niryuu/sni-each-converted huggingface dataset:
eq { count { filter_greater { filter_greater { all_rows ; away team score ; 10 } ; crowd ; 12000 } } ; 2 } = true
Output: select the rows whose away team score record is greater than 10 . among these rows , select the rows whose crowd record is greater than 12000 . the number of such rows is 2 .
and { only { filter_eq { all_rows ; certification ; 2x platinum } } ; eq { hop { filter_eq { all_rows ; certification ; 2x platinum } ; album } ; my love : essential collection } } = true
Output: select the rows whose certification record fuzzily matches to 2x platinum . there is only one such row in the table . the album record of this unqiue row is my love : essential collection .
eq { count { filter_eq { filter_eq { all_rows ; date ; october } ; result ; w } } ; 3 } = true
Output: select the rows whose date record fuzzily matches to october . among these rows , select the rows whose result record fuzzily matches to w . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; distance ; 1000 m } } ; 4 } = true
Output: select the rows whose distance record fuzzily matches to 1000 m . the number of such rows is 4 .
and { only { filter_eq { all_rows ; storyteller ; gary } } ; eq { hop { filter_eq { all_rows ; storyteller ; gary } ; - } ; 11 } } = true
Output: select the rows whose storyteller record fuzzily matches to gary . there is only one such row in the table . the  record of this unqiue row is 11 .
and { not_eq { hop { filter_eq { all_rows ; ngc number ; 2736 } ; object type } ; hop { filter_eq { all_rows ; ngc number ; 2770 } ; object type } } ; and { eq { hop { filter_eq { all_rows ; ngc number ; 2736 } ; object type } ; diffuse nebula } ; eq { hop { filter_eq { all_rows ; ngc number ; 2770 } ; object type } ; spiral galaxy } } } = true
Output: select the rows whose ngc number record fuzzily matches to 2736 . take the object type record of this row . select the rows whose ngc number record fuzzily matches to 2770 . take the object type record of this row . the first record does not match to the second record . the object type record of the first row is diffuse nebula . the object type record of the second row is spiral galaxy .
all_eq { all_rows ; color commentator ( s ) ; stu lantz } = true
Output: for the color commentator  s  records of all rows , all of them fuzzily match to stu lantz .
eq { hop { argmax { all_rows ; micro ( 10ha ) } ; department } ; potosi } = true
Output: select the row whose micro  10ha  record of all rows is maximum . the department record of this row is potosi .
most_eq { all_rows ; director ; sharon gosling } = true
Output: for the director records of all rows , most of them fuzzily match to sharon gosling .
most_eq { all_rows ; under - 11 ; not played } = true
Output: for the under  11 records of all rows , most of them fuzzily match to not played .
eq { hop { nth_argmin { all_rows ; date ; 1 } ; date } ; may 14 , 2008 } = true
Output: select the row whose date record of all rows is 1st minimum . the date record of this row is may 14 , 2008 .
eq { hop { argmax { all_rows ; avg attendance } ; year } ; 2009 } = true
Output: select the row whose avg attendance record of all rows is maximum . the year record of this row is 2009 .
eq { count { filter_greater { filter_less { all_rows ; matches ; 10 } ; innings ; 14 } } ; 2 } = true
Output: select the rows whose matches record is less than 10 . among these rows , select the rows whose innings record is greater than 14 . the number of such rows is 2 .
eq { nth_min { all_rows ; date ; 1 } ; october 24 , 2008 } = true
Output: the 1st minimum date record of all rows is october 24 , 2008 .
eq { hop { argmax { all_rows ; location attendance } ; team } ; la lakers } = true
Output: select the row whose location attendance record of all rows is maximum . the team record of this row is la lakers .
round_eq { avg { all_rows ; margin of victory } ; 4 } = true
Output: the average of the margin of victory record of all rows is 4 .
and { only { filter_eq { all_rows ; tournament ; egypt } } ; eq { hop { filter_eq { all_rows ; tournament ; egypt } ; date } ; 31 october 2010 } } = true
Output: select the rows whose tournament record fuzzily matches to egypt . there is only one such row in the table . the date record of this unqiue row is 31 october 2010 .
