niryuu/sni-each-converted huggingface dataset:
eq { count { filter_eq { all_rows ; floors ; 47 } } ; 2 } = true
Output: select the rows whose floors record is equal to 47 . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; television service ; telemarket } } ; 2 } = true
Output: select the rows whose television service record fuzzily matches to telemarket . the number of such rows is 2 .
and { eq { hop { argmax { all_rows ; attendance } ; year } ; 2008 } ; eq { hop { argmax { all_rows ; attendance } ; date } ; sunday , november 16 } } = true
Output: select the row whose attendance record of all rows is maximum . the year record of this row is 2008 . the date record of this row is sunday , november 16 .
eq { count { filter_eq { all_rows ; outcome ; runner - up } } ; 3 } = true
Output: select the rows whose outcome record fuzzily matches to runner  up . the number of such rows is 3 .
and { only { filter_eq { all_rows ; capacity ; 1598 cc } } ; eq { hop { filter_eq { all_rows ; capacity ; 1598 cc } ; power } ; daewoo } } = true
Output: select the rows whose capacity record fuzzily matches to 1598 cc . there is only one such row in the table . the power record of this unqiue row is daewoo .
and { only { filter_less { all_rows ; blank ends ; 10 } } ; eq { hop { filter_less { all_rows ; blank ends ; 10 } ; skip } ; johnny frederiksen } } = true
Output: select the rows whose blank ends record is less than 10 . there is only one such row in the table . the skip record of this unqiue row is johnny frederiksen .
and { only { filter_less { all_rows ; no of villages ; 5 } } ; eq { hop { filter_less { all_rows ; no of villages ; 5 } ; district } ; boe } } = true
Output: select the rows whose no of villages record is less than 5 . there is only one such row in the table . the district record of this unqiue row is boe .
eq { hop { nth_argmin { all_rows ; entered service ; 3 } ; locomotive } ; bl28 } = true
Output: select the row whose entered service record of all rows is 3rd minimum . the locomotive record of this row is bl28 .
eq { hop { argmax { all_rows ; total } ; nation } ; united states } = true
Output: select the row whose total record of all rows is maximum . the nation record of this row is united states .
eq { diff { hop { filter_eq { all_rows ; name ; phil karn } ; goals } ; hop { filter_eq { all_rows ; name ; michael apple } ; goals } } ; 2 } = true
Output: select the rows whose name record fuzzily matches to phil karn . take the goals record of this row . select the rows whose name record fuzzily matches to michael apple . take the goals record of this row . the first record is 2 larger than the second record .
round_eq { avg { all_rows ; attendance } ; 16119 } = true
Output: the average of the attendance record of all rows is 16119 .
most_less { all_rows ; height ; 190 } = true
Output: for the height records of all rows , most of them are less than 190 .
most_eq { all_rows ; type ; norteño } = true
Output: for the type records of all rows , most of them fuzzily match to norteño .
most_eq { all_rows ; high rebounds ; kevin love } = true
Output: for the high rebounds records of all rows , most of them fuzzily match to kevin love .
and { eq { min { all_rows ; height ( m ) } ; 610 } ; eq { hop { argmin { all_rows ; height ( m ) } ; peak } ; birks fell } } = true
Output: the minimum height  m  record of all rows is 610 . the peak record of the row with superlative height  m  record is birks fell .
only { filter_eq { all_rows ; rider ; suzuki } } = true
Output: select the rows whose rider record fuzzily matches to suzuki . there is only one such row in the table .
round_eq { avg { all_rows ; to par } ; -3.4 } = true
Output: the average of the to par record of all rows is 3.4 .
eq { hop { argmax { all_rows ; minutes played } ; year } ; 1999 } = true
Output: select the row whose minutes played record of all rows is maximum . the year record of this row is 1999 .
eq { count { filter_eq { all_rows ; gold ; 1 } } ; 2 } = true
Output: select the rows whose gold record is equal to 1 . the number of such rows is 2 .
round_eq { avg { all_rows ; crowd } ; 24662 } = true
Output: the average of the crowd record of all rows is 24662 .
and { only { filter_eq { all_rows ; chassis ; ensign } } ; eq { hop { filter_eq { all_rows ; chassis ; ensign } ; year } ; 1978 } } = true
Output: select the rows whose chassis record fuzzily matches to ensign . there is only one such row in the table . the year record of this unqiue row is 1978 .
eq { max { filter_eq { all_rows ; exited ; day 16 } ; finished } ; 3rd } = true
Output: select the rows whose exited record fuzzily matches to day 16 . the maximum finished record of these rows is 3rd .
eq { count { filter_greater { filter_eq { all_rows ; home ; tampa bay } ; attendance ; 19,000 } } ; 4 } = true
Output: select the rows whose home record fuzzily matches to tampa bay . among these rows , select the rows whose attendance record is greater than 19,000 . the number of such rows is 4 .
most_greater_eq { filter_eq { all_rows ; laps ; 64 } ; points ; 15 } = true
Output: select the rows whose laps record is equal to 64 . for the points records of these rows , most of them are greater than or equal to 15 .
eq { count { filter_greater { all_rows ; points ; 270 } } ; 2 } = true
Output: select the rows whose points record is greater than 270 . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; opposition ; surrey } ; score } ; hop { filter_eq { all_rows ; opposition ; somerset } ; score } } = true
Output: select the rows whose opposition record fuzzily matches to surrey . take the score record of this row . select the rows whose opposition record fuzzily matches to somerset . take the score record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; chassis ; connew pc1 } } ; 1 } = true
Output: select the rows whose chassis record fuzzily matches to connew pc1 . the number of such rows is 1 .
