 2 } ; player } ; patrick ewing } = true
Output: select the row whose jersey number  s  record of all rows is 2nd maximum . the player record of this row is patrick ewing .
eq { count { filter_eq { all_rows ; year ; 1987 } } ; 5 } = true
Output: select the rows whose year record is equal to 1987 . the number of such rows is 5 .
greater { hop { filter_eq { all_rows ; away team ; fitzroy } ; away team score } ; hop { filter_eq { all_rows ; away team ; north melbourne } ; away team score } } = true
Output: select the rows whose away team record fuzzily matches to fitzroy . take the away team score record of this row . select the rows whose away team record fuzzily matches to north melbourne . take the away team score record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; opponent ; st louis blues } } ; 2 } = true
Output: select the rows whose opponent record fuzzily matches to st louis blues . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; league ; high desert } } ; 6 } = true
Output: select the rows whose league record fuzzily matches to high desert . the number of such rows is 6 .
most_less { all_rows ; overall wc points ( rank ) ; 500 } = true
Output: for the overall wc points  rank  records of all rows , most of them are less than 500 .
eq { hop { argmax { all_rows ; vuelta } ; name } ; tony rominger ( sui ) } = true
Output: select the row whose vuelta record of all rows is maximum . the name record of this row is tony rominger  sui  .
less { hop { filter_eq { all_rows ; model ; nd4 } ; top speed ( in operation ) （ km / h ） } ; hop { filter_eq { all_rows ; model ; nd5 } ; top speed ( in operation ) （ km / h ） } } = true
Output: select the rows whose model record fuzzily matches to nd4 . take the top speed  in operation  （ km  h ） record of this row . select the rows whose model record fuzzily matches to nd5 . take the top speed  in operation  （ km  h ） record of this row . the first record is less than the second record .
eq { hop { nth_argmax { all_rows ; us r & b ; 2 } ; title } ; pretty mess } = true
Output: select the row whose us r  b record of all rows is 2nd maximum . the title record of this row is pretty mess .
eq { diff { hop { filter_eq { all_rows ; player ; craig kieswetter } ; runs } ; hop { filter_eq { all_rows ; player ; zander de bruyn } ; runs } } ; 7 } = true
Output: select the rows whose player record fuzzily matches to craig kieswetter . take the runs record of this row . select the rows whose player record fuzzily matches to zander de bruyn . take the runs record of this row . the first record is 7 larger than the second record .
eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; dallas cowboys } ; date ; 2 } ; attendance } ; 54714 } = true
Output: select the rows whose opponent record fuzzily matches to dallas cowboys . select the row whose date record of these rows is 2nd minimum . the attendance record of this row is 54714 .
eq { count { filter_all { all_rows ; athlete } } ; 5 } = true
Output: select the rows whose athlete record is arbitrary . the number of such rows is 5 .
greater { hop { filter_eq { all_rows ; season ; 2001 } ; number of clubs } ; hop { filter_eq { all_rows ; season ; 1996 } ; number of clubs } } = true
Output: select the rows whose season record fuzzily matches to 2001 . take the number of clubs record of this row . select the rows whose season record fuzzily matches to 1996 . take the number of clubs record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; won ; 2 } ; club } ; treorchy rfc } = true
Output: select the row whose won record of all rows is 2nd maximum . the club record of this row is treorchy rfc .
greater { hop { filter_eq { all_rows ; name ; cavendish ( ex - sibyl ) } ; launched } ; hop { filter_eq { all_rows ; name ; caesar ( ex - ranger ) } ; launched } } = true
Output: select the rows whose name record fuzzily matches to cavendish  ex  sibyl  . take the launched record of this row . select the rows whose name record fuzzily matches to caesar  ex  ranger  . take the launched record of this row . the first record is greater than the second record .
greater { hop { filter_eq { all_rows ; date ; january 31 , 2008 } ; score } ; hop { filter_eq { all_rows ; date ; january 26 , 2008 } ; score } } = true
Output: select the rows whose date record fuzzily matches to january 31 , 2008 . take the score record of this row . select the rows whose date record fuzzily matches to january 26 , 2008 . take the score record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; date ; 1 april 2006 } } ; 6 } = true
Output: select the rows whose date record fuzzily matches to 1 april 2006 . the number of such rows is 6 .
round_eq { avg { all_rows ; age } ; 29 } = true
Output: the average of the age record of all rows is 29 .
eq { hop { argmax { all_rows ; population } ; urban area } ; madrid } = true
Output: select the row whose population record of all rows is maximum . the urban area record of this row is madrid .