eq { count { filter_greater { filter_eq { all_rows ; venue ; central } ; capacity ; 5000 } } ; 2 } = true
Output: select the rows whose venue record fuzzily matches to central . among these rows , select the rows whose capacity record is greater than 5000 . the number of such rows is 2 .
eq { max { filter_eq { all_rows ; carrier ; orange } ; connection speed } ; 236.8 kbit / s } = true
Output: select the rows whose carrier record fuzzily matches to orange . the maximum connection speed record of these rows is 236.8 kbit  s .
eq { count { filter_eq { filter_eq { all_rows ; reason for change ; resigned } ; date successor seated ; november } } ; 4 } = true
Output: select the rows whose reason for change record fuzzily matches to resigned . among these rows , select the rows whose date successor seated record fuzzily matches to november . the number of such rows is 4 .
eq { hop { nth_argmin { filter_greater_eq { all_rows ; losses ; 15 } ; goal difference ; 1 } ; club } ; cd tarrasa } = true
Output: select the rows whose losses record is greater than or equal to 15 . select the row whose goal difference record of these rows is 1st minimum . the club record of this row is cd tarrasa .
greater { hop { filter_eq { all_rows ; year ; 1954 } ; points } ; hop { filter_eq { all_rows ; year ; 1957 } ; points } } = true
Output: select the rows whose year record fuzzily matches to 1954 . take the points record of this row . select the rows whose year record fuzzily matches to 1957 . take the points record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; floors ; 3 } ; street address } ; 1111 main street } = true
Output: select the row whose floors record of all rows is 3rd maximum . the street address record of this row is 1111 main street .
eq { count { filter_eq { all_rows ; outcome ; winner } } ; 3 } = true
Output: select the rows whose outcome record fuzzily matches to winner . the number of such rows is 3 .
less { hop { filter_eq { all_rows ; player ; ty warren } ; round } ; hop { filter_eq { all_rows ; player ; dan klecko } ; round } } = true
Output: select the rows whose player record fuzzily matches to ty warren . take the round record of this row . select the rows whose player record fuzzily matches to dan klecko . take the round record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; for } ; 31 } = true
Output: the average of the for record of all rows is 31 .
eq { hop { nth_argmin { all_rows ; date ; 3 } ; opponents } ; viking } = true
Output: select the row whose date record of all rows is 3rd minimum . the opponents record of this row is viking .
and { only { filter_eq { all_rows ; to par ; -1 } } ; eq { hop { filter_eq { all_rows ; to par ; -1 } ; player } ; jodie mudd } } = true
Output: select the rows whose to par record is equal to 1 . there is only one such row in the table . the player record of this unqiue row is jodie mudd .
most_less { all_rows ; top division titles ; 10 } = true
Output: for the top division titles records of all rows , most of them are less than 10 .
greater { hop { filter_eq { all_rows ; service area ; sw ireland } ; 2fm ( mhz ) } ; hop { filter_eq { all_rows ; service area ; se ireland } ; 2fm ( mhz ) } } = true
Output: select the rows whose service area record fuzzily matches to sw ireland . take the 2fm  mhz  record of this row . select the rows whose service area record fuzzily matches to se ireland . take the 2fm  mhz  record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; launch } ; 2001-2002 } = true
Output: the average of the launch record of all rows is 20012002 .
and { only { filter_eq { all_rows ; country ; australia } } ; eq { hop { filter_eq { all_rows ; country ; australia } ; player } ; david graham } } = true
Output: select the rows whose country record fuzzily matches to australia . there is only one such row in the table . the player record of this unqiue row is david graham .
eq { hop { nth_argmax { all_rows ; points ; 1 } ; club } ; pentyrch rfc } = true
Output: select the row whose points record of all rows is 1st maximum . the club record of this row is pentyrch rfc .