greater { hop { filter_eq { all_rows ; opposing teams ; france } ; against } ; hop { filter_eq { all_rows ; opposing teams ; wales } ; against } } = true
Output: select the rows whose opposing teams record fuzzily matches to france . take the against record of this row . select the rows whose opposing teams record fuzzily matches to wales . take the against record of this row . the first record is greater than the second record .
and { only { filter_less { all_rows ; time ; 22.2 } } ; eq { hop { filter_less { all_rows ; time ; 22.2 } ; athlete } ; veronica campbell - brown } } = true
Output: select the rows whose time record is less than 22.2 . there is only one such row in the table . the athlete record of this unqiue row is veronica campbell  brown .
and { only { filter_greater { all_rows ; games played ; 100 } } ; eq { hop { filter_greater { all_rows ; games played ; 100 } ; player } ; john curry } } = true
Output: select the rows whose games played record is greater than 100 . there is only one such row in the table . the player record of this unqiue row is john curry .
eq { nth_min { all_rows ; date ; 2 } ; december 21 , 1986 } = true
Output: the 2nd minimum date record of all rows is december 21 , 1986 .
eq { hop { argmax { all_rows ; socialist } ; date released } ; august 4 , 1991 } = true
Output: select the row whose socialist record of all rows is maximum . the date released record of this row is august 4 , 1991 .
most_eq { all_rows ; nominated work ; 71 : into the fire } = true
Output: for the nominated work records of all rows , most of them fuzzily match to 71 : into the fire .
greater { hop { filter_eq { all_rows ; week ; 17 } ; result } ; hop { filter_eq { all_rows ; week ; 16 } ; result } } = true
Output: select the rows whose week record fuzzily matches to 17 . take the result record of this row . select the rows whose week record fuzzily matches to 16 . take the result record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; caps ; 3 } ; player } ; kyle beckerman } = true
Output: select the row whose caps record of all rows is 3rd maximum . the player record of this row is kyle beckerman .
less { hop { filter_eq { all_rows ; player ; jason doig } ; round } ; hop { filter_eq { all_rows ; player ; robert deciantis } ; round } } = true
Output: select the rows whose player record fuzzily matches to jason doig . take the round record of this row . select the rows whose player record fuzzily matches to robert deciantis . take the round record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; weight ( kg ) } ; 55.6 } = true
Output: the average of the weight  kg  record of all rows is 55.6 .
and { only { filter_eq { all_rows ; result ; lost re - election republican gain } } ; eq { hop { filter_eq { all_rows ; result ; lost re - election republican gain } ; incumbent } ; dixie gilmer } } = true
Output: select the rows whose result record fuzzily matches to lost re  election republican gain . there is only one such row in the table . the incumbent record of this unqiue row is dixie gilmer .
less { hop { filter_eq { all_rows ; vehicle ; mitsubishi i - miev } ; clean electric grid california ( san francisco ) } ; hop { filter_eq { all_rows ; vehicle ; chevrolet volt } ; clean electric grid california ( san francisco ) } } = true
Output: select the rows whose vehicle record fuzzily matches to mitsubishi i  miev . take the clean electric grid california  san francisco  record of this row . select the rows whose vehicle record fuzzily matches to chevrolet volt . take the clean electric grid california  san francisco  record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; name ; arg } } ; 2 } = true
Output: select the rows whose name record fuzzily matches to arg . the number of such rows is 2 .
most_eq { all_rows ; entrant ; clarke - mordaunt - guthrie } = true
Output: for the entrant records of all rows , most of them fuzzily match to clarke  mordaunt  guthrie .
and { only { filter_eq { all_rows ; authority ; state integrated } } ; eq { hop { filter_eq { all_rows ; authority ; state integrated } ; name } ; st joseph 's catholic school } } = true
Output: select the rows whose authority record fuzzily matches to state integrated . there is only one such row in the table . the name record of this unqiue row is st joseph s catholic school .
eq { hop { argmax { all_rows ; year ( s ) retired } ; class } ; a } = true
Output: select the row whose year  s  retired record of all rows is maximum . the class record of this row is a .
greater { hop { filter_eq { all_rows ; home team ; fitzroy } ; home team score } ; hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score } } = true
Output: select the rows whose home team record fuzzily matches to fitzroy . take the home team score record of this row . select the rows whose home team record fuzzily matches to north melbourne . take the home team score record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; wins } ; club } ; ca osasuna } = true
Output: select the row whose wins record of all rows is maximum . the club record of this row is ca osasuna .
and { only { filter_eq { filter_greater_eq { all_rows ; top prize ; 10000 } ; host ; jason gunn } } ; eq { hop { filter_eq { filter_greater_eq { all_rows ; top prize ; 10000 } ; host ; jason gunn } ; channel } ; tvnz } } = true
Output: select the rows whose top prize record is greater than or equal to 10000 . among these rows , select the rows whose host record fuzzily matches to jason gunn . there is only one such row in the table . the channel record of this unqiue row is tvnz .
eq { count { filter_greater { all_rows ; length ; 4:00 } } ; 3 } = true
Output: select the rows whose length record is greater than 4:00 . the number of such rows is 3 .
and { eq { nth_min { all_rows ; round ; 8 } ; 7 } ; eq { hop { nth_argmin { all_rows ; round ; 8 } ; player } ; bob chandler } } = true
Output: the 8th minimum round record of all rows is 7 . the player record of the row with 8th minimum round record is bob chandler .
and { only { filter_eq { all_rows ; scoring rank ; n/a } } ; eq { hop { filter_eq { all_rows ; scoring rank ; n/a } ; year } ; 2005 } } = true
Output: select the rows whose scoring rank record fuzzily matches to na . there is only one such row in the table . the year record of this unqiue row is 2005 .