eq { count { filter_eq { all_rows ; position ; hb } } ; 4 } = true
Output: select the rows whose position record fuzzily matches to hb . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; victor ; stalemate } } ; 2 } = true
Output: select the rows whose victor record fuzzily matches to stalemate . the number of such rows is 2 .
and { only { filter_eq { all_rows ; directed by ; guy bee } } ; eq { hop { filter_eq { all_rows ; directed by ; guy bee } ; title } ; family matters } } = true
Output: select the rows whose directed by record fuzzily matches to guy bee . there is only one such row in the table . the title record of this unqiue row is family matters .
eq { hop { argmin { all_rows ; roll } ; name } ; timatanga community school } = true
Output: select the row whose roll record of all rows is minimum . the name record of this row is timatanga community school .
eq { count { filter_eq { all_rows ; points ; n / a } } ; 2 } = true
Output: select the rows whose points record fuzzily matches to n  a . the number of such rows is 2 .
less { hop { filter_eq { all_rows ; men 's singles ; nicholas hall } ; year } ; hop { filter_eq { all_rows ; men 's singles ; andrew smith } ; year } } = true
Output: select the rows whose men s singles record fuzzily matches to nicholas hall . take the year record of this row . select the rows whose men s singles record fuzzily matches to andrew smith . take the year record of this row . the first record is less than the second record .
eq { hop { nth_argmax { all_rows ; crime ; 2 } ; character } ; shane casey } = true
Output: select the row whose crime record of all rows is 2nd maximum . the character record of this row is shane casey .
round_eq { sum { all_rows ; points } ; 56 } = true
Output: the sum of the points record of all rows is 56 .
eq { hop { argmax { all_rows ; area ( km 2 ) } ; county } ; tana river } = true
Output: select the row whose area  km 2  record of all rows is maximum . the county record of this row is tana river .
eq { min { filter_eq { all_rows ; surface ; clay } ; date } ; january 8 , 2001 } = true
Output: select the rows whose surface record fuzzily matches to clay . the minimum date record of these rows is january 8 , 2001 .
and { only { filter_less { all_rows ; races ; 10 } } ; eq { hop { filter_less { all_rows ; races ; 10 } ; drivers } ; ben hanley } } = true
Output: select the rows whose races record is less than 10 . there is only one such row in the table . the drivers record of this unqiue row is ben hanley .
round_eq { avg { all_rows ; oricon peak } ; 78.2 } = true
Output: the average of the oricon peak record of all rows is 78.2 .
round_eq { avg { all_rows ; pick } ; 16.6 } = true
Output: the average of the pick record of all rows is 16.6 .
eq { count { filter_eq { all_rows ; studio host ; alan massengale } } ; 3 } = true
Output: select the rows whose studio host record fuzzily matches to alan massengale . the number of such rows is 3 .
round_eq { avg { all_rows ; leading scorer } ; 28.5 } = true
Output: the average of the leading scorer record of all rows is 28.5 .
eq { max { all_rows ; score } ; 5 - 1 } = true
Output: the maximum score record of all rows is 5  1 .
eq { diff { hop { filter_eq { all_rows ; club ; mold rfc } ; losing bonus } ; hop { filter_eq { all_rows ; club ; ruthin rfc } ; losing bonus } } ; -1 } = true
Output: select the rows whose club record fuzzily matches to mold rfc . take the losing bonus record of this row . select the rows whose club record fuzzily matches to ruthin rfc . take the losing bonus record of this row . the second record is 1 larger than the first record .
greater { hop { filter_eq { all_rows ; gold ; norbert sturny ( sui ) } ; year } ; hop { filter_eq { all_rows ; gold ; malcolm cooper ( gbr ) } ; year } } = true
Output: select the rows whose gold record fuzzily matches to norbert sturny  sui  . take the year record of this row . select the rows whose gold record fuzzily matches to malcolm cooper  gbr  . take the year record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; year left } ; 1977 } = true
Output: the average of the year left record of all rows is 1977 .
most_eq { all_rows ; high assists ; steve nash } = true
Output: for the high assists records of all rows , most of them fuzzily match to steve nash .
less { hop { filter_eq { all_rows ; call sign ; ktaw } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; ktml } ; frequency mhz } } = true
Output: select the rows whose call sign record fuzzily matches to ktaw . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to ktml . take the frequency mhz record of this row . the first record is less than the second record .
eq { count { filter_greater { all_rows ; census ranking ; 1000 } } ; 3 } = true
Output: select the rows whose census ranking record is greater than 1000 . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; nationality ; united states } } ; 2 } = true
Output: select the rows whose nationality record fuzzily matches to united states . the number of such rows is 2 .
eq { hop { argmax { all_rows ; championships } ; established } ; 1979 } = true
Output: select the row whose championships record of all rows is maximum . the established record of this row is 1979 .