and { eq { hop { nth_argmin { all_rows ; founded ; 1 } ; institution } ; erskine college } ; eq { hop { nth_argmin { all_rows ; founded ; 1 } ; location } ; due west , south carolina } } = true
Output: select the row whose founded record of all rows is 1st minimum . the institution record of this row is erskine college . the location record of this row is due west , south carolina .
most_greater { all_rows ; innings ; 20 } = true
Output: for the innings records of all rows , most of them are greater than 20 .
most_less { all_rows ; radial velocity ( m / s ) ; 10 } = true
Output: for the radial velocity  m  s  records of all rows , most of them are less than 10 .
most_eq { all_rows ; location attendance ; oracle arena } = true
Output: for the location attendance records of all rows , most of them fuzzily match to oracle arena .
eq { count { filter_eq { all_rows ; director ; fraser } } ; 2 } = true
Output: select the rows whose director record fuzzily matches to fraser . the number of such rows is 2 .
round_eq { avg { all_rows ; attendance } ; 67503 } = true
Output: the average of the attendance record of all rows is 67503 .
and { eq { nth_min { all_rows ; us viewers ( million ) ; 1 } ; 7.70 } ; eq { hop { nth_argmin { all_rows ; us viewers ( million ) ; 1 } ; title } ; the dreamscape } } = true
Output: the 1st minimum us viewers  million  record of all rows is 7.70 . the title record of the row with 1st minimum us viewers  million  record is the dreamscape .
eq { hop { argmax { all_rows ; height ft ( m ) } ; name } ; 100 north tampa } = true
Output: select the row whose height ft  m  record of all rows is maximum . the name record of this row is 100 north tampa .
and { only { filter_eq { all_rows ; champions ; mercedes paz rene simpson } } ; eq { hop { filter_eq { all_rows ; champions ; mercedes paz rene simpson } ; year } ; 1995 } } = true
Output: select the rows whose champions record fuzzily matches to mercedes paz rene simpson . there is only one such row in the table . the year record of this unqiue row is 1995 .
eq { count { filter_eq { all_rows ; role ; police officer lung } } ; 5 } = true
Output: select the rows whose role record fuzzily matches to police officer lung . the number of such rows is 5 .
eq { hop { nth_argmin { all_rows ; date of appointment ; 3 } ; replaced by } ; ivan pudar } = true
Output: select the row whose date of appointment record of all rows is 3rd minimum . the replaced by record of this row is ivan pudar .
round_eq { avg { all_rows ; points } ; 42.91 } = true
Output: the average of the points record of all rows is 42.91 .
less { hop { filter_eq { all_rows ; candidate 's name ; michael mackay } ; rank } ; hop { filter_eq { all_rows ; candidate 's name ; joe larkin } ; rank } } = true
Output: select the rows whose candidate s name record fuzzily matches to michael mackay . take the rank record of this row . select the rows whose candidate s name record fuzzily matches to joe larkin . take the rank record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; points } ; 3 } = true
Output: the average of the points record of all rows is 3 .
greater { hop { filter_eq { all_rows ; season ; 2007 } ; games } ; hop { filter_eq { all_rows ; season ; 2006 } ; games } } = true
Output: select the rows whose season record fuzzily matches to 2007 . take the games record of this row . select the rows whose season record fuzzily matches to 2006 . take the games record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; region 2 ; 2004 } } ; 3 } = true
Output: select the rows whose region 2 record fuzzily matches to 2004 . the number of such rows is 3 .
and { only { filter_eq { all_rows ; isbn us ; unknown } } ; eq { hop { filter_eq { all_rows ; isbn us ; unknown } ; title } ; mission to venus } } = true
Output: select the rows whose isbn us record fuzzily matches to unknown . there is only one such row in the table . the title record of this unqiue row is mission to venus .
and { only { filter_eq { all_rows ; no party preference ; 22.6 % } } ; eq { hop { filter_eq { all_rows ; no party preference ; 22.6 % } ; city } ; lemon grove } } = true
Output: select the rows whose no party preference record fuzzily matches to 22.6  . there is only one such row in the table . the city record of this unqiue row is lemon grove .