greater { hop { filter_eq { all_rows ; school ; martinsville } ; enrollment } ; hop { filter_eq { all_rows ; school ; mooresville } ; enrollment } } = true
Output: select the rows whose school record fuzzily matches to martinsville . take the enrollment record of this row . select the rows whose school record fuzzily matches to mooresville . take the enrollment record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; score } ; 140 } = true
Output: the average of the score record of all rows is 140 .
eq { count { filter_eq { all_rows ; current streak ; l } } ; 2 } = true
Output: select the rows whose current streak record fuzzily matches to l . the number of such rows is 2 .
greater { hop { filter_eq { all_rows ; call sign ; cjjc - fm } ; frequency } ; hop { filter_eq { all_rows ; call sign ; cfgw - fm } ; frequency } } = true
Output: select the rows whose call sign record fuzzily matches to cjjc  fm . take the frequency record of this row . select the rows whose call sign record fuzzily matches to cfgw  fm . take the frequency record of this row . the first record is greater than the second record .
most_eq { all_rows ; object type ; open cluster } = true
Output: for the object type records of all rows , most of them fuzzily match to open cluster .
eq { hop { argmax { all_rows ; points } ; artist } ; grethe & jørgen ingmann } = true
Output: select the row whose points record of all rows is maximum . the artist record of this row is grethe  jørgen ingmann .
eq { count { filter_less { filter_eq { all_rows ; visitor ; spurs } ; attendance ; 18000 } } ; 3 } = true
Output: select the rows whose visitor record fuzzily matches to spurs . among these rows , select the rows whose attendance record is less than 18000 . the number of such rows is 3 .
most_greater { all_rows ; crowd ; 14,000 } = true
Output: for the crowd records of all rows , most of them are greater than 14,000 .
and { only { filter_eq { all_rows ; country ; england } } ; eq { hop { filter_eq { all_rows ; country ; england } ; player } ; nick faldo } } = true
Output: select the rows whose country record fuzzily matches to england . there is only one such row in the table . the player record of this unqiue row is nick faldo .
eq { hop { argmin { all_rows ; first elected } ; incumbent } ; john e rankin } = true
Output: select the row whose first elected record of all rows is minimum . the incumbent record of this row is john e rankin .
eq { hop { argmax { all_rows ; original air date } ; title } ; episode 27 } = true
Output: select the row whose original air date record of all rows is maximum . the title record of this row is episode 27 .
less { hop { filter_eq { all_rows ; partnerships ; younis khan / shoaib malik } ; runs ( balls ) } ; hop { filter_eq { all_rows ; partnerships ; gautam gambhir / virender sehwag } ; runs ( balls ) } } = true
Output: select the rows whose partnerships record fuzzily matches to younis khan  shoaib malik . take the runs  balls  record of this row . select the rows whose partnerships record fuzzily matches to gautam gambhir  virender sehwag . take the runs  balls  record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; opponent in the final ; ágnes szávay } } ; 2 } = true
Output: select the rows whose opponent in the final record fuzzily matches to ágnes szávay . the number of such rows is 2 .
and { only { filter_eq { all_rows ; frequency ; am } } ; eq { hop { filter_eq { all_rows ; frequency ; am } ; branding } ; am 1150 } } = true
Output: select the rows whose frequency record fuzzily matches to am . there is only one such row in the table . the branding record of this unqiue row is am 1150 .
most_eq { filter_eq { all_rows ; category ; best series - drama } ; recipients and nominees ; best series-drama } = true
Output: select the rows whose category record fuzzily matches to best series  drama . for the recipients and nominees records of these rows , most of them fuzzily match to best seriesdrama .
and { only { filter_eq { all_rows ; country ; ghana } } ; eq { hop { filter_eq { all_rows ; country ; ghana } ; name } ; owusu - abeyie } } = true
Output: select the rows whose country record fuzzily matches to ghana . there is only one such row in the table . the name record of this unqiue row is owusu  abeyie .
and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; date } ; 3 may 1998 } } = true
Output: select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the date record of this unqiue row is 3 may 1998 .
eq { count { filter_less { filter_greater { all_rows ; round ; 3 } ; overall ; 200 } } ; 3 } = true
Output: select the rows whose round record is greater than 3 . among these rows , select the rows whose overall record is less than 200 . the number of such rows is 3 .
and { only { filter_eq { all_rows ; location ; japan } } ; eq { hop { filter_eq { all_rows ; location ; japan } ; water park } ; summerland } } = true
Output: select the rows whose location record fuzzily matches to japan . there is only one such row in the table . the water park record of this unqiue row is summerland .
eq { hop { nth_argmin { all_rows ; distance ( ly ) ; 1 } ; constellation } ; ursa major } = true
Output: select the row whose distance  ly  record of all rows is 1st minimum . the constellation record of this row is ursa major .
eq { hop { argmax { all_rows ; crowd } ; venue } ; mcg } = true
Output: select the row whose crowd record of all rows is maximum . the venue record of this row is mcg .
less { hop { filter_eq { all_rows ; 1953 ; nathan karp } ; total } ; hop { filter_eq { all_rows ; 1953 ; david l weiss } ; total } } = true
Output: select the rows whose 1953 record fuzzily matches to nathan karp . take the total record of this row . select the rows whose 1953 record fuzzily matches to david l weiss . take the total record of this row . the first record is less than the second record .
round_eq { sum { all_rows ; bb + hbp } ; 580 } = true
Output: the sum of the bb + hbp record of all rows is 580 .
eq { count { filter_eq { all_rows ; original canadian air date ; august } } ; 3 } = true
Output: select the rows whose original canadian air date record fuzzily matches to august . the number of such rows is 3 .
and { only { filter_greater { all_rows ; prominence ( m ) ; 3000 } } ; eq { hop { filter_greater { all_rows ; prominence ( m ) ; 3000 } ; peak } ; piton des neiges } } = true
Output: select the rows whose prominence  m  record is greater than 3000 . there is only one such row in the table . the peak record of this unqiue row is piton des neiges .
most_eq { all_rows ; final venue ; belle vue } = true
Output: for the final venue records of all rows , most of them fuzzily match to belle vue .
eq { hop { nth_argmin { all_rows ; took office ; 4 } ; delegate } ; brian k mchale } = true
Output: select the row whose took office record of all rows is 4th minimum . the delegate record of this row is brian k mchale .
round_eq { avg { all_rows ; lost } ; 4 } = true
Output: the average of the lost record of all rows is 4 .
eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; henry clay } = true
Output: select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is henry clay .
most_greater_eq { all_rows ; us viewers ( million ) ; 1.0 } = true
Output: for the us viewers  million  records of all rows , most of them are greater than or equal to 1.0 .
only { filter_eq { all_rows ; recorded ; 1980 } } = true
Output: select the rows whose recorded record fuzzily matches to 1980 . there is only one such row in the table .
and { greater { hop { filter_eq { all_rows ; team ; 2009 } ; losses } ; hop { filter_eq { all_rows ; team ; 2011 } ; losses } } ; and { eq { hop { filter_eq { all_rows ; team ; 2009 } ; losses } ; 8 } ; eq { hop { filter_eq { all_rows ; team ; 2011 } ; losses } ; 4 } } } = true
Output: select the rows whose team record fuzzily matches to 2009 . take the losses record of this row . select the rows whose team record fuzzily matches to 2011 . take the losses record of this row . the first record is greater than the second record . the losses record of the first row is 8 . the losses record of the second row is 4 .
and { greater { hop { filter_eq { all_rows ; player ; jeff urban } ; pick } ; hop { filter_eq { all_rows ; player ; chris george } ; pick } } ; and { eq { hop { filter_eq { all_rows ; player ; jeff urban } ; pick } ; 41 } ; eq { hop { filter_eq { all_rows ; player ; chris george } ; pick } ; 31 } } } = true
Output: select the rows whose player record fuzzily matches to jeff urban . take the pick record of this row . select the rows whose player record fuzzily matches to chris george . take the pick record of this row . the first record is greater than the second record . the pick record of the first row is 41 . the pick record of the second row is 31 .
less { hop { filter_eq { all_rows ; incumbent ; ivor d fenton } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; hardie scott } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to ivor d fenton . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to hardie scott . take the first elected record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; winning constructor ; bugatti } } ; 4 } = true
Output: select the rows whose winning constructor record fuzzily matches to bugatti . the number of such rows is 4 .
eq { hop { argmax { all_rows ; total goals } ; name } ; billy price } = true
Output: select the row whose total goals record of all rows is maximum . the name record of this row is billy price .
eq { hop { filter_eq { all_rows ; date ; october 14 } ; attendance } ; hop { filter_eq { all_rows ; date ; october 8 } ; attendance } } = true
Output: select the rows whose date record fuzzily matches to october 14 . take the attendance record of this row . select the rows whose date record fuzzily matches to october 8 . take the attendance record of this row . the first record is equal to the second record .
eq { hop { argmax { all_rows ; ratings ( kanto ) } ; episode } ; 3 } = true
Output: select the row whose ratings  kanto  record of all rows is maximum . the episode record of this row is 3 .
eq { hop { argmax { all_rows ; population 2011 census ( adjusted ) } ; administrative division } ; dhaka district } = true
Output: select the row whose population 2011 census  adjusted  record of all rows is maximum . the administrative division record of this row is dhaka district .
greater { hop { filter_eq { all_rows ; rr romaja ; gangwon } ; area } ; hop { filter_eq { all_rows ; rr romaja ; jeju } ; area } } = true
Output: select the rows whose rr romaja record fuzzily matches to gangwon . take the area record of this row . select the rows whose rr romaja record fuzzily matches to jeju . take the area record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; score } ; 70.46 } = true
Output: the average of the score record of all rows is 70.46 .
round_eq { avg { all_rows ; result } ; 2.2 } = true
Output: the average of the result record of all rows is 2.2 .
most_greater_eq { all_rows ; drawn ; 1 } = true
Output: for the drawn records of all rows , most of them are greater than or equal to 1 .
and { only { filter_eq { all_rows ; score ; 1-1 } } ; eq { hop { filter_eq { all_rows ; score ; 1-1 } ; april } ; 1 } } = true
Output: select the rows whose score record fuzzily matches to 11 . there is only one such row in the table . the april record of this unqiue row is 1 .
and { eq { nth_min { all_rows ; round ; 2 } ; 2 } ; eq { hop { nth_argmin { all_rows ; round ; 2 } ; player } ; joe hernandez } } = true
Output: the 2nd minimum round record of all rows is 2 . the player record of the row with 2nd minimum round record is joe hernandez .
most_eq { all_rows ; venue ; london } = true
Output: for the venue records of all rows , most of them fuzzily match to london .
eq { hop { argmin { all_rows ; car } ; driver } ; mike skinner } = true
Output: select the row whose car record of all rows is minimum . the driver record of this row is mike skinner .
eq { count { filter_all { all_rows ; nation } } ; 9 } = true
Output: select the rows whose nation record is arbitrary . the number of such rows is 9 .