most_eq { all_rows ; engine ; audi 3.6 l turbo v8 } = true
Output: for the engine records of all rows , most of them fuzzily match to audi 3.6 l turbo v8 .
eq { hop { argmax { all_rows ; average ratings } ; romaji title } ; karei - naru ichizoku } = true
Output: select the row whose average ratings record of all rows is maximum . the romaji title record of this row is karei  naru ichizoku .
eq { count { filter_eq { all_rows ; position ; ol } } ; 3 } = true
Output: select the rows whose position record fuzzily matches to ol . the number of such rows is 3 .
eq { count { filter_eq { filter_eq { all_rows ; nationality ; united states } ; position ; wing } } ; 2 } = true
Output: select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose position record fuzzily matches to wing . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; party ; democratic } } ; 3 } = true
Output: select the rows whose party record fuzzily matches to democratic . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; november 12 , 2000 } = true
Output: select the row whose attendance record of all rows is 2nd maximum . the date record of this row is november 12 , 2000 .
eq { hop { argmin { all_rows ; date of successors formal installation } ; successor } ; john parker hale ( r ) } = true
Output: select the row whose date of successors formal installation record of all rows is minimum . the successor record of this row is john parker hale  r  .
greater { hop { filter_eq { all_rows ; driver ; timo glock } ; laps } ; hop { filter_eq { all_rows ; driver ; adrian sutil } ; laps } } = true
Output: select the rows whose driver record fuzzily matches to timo glock . take the laps record of this row . select the rows whose driver record fuzzily matches to adrian sutil . take the laps record of this row . the first record is greater than the second record .
round_eq { sum { all_rows ; number of stations } ; 259 } = true
Output: the sum of the number of stations record of all rows is 259 .
and { only { filter_eq { all_rows ; score ; 2 - 2 } } ; and { eq { hop { filter_eq { all_rows ; score ; 2 - 2 } ; date } ; 8 october 2007 } ; eq { hop { filter_eq { all_rows ; score ; 2 - 2 } ; result } ; draw } } } = true
Output: select the rows whose score record fuzzily matches to 2  2 . there is only one such row in the table . the date record of this unqiue row is 8 october 2007 . the result record of this unqiue row is draw .
and { only { filter_eq { all_rows ; college ; southern illinois } } ; eq { hop { filter_eq { all_rows ; college ; southern illinois } ; name } ; damon jones } } = true
Output: select the rows whose college record fuzzily matches to southern illinois . there is only one such row in the table . the name record of this unqiue row is damon jones .
eq { hop { nth_argmin { all_rows ; event ; 3 } ; circuit } ; wakefield park } = true
Output: select the row whose event record of all rows is 3rd minimum . the circuit record of this row is wakefield park .
eq { count { filter_eq { all_rows ; year joined ; 1932 } } ; 7 } = true
Output: select the rows whose year joined record is equal to 1932 . the number of such rows is 7 .
eq { count { filter_eq { all_rows ; location ; japan } } ; 3 } = true
Output: select the rows whose location record fuzzily matches to japan . the number of such rows is 3 .
greater { hop { filter_eq { all_rows ; callsign ; xemr } ; frequency } ; hop { filter_eq { all_rows ; callsign ; xeg } ; frequency } } = true
Output: select the rows whose callsign record fuzzily matches to xemr . take the frequency record of this row . select the rows whose callsign record fuzzily matches to xeg . take the frequency record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; swimsuit ; 8.432 } } ; 2 } = true
Output: select the rows whose swimsuit record fuzzily matches to 8.432 . the number of such rows is 2 .
most_greater_eq { all_rows ; total votes ; 29000 } = true
Output: for the total votes records of all rows , most of them are greater than or equal to 29000 .
greater { hop { filter_eq { all_rows ; name ; víctor valdés } ; games } ; hop { filter_eq { all_rows ; name ; joan segarra } ; games } } = true
Output: select the rows whose name record fuzzily matches to víctor valdés . take the games record of this row . select the rows whose name record fuzzily matches to joan segarra . take the games record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; capacity } ; 22800 } = true
Output: the average of the capacity record of all rows is 22800 .