all_eq { all_rows ; date ; 16 july 1949 } = true
Output: for the date records of all rows , all of them fuzzily match to 16 july 1949 .
and { only { filter_greater { all_rows ; money ; 100000 } } ; eq { hop { filter_greater { all_rows ; money ; 100000 } ; player } ; ben crenshaw } } = true
Output: select the rows whose money record is greater than 100000 . there is only one such row in the table . the player record of this unqiue row is ben crenshaw .
round_eq { sum { filter_eq { all_rows ; team ; honda } ; wins } ; 6 } = true
Output: select the rows whose team record fuzzily matches to honda . the sum of the wins record of these rows is 6 .
greater { hop { filter_eq { all_rows ; branding ; mom 's radio 88.3 cebu } ; power ( kw ) } ; hop { filter_eq { all_rows ; branding ; mom 's radio 101.5 tacloban } ; power ( kw ) } } = true
Output: select the rows whose branding record fuzzily matches to mom s radio 88.3 cebu . take the power  kw  record of this row . select the rows whose branding record fuzzily matches to mom s radio 101.5 tacloban . take the power  kw  record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; 4000ft + } ; country } ; scotland } = true
Output: select the row whose 4000ft + record of all rows is maximum . the country record of this row is scotland .
most_eq { all_rows ; chassis ; kurtis kraft 500b } = true
Output: for the chassis records of all rows , most of them fuzzily match to kurtis kraft 500b .
all_eq { all_rows ; directed by ; don colliver } = true
Output: for the directed by records of all rows , all of them fuzzily match to don colliver .
eq { count { filter_eq { all_rows ; venue ; brisbane cricket ground } } ; 2 } = true
Output: select the rows whose venue record fuzzily matches to brisbane cricket ground . the number of such rows is 2 .
eq { hop { argmin { all_rows ; date successor seated } ; successor } ; william g thompson ( r ) } = true
Output: select the row whose date successor seated record of all rows is minimum . the successor record of this row is william g thompson  r  .
and { only { filter_eq { filter_greater { all_rows ; home team score ; 4 } ; venue ; brunswick street oval } } ; eq { hop { filter_eq { filter_greater { all_rows ; home team score ; 4 } ; venue ; brunswick street oval } ; home team } ; fitzroy } } = true
Output: select the rows whose home team score record is greater than 4 . among these rows , select the rows whose venue record fuzzily matches to brunswick street oval . there is only one such row in the table . the home team record of this unqiue row is fitzroy .
and { eq { diff { hop { filter_eq { all_rows ; tournament ; bc open } ; margin of victory } ; hop { filter_eq { all_rows ; tournament ; glen campbell - los angeles open } ; margin of victory } } ; 3 } ; and { eq { hop { filter_eq { all_rows ; tournament ; bc open } ; margin of victory } ; 5 strokes } ; eq { hop { filter_eq { all_rows ; tournament ; glen campbell - los angeles open } ; margin of victory } ; 2 strokes } } } = true
Output: select the rows whose tournament record fuzzily matches to bc open . take the margin of victory record of this row . select the rows whose tournament record fuzzily matches to glen campbell  los angeles open . take the margin of victory record of this row . the first record is 3 larger than the second record . the margin of victory record of the first row is 5 strokes . the margin of victory record of the second row is 2 strokes .
eq { count { filter_greater { filter_eq { all_rows ; seasons ; 3 } ; points average ; 40 } } ; 4 } = true
Output: select the rows whose seasons record is equal to 3 . among these rows , select the rows whose points average record is greater than 40 . the number of such rows is 4 .
round_eq { avg { all_rows ; number of deputies } ; 114 } = true
Output: the average of the number of deputies record of all rows is 114 .
only { filter_eq { all_rows ; winner ; layne beachley ( aus ) } } = true
Output: select the rows whose winner record fuzzily matches to layne beachley  aus  . there is only one such row in the table .
most_greater_eq { all_rows ; crowd ; 5000 } = true
Output: for the crowd records of all rows , most of them are greater than or equal to 5000 .
less { hop { filter_eq { all_rows ; born ; 30 november 1874 } ; left house } ; hop { filter_eq { all_rows ; born ; 16 january 1905 } ; left house } } = true
Output: select the rows whose born record fuzzily matches to 30 november 1874 . take the left house record of this row . select the rows whose born record fuzzily matches to 16 january 1905 . take the left house record of this row . the first record is less than the second record .
only { filter_eq { all_rows ; country ; italy } } = true
Output: select the rows whose country record fuzzily matches to italy . there is only one such row in the table .
eq { count { filter_eq { all_rows ; year born ; 1986 } } ; 2 } = true
Output: select the rows whose year born record is equal to 1986 . the number of such rows is 2 .
and { less { hop { filter_eq { all_rows ; record ; 4 - 0 } ; time } ; hop { filter_eq { all_rows ; record ; 5 - 0 } ; time } } ; and { eq { hop { filter_eq { all_rows ; record ; 4 - 0 } ; time } ; 1:43 } ; eq { hop { filter_eq { all_rows ; record ; 5 - 0 } ; time } ; 5:00 } } } = true
Output: select the rows whose record record fuzzily matches to 4  0 . take the time record of this row . select the rows whose record record fuzzily matches to 5  0 . take the time record of this row . the first record is less than the second record . the time record of the first row is 1:43 . the time record of the second row is 5:00 .
eq { count { filter_eq { all_rows ; timeslot ( est ) ; wednesday } } ; 3 } = true
Output: select the rows whose timeslot  est  record fuzzily matches to wednesday . the number of such rows is 3 .
and { only { filter_eq { all_rows ; lost ; 17 } } ; eq { hop { filter_eq { all_rows ; lost ; 17 } ; club } ; milford haven rfc } } = true
Output: select the rows whose lost record is equal to 17 . there is only one such row in the table . the club record of this unqiue row is milford haven rfc .
eq { count { filter_eq { all_rows ; county ; kilkenny } } ; 2 } = true
Output: select the rows whose county record fuzzily matches to kilkenny . the number of such rows is 2 .
eq { diff { hop { filter_eq { all_rows ; race ; flight stakes } ; date } ; hop { filter_eq { all_rows ; race ; cox plate } ; date } } ; -21 } = true
Output: select the rows whose race record fuzzily matches to flight stakes . take the date record of this row . select the rows whose race record fuzzily matches to cox plate . take the date record of this row . the second record is 21 larger than the first record .
round_eq { avg { all_rows ; crowd } ; 25185 } = true
Output: the average of the crowd record of all rows is 25185 .
eq { count { filter_eq { all_rows ; gold ; 13 } } ; 2 } = true
Output: select the rows whose gold record is equal to 13 . the number of such rows is 2 .
eq { hop { nth_argmin { filter_eq { all_rows ; location ; st pete times forum } ; game ; 1 } ; attendance } ; 16104 } = true
Output: select the rows whose location record fuzzily matches to st pete times forum . select the row whose game record of these rows is 1st minimum . the attendance record of this row is 16104 .
and { only { filter_eq { all_rows ; authority ; state integrated } } ; eq { hop { filter_eq { all_rows ; authority ; state integrated } ; name } ; st joseph 's school } } = true
Output: select the rows whose authority record fuzzily matches to state integrated . there is only one such row in the table . the name record of this unqiue row is st joseph s school .
and { only { filter_not_eq { all_rows ; top - 10 ; 0 } } ; eq { hop { filter_not_eq { all_rows ; top - 10 ; 0 } ; tournament } ; the open championship } } = true
Output: select the rows whose top  10 record is not equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is the open championship .
eq { count { filter_eq { all_rows ; wins ; 0 } } ; 3 } = true
Output: select the rows whose wins record is equal to 0 . the number of such rows is 3 .
round_eq { sum { all_rows ; number in service } ; 310 } = true
Output: the sum of the number in service record of all rows is 310 .
round_eq { avg { filter_less_eq { all_rows ; position ; 5 } ; points } ; 22 } = true
Output: select the rows whose position record is less than or equal to 5 . the average of the points record of these rows is 22 .
eq { count { filter_eq { all_rows ; rank ; 4 } } ; 2 } = true
Output: select the rows whose rank record is equal to 4 . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; candidates ; 2 } ; incumbent } ; elijah cummings } = true
Output: select the row whose candidates record of all rows is 2nd maximum . the incumbent record of this row is elijah cummings .
and { only { filter_eq { all_rows ; winner ; none } } ; eq { hop { filter_eq { all_rows ; winner ; none } ; season } ; 3 } } = true
Output: select the rows whose winner record fuzzily matches to none . there is only one such row in the table . the season record of this unqiue row is 3 .
eq { count { filter_eq { all_rows ; location ; india } } ; 2 } = true
Output: select the rows whose location record fuzzily matches to india . the number of such rows is 2 .
round_eq { avg { all_rows ; crowd } ; 21486 } = true
Output: the average of the crowd record of all rows is 21486 .
round_eq { avg { all_rows ; earnings } ; 1434310 } = true
Output: the average of the earnings record of all rows is 1434310 .
greater { hop { filter_eq { all_rows ; institution ; smith college } ; enrollment } ; hop { filter_eq { all_rows ; institution ; united states coast guard academy } ; enrollment } } = true
Output: select the rows whose institution record fuzzily matches to smith college . take the enrollment record of this row . select the rows whose institution record fuzzily matches to united states coast guard academy . take the enrollment record of this row . the first record is greater than the second record .
greater { hop { filter_eq { all_rows ; name ; happy planet index } ; rank } ; hop { filter_eq { all_rows ; name ; environmental performance index } ; rank } } = true
Output: select the rows whose name record fuzzily matches to happy planet index . take the rank record of this row . select the rows whose name record fuzzily matches to environmental performance index . take the rank record of this row . the first record is greater than the second record .
and { eq { nth_min { all_rows ; rank ; 1 } ; 1 } ; eq { hop { nth_argmin { all_rows ; rank ; 1 } ; country } ; dominican republic } } = true
Output: the 1st minimum rank record of all rows is 1 . the country record of the row with 1st minimum rank record is dominican republic .
round_eq { avg { all_rows ; candidates } ; 60 } = true
Output: the average of the candidates record of all rows is 60 .
most_greater { all_rows ; year ; 1900 } = true
Output: for the year records of all rows , most of them are greater than 1900 .
and { only { filter_eq { all_rows ; polyunsaturated fat ; 3 % } } ; eq { hop { filter_eq { all_rows ; polyunsaturated fat ; 3 % } ; total fat } ; 94 g } } = true
Output: select the rows whose polyunsaturated fat record fuzzily matches to 3  . there is only one such row in the table . the total fat record of this unqiue row is 94 g .
eq { hop { argmax { all_rows ; gold } ; nation } ; austria } = true
Output: select the row whose gold record of all rows is maximum . the nation record of this row is austria .
and { only { filter_eq { all_rows ; venue ; kuwait } } ; eq { hop { filter_eq { all_rows ; venue ; kuwait } ; date } ; 26 jan 2005 } } = true
Output: select the rows whose venue record fuzzily matches to kuwait . there is only one such row in the table . the date record of this unqiue row is 26 jan 2005 .