eq { count { filter_eq { all_rows ; habitat type ; forest steppe } } ; 8 } = true
Output: select the rows whose habitat type record fuzzily matches to forest steppe . the number of such rows is 8 .
eq { count { filter_eq { all_rows ; engine ; bmw } } ; 4 } = true
Output: select the rows whose engine record fuzzily matches to bmw . the number of such rows is 4 .
eq { count { filter_greater { filter_eq { all_rows ; date ; september } ; attendance ; 30000 } } ; 2 } = true
Output: select the rows whose date record fuzzily matches to september . among these rows , select the rows whose attendance record is greater than 30000 . the number of such rows is 2 .
eq { hop { argmin { all_rows ; pick } ; player } ; lee evans } = true
Output: select the row whose pick record of all rows is minimum . the player record of this row is lee evans .
and { only { filter_eq { all_rows ; competition ; 2004 copa américa } } ; eq { hop { filter_eq { all_rows ; competition ; 2004 copa américa } ; date } ; 14 july 2004 } } = true
Output: select the rows whose competition record fuzzily matches to 2004 copa américa . there is only one such row in the table . the date record of this unqiue row is 14 july 2004 .
and { only { filter_eq { filter_greater { all_rows ; gold ; 0 } ; bronze ; 2 } } ; eq { hop { filter_eq { filter_greater { all_rows ; gold ; 0 } ; bronze ; 2 } ; nation } ; soviet union ( urs ) } } = true
Output: select the rows whose gold record is greater than 0 . among these rows , select the rows whose bronze record is equal to 2 . there is only one such row in the table . the nation record of this unqiue row is soviet union  urs  .
round_eq { sum { all_rows ; votes } ; 3383 } = true
Output: the sum of the votes record of all rows is 3383 .
greater { hop { filter_eq { all_rows ; name ; ladislao kubala } ; goals } ; hop { filter_eq { all_rows ; name ; carles rexach } ; goals } } = true
Output: select the rows whose name record fuzzily matches to ladislao kubala . take the goals record of this row . select the rows whose name record fuzzily matches to carles rexach . take the goals record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; headquarters ; united kingdom } } ; 2 } = true
Output: select the rows whose headquarters record fuzzily matches to united kingdom . the number of such rows is 2 .
eq { hop { argmax { all_rows ; high points } ; high points } ; dalembert ( 23 ) } = true
Output: select the row whose high points record of all rows is maximum . the high points record of this row is dalembert  23  .
and { only { filter_eq { all_rows ; relationship with predecessor ; and } } ; eq { hop { filter_eq { all_rows ; relationship with predecessor ; and } ; name } ; maximilian } } = true
Output: select the rows whose relationship with predecessor record fuzzily matches to and . there is only one such row in the table . the name record of this unqiue row is maximilian .
eq { hop { argmax { all_rows ; age } ; candidate } ; kirsty parkhill } = true
Output: select the row whose age record of all rows is maximum . the candidate record of this row is kirsty parkhill .
eq { count { filter_eq { all_rows ; shooter ; cze } } ; 2 } = true
Output: select the rows whose shooter record fuzzily matches to cze . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; position ; power forward } } ; 3 } = true
Output: select the rows whose position record fuzzily matches to power forward . the number of such rows is 3 .
round_eq { avg { all_rows ; attendance } ; 26593 } = true
Output: the average of the attendance record of all rows is 26593 .
less { hop { filter_eq { all_rows ; artist ; dire straits } ; claimed sales } ; hop { filter_eq { all_rows ; artist ; barbra streisand } ; claimed sales } } = true
Output: select the rows whose artist record fuzzily matches to dire straits . take the claimed sales record of this row . select the rows whose artist record fuzzily matches to barbra streisand . take the claimed sales record of this row . the first record is less than the second record .
all_eq { all_rows ; authority ; state } = true
Output: for the authority records of all rows , all of them fuzzily match to state .
eq { hop { argmax { all_rows ; date } ; number & name } ; no 07005 } = true
Output: select the row whose date record of all rows is maximum . the number  name record of this row is no 07005 .
eq { nth_max { filter_eq { all_rows ; location ; woodlands stadium ndola } ; date ; 1 } ; 29 june 2008 } = true
Output: select the rows whose location record fuzzily matches to woodlands stadium ndola . the 1st maximum date record of these rows is 29 june 2008 .
greater { hop { filter_eq { all_rows ; nation ; ecuador } ; 2011 ( imf ) } ; hop { filter_eq { all_rows ; nation ; paraguay } ; 2011 ( imf ) } } = true
Output: select the rows whose nation record fuzzily matches to ecuador . take the 2011  imf  record of this row . select the rows whose nation record fuzzily matches to paraguay . take the 2011  imf  record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; tournament ; taipei , taiwan } } ; 2 } = true
Output: select the rows whose tournament record fuzzily matches to taipei , taiwan . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; population ; 2 } ; official name } ; hillsborough } = true
Output: select the row whose population record of all rows is 2nd maximum . the official name record of this row is hillsborough .
and { only { filter_greater { all_rows ; metres ; 300 } } ; eq { hop { filter_greater { all_rows ; metres ; 300 } ; name } ; the shard } } = true
Output: select the rows whose metres record is greater than 300 . there is only one such row in the table . the name record of this unqiue row is the shard .
eq { count { filter_eq { all_rows ; date ; 1995 } } ; 2 } = true
Output: select the rows whose date record fuzzily matches to 1995 . the number of such rows is 2 .
most_eq { all_rows ; date ; september } = true
Output: for the date records of all rows , most of them fuzzily match to september .
less { hop { filter_eq { all_rows ; opponent ; jason st louis } ; time } ; hop { filter_eq { all_rows ; opponent ; mike swick } ; time } } = true
Output: select the rows whose opponent record fuzzily matches to jason st louis . take the time record of this row . select the rows whose opponent record fuzzily matches to mike swick . take the time record of this row . the first record is less than the second record .
only { filter_eq { all_rows ; first audition date ; september } } = true
Output: select the rows whose first audition date record fuzzily matches to september . there is only one such row in the table .
round_eq { avg { all_rows ; attendance } ; 28,610 } = true
Output: the average of the attendance record of all rows is 28,610 .
less { hop { filter_eq { all_rows ; president ; joão havelange } ; took office } ; hop { filter_eq { all_rows ; president ; sepp blatter } ; took office } } = true
Output: select the rows whose president record fuzzily matches to joão havelange . take the took office record of this row . select the rows whose president record fuzzily matches to sepp blatter . take the took office record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; manner of departure ; contract terminated } } ; 6 } = true
Output: select the rows whose manner of departure record fuzzily matches to contract terminated . the number of such rows is 6 .
most_greater { all_rows ; population ( 2011 - 01 - 01 ) ; 511840 } = true
Output: for the population  2011  01  01  records of all rows , most of them are greater than 511840 .
all_eq { all_rows ; date ; 25 august 1979 } = true
Output: for the date records of all rows , all of them fuzzily match to 25 august 1979 .
eq { hop { nth_argmax { all_rows ; us viewers ( million ) ; 2 } ; title } ; sabotage } = true
Output: select the row whose us viewers  million  record of all rows is 2nd maximum . the title record of this row is sabotage .
eq { hop { argmax { all_rows ; stages } ; name } ; air21 tour pilipinas } = true
Output: select the row whose stages record of all rows is maximum . the name record of this row is air21 tour pilipinas .
eq { count { filter_eq { filter_eq { all_rows ; date ; november } ; result ; w } } ; 3 } = true
Output: select the rows whose date record fuzzily matches to november . among these rows , select the rows whose result record fuzzily matches to w . the number of such rows is 3 .