eq { hop { argmax { all_rows ; races } ; season } ; 2010 } = true
Output: select the row whose races record of all rows is maximum . the season record of this row is 2010 .
eq { hop { argmax { all_rows ; points } ; team } ; sc internacional de são paulo } = true
Output: select the row whose points record of all rows is maximum . the team record of this row is sc internacional de são paulo .
eq { count { filter_eq { all_rows ; high assists ; rondo } } ; 6 } = true
Output: select the rows whose high assists record fuzzily matches to rondo . the number of such rows is 6 .
and { only { filter_eq { filter_eq { all_rows ; label ; rock records } ; release date ; 2000 } } ; eq { hop { filter_eq { filter_eq { all_rows ; label ; rock records } ; release date ; 2000 } ; english title } ; courage } } = true
Output: select the rows whose label record fuzzily matches to rock records . among these rows , select the rows whose release date record fuzzily matches to 2000 . there is only one such row in the table . the english title record of this unqiue row is courage .
and { only { filter_eq { all_rows ; cons ; 18 % } } ; eq { hop { filter_eq { all_rows ; cons ; 18 % } ; date ( s ) conducted } ; 27 october 2010 } } = true
Output: select the rows whose cons record fuzzily matches to 18  . there is only one such row in the table . the date  s  conducted record of this unqiue row is 27 october 2010 .
eq { hop { argmax { all_rows ; attendance } ; date } ; september 28 , 1986 } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is september 28 , 1986 .
and { only { filter_eq { all_rows ; venue ; brunswick street oval } } ; and { eq { hop { filter_eq { all_rows ; venue ; brunswick street oval } ; home team } ; fitzroy } ; eq { hop { filter_eq { all_rows ; venue ; brunswick street oval } ; away team } ; collingwood } } } = true
Output: select the rows whose venue record fuzzily matches to brunswick street oval . there is only one such row in the table . the home team record of this unqiue row is fitzroy . the away team record of this unqiue row is collingwood .
greater { hop { filter_eq { all_rows ; artist ; demos beke } ; points } ; hop { filter_eq { all_rows ; artist ; lucas christodolou } ; points } } = true
Output: select the rows whose artist record fuzzily matches to demos beke . take the points record of this row . select the rows whose artist record fuzzily matches to lucas christodolou . take the points record of this row . the first record is greater than the second record .
eq { count { filter_eq { filter_eq { all_rows ; location ; japan } ; res ; win } } ; 3 } = true
Output: select the rows whose location record fuzzily matches to japan . among these rows , select the rows whose res record fuzzily matches to win . the number of such rows is 3 .
eq { hop { argmax { all_rows ; production ( mt ) } ; commodity } ; wheat } = true
Output: select the row whose production  mt  record of all rows is maximum . the commodity record of this row is wheat .
eq { hop { argmax { all_rows ; weight } ; diameter } ; 28 mm } = true
Output: select the row whose weight record of all rows is maximum . the diameter record of this row is 28 mm .
and { only { filter_eq { all_rows ; date ; march 2006 } } ; eq { hop { filter_eq { all_rows ; date ; march 2006 } ; score } ; 1 - 0 } } = true
Output: select the rows whose date record fuzzily matches to march 2006 . there is only one such row in the table . the score record of this unqiue row is 1  0 .
round_eq { avg { filter_eq { all_rows ; position ; cornerback } ; overall } ; 41 } = true
Output: select the rows whose position record fuzzily matches to cornerback . the average of the overall record of these rows is 41 .
eq { hop { argmin { all_rows ; score } ; player } ; tim simpson } = true
Output: select the row whose score record of all rows is minimum . the player record of this row is tim simpson .
and { only { filter_eq { all_rows ; location ; england } } ; eq { hop { filter_eq { all_rows ; location ; england } ; name } ; proraphidia gomezi } } = true
Output: select the rows whose location record fuzzily matches to england . there is only one such row in the table . the name record of this unqiue row is proraphidia gomezi .
round_eq { avg { all_rows ; losses } ; 13 } = true
Output: the average of the losses record of all rows is 13 .
less { hop { filter_eq { all_rows ; overall pick ; 9 } ; overall pick } ; hop { filter_eq { all_rows ; overall pick ; 18 } ; overall pick } } = true
Output: select the rows whose overall pick record fuzzily matches to 9 . take the overall pick record of this row . select the rows whose overall pick record fuzzily matches to 18 . take the overall pick record of this row . the first record is less than the second record .
and { eq { max { all_rows ; score } ; 7 - 1 } ; eq { hop { argmax { all_rows ; score } ; home team } ; aston villa } } = true
Output: the maximum score record of all rows is 7  1 . the home team record of the row with superlative score record is aston villa .
and { only { filter_eq { all_rows ; lec sport ; men 's lacrosse } } ; eq { hop { filter_eq { all_rows ; lec sport ; men 's lacrosse } ; institution } ; salem state university } } = true
Output: select the rows whose lec sport record fuzzily matches to men s lacrosse . there is only one such row in the table . the institution record of this unqiue row is salem state university .
and { only { filter_eq { all_rows ; school / club team ; oral roberts } } ; eq { hop { filter_eq { all_rows ; school / club team ; oral roberts } ; player } ; haywoode workman } } = true
Output: select the rows whose school  club team record fuzzily matches to oral roberts . there is only one such row in the table . the player record of this unqiue row is haywoode workman .
eq { count { filter_eq { all_rows ; position ; forward / center } } ; 2 } = true
Output: select the rows whose position record fuzzily matches to forward  center . the number of such rows is 2 .