eq { hop { argmax { all_rows ; attendance } ; date } ; october 11 } = true
Output: select the row whose attendance record of all rows is maximum . the date record of this row is october 11 .
round_eq { avg { all_rows ; money } ; 1584 } = true
Output: the average of the money record of all rows is 1584 .
most_less { all_rows ; built ; 2000 } = true
Output: for the built records of all rows , most of them are less than 2000 .
eq { nth_min { filter_greater { all_rows ; height ft ( m ) ; 200 } ; floors ( stories ) ; 1 } ; 12 } = true
Output: select the rows whose height ft  m  record is greater than 200 . the 1st minimum floors  stories  record of these rows is 12 .
less { hop { filter_eq { all_rows ; player ; fedor dmitriev } ; year born } ; hop { filter_eq { all_rows ; player ; anton ponkrashov } ; year born } } = true
Output: select the rows whose player record fuzzily matches to fedor dmitriev . take the year born record of this row . select the rows whose player record fuzzily matches to anton ponkrashov . take the year born record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; venue ; princes park } } ; eq { hop { filter_eq { all_rows ; venue ; princes park } ; away team } ; hawthorn } } = true
Output: select the rows whose venue record fuzzily matches to princes park . there is only one such row in the table . the away team record of this unqiue row is hawthorn .
greater { hop { filter_eq { all_rows ; driver ; mark webber } ; time / retired } ; hop { filter_eq { all_rows ; driver ; jarno trulli } ; time / retired } } = true
Output: select the rows whose driver record fuzzily matches to mark webber . take the time  retired record of this row . select the rows whose driver record fuzzily matches to jarno trulli . take the time  retired record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; attendance / g } ; 11360 } = true
Output: the average of the attendance  g record of all rows is 11360 .
most_eq { filter_greater { all_rows ; enrollment ; 350 } ; ihsaa football class ; a } = true
Output: select the rows whose enrollment record is greater than 350 . for the ihsaa football class records of these rows , most of them fuzzily match to a .
less { hop { filter_eq { all_rows ; opponent ; ottawa senators } ; february } ; hop { filter_eq { all_rows ; opponent ; nashville predators } ; february } } = true
Output: select the rows whose opponent record fuzzily matches to ottawa senators . take the february record of this row . select the rows whose opponent record fuzzily matches to nashville predators . take the february record of this row . the first record is less than the second record .
greater { hop { filter_eq { all_rows ; skip ; wang bingyu } ; shot % } ; hop { filter_eq { all_rows ; skip ; anna sidorova } ; shot % } } = true
Output: select the rows whose skip record fuzzily matches to wang bingyu . take the shot  record of this row . select the rows whose skip record fuzzily matches to anna sidorova . take the shot  record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; viewing figure } ; episode } ; episode 11 } = true
Output: select the row whose viewing figure record of all rows is maximum . the episode record of this row is episode 11 .
eq { hop { argmin { all_rows ; swing to gain } ; constituency } ; dundee east } = true
Output: select the row whose swing to gain record of all rows is minimum . the constituency record of this row is dundee east .
eq { hop { nth_argmax { all_rows ; tonnes ; 2 } ; location } ; anchorage , alaska } = true
Output: select the row whose tonnes record of all rows is 2nd maximum . the location record of this row is anchorage , alaska .
eq { count { filter_eq { all_rows ; premier date ; 2004 } } ; 4 } = true
Output: select the rows whose premier date record fuzzily matches to 2004 . the number of such rows is 4 .
most_less { all_rows ; percentage of possible points ; 70 } = true
Output: for the percentage of possible points records of all rows , most of them are less than 70 .
eq { count { filter_eq { filter_eq { all_rows ; country ; united states } ; score ; 143 } } ; 2 } = true
Output: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose score record fuzzily matches to 143 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; size } ; school } ; mitchell } = true
Output: select the row whose size record of all rows is maximum . the school record of this row is mitchell .
and { only { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; retired } } ; eq { hop { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; retired } ; incumbent } ; jack fields } } = true
Output: select the rows whose party record fuzzily matches to