round_eq { avg { all_rows ; population ( 2010 census ) } ; 68,253 } = true
Output: the average of the population  2010 census  record of all rows is 68,253 .
and { only { filter_eq { all_rows ; venue ; south korea } } ; eq { hop { filter_eq { all_rows ; venue ; south korea } ; tournament } ; summer olympics } } = true
Output: select the rows whose venue record fuzzily matches to south korea . there is only one such row in the table . the tournament record of this unqiue row is summer olympics .
eq { hop { argmax { all_rows ; area ( km 2 ) } ; main town } ; adampan } = true
Output: select the row whose area  km 2  record of all rows is maximum . the main town record of this row is adampan .
eq { hop { argmax { filter_eq { all_rows ; game site ; shea stadium } ; attendance } ; date } ; 1983 - 10 - 16 } = true
Output: select the rows whose game site record fuzzily matches to shea stadium . select the row whose attendance record of these rows is maximum . the date record of this row is 1983  10  16 .
and { only { filter_eq { all_rows ; host ; jack arute } } ; eq { hop { filter_eq { all_rows ; host ; jack arute } ; year } ; 2008 } } = true
Output: select the rows whose host record fuzzily matches to jack arute . there is only one such row in the table . the year record of this unqiue row is 2008 .
and { only { filter_eq { all_rows ; result ; lost re - election } } ; eq { hop { filter_eq { all_rows ; result ; lost re - election } ; incumbent } ; ralph r eltse } } = true
Output: select the rows whose result record fuzzily matches to lost re  election . there is only one such row in the table . the incumbent record of this unqiue row is ralph r eltse .
round_eq { avg { all_rows ; result } ; 27 } = true
Output: the average of the result record of all rows is 27 .
eq { hop { argmax { all_rows ; average } ; player } ; alec bedser } = true
Output: select the row whose average record of all rows is maximum . the player record of this row is alec bedser .
round_eq { avg { filter_greater { all_rows ; total ; 13 } ; fighting spirit } ; 5 } = true
Output: select the rows whose total record is greater than 13 . the average of the fighting spirit record of these rows is 5 .
eq { count { filter_eq { filter_eq { all_rows ; score ; w } ; high points ; ray allen } } ; 3 } = true
Output: select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high points record fuzzily matches to ray allen . the number of such rows is 3 .
round_eq { sum { filter_eq { all_rows ; network ; the wb } ; viewers in millions } ; 7.62 } = true
Output: select the rows whose network record fuzzily matches to the wb . the sum of the viewers in millions record of these rows is 7.62 .
round_eq { avg { all_rows ; league cup } ; .2 } = true
Output: the average of the league cup record of all rows is .2 .
eq { hop { nth_argmax { all_rows ; pts ; 3 } ; season } ; 2010 - 11 } = true
Output: select the row whose pts record of all rows is 3rd maximum . the season record of this row is 2010  11 .
eq { hop { argmax { filter_eq { all_rows ; period active ; 199 } ; claimed sales } ; artist } ; the black eyed peas } = true
Output: select the rows whose period active record fuzzily matches to 199 . select the row whose claimed sales record of these rows is maximum . the artist record of this row is the black eyed peas .
most_eq { all_rows ; founded ; 18 } = true
Output: for the founded records of all rows , most of them fuzzily match to 18 .
and { eq { max { filter_eq { all_rows ; country ; south africa } ; total } ; 287 } ; and { eq { hop { argmax { filter_eq { all_rows ; country ; south africa } ; total } ; player } ; ernie els } ; eq { hop { argmax { filter_eq { all_rows ; country ; south africa } ; total } ; to par } ; + 3 } } } = true
Output: select the rows whose country record fuzzily matches to south africa . the maximum total record of these rows is 287 . the player record of the row with superlative total record is ernie els . the to par record of the row with superlative total record is + 3 .
and { only { filter_eq { all_rows ; turbines ; 1 } } ; eq { hop { filter_eq { all_rows ; turbines ; 1 } ; wind farm } ; glenough extension } } = true
Output: select the rows whose turbines record is equal to 1 . there is only one such row in the table . the wind farm record of this unqiue row is glenough extension .
eq { hop { nth_argmax { all_rows ; no ; 3 } ; player } ; ashraf amaya } = true
Output: select the row whose no record of all rows is 3rd maximum . the player record of this row is ashraf amaya .
eq { count { filter_eq { all_rows ; date ; 6 june 1981 } } ; 3 } = true
Output: select the rows whose date record fuzzily matches to 6 june 1981 . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; draws ; 15 } } ; 4 } = true
Output: select the rows whose draws record is equal to 15 . the number of such rows is 4 .
eq { hop { argmax { all_rows ; number of viewers } ; show } ; 1966 world cup final } = true
Output: select the row whose number of viewers record of all rows is maximum . the show record of this row is 1966 world cup final .
most_eq { all_rows ; result ; aus } = true
Output: for the result records of all rows , most of them fuzzily match to aus .
and { only { filter_greater { filter_greater { all_rows ; remittances 2011 ; 10 } ; remittances 2010 ; 50 } } ; eq { hop { filter_greater { filter_greater { all_rows ; remittances 2011 ; 10 } ; remittances 2010 ; 50 } ; country } ; india } } = true
Output: select the rows whose remittances 2011 record is greater than 10 . among these rows , select the rows whose remittances 2010 record is greater than 50 . there is only one such row in the table . the country record of this unqiue row is india .
eq { count { filter_eq { all_rows ; agg ; 2-1 } } ; 2 } = true
Output: select the rows whose agg record fuzzily matches to 21 . the number of such rows is 2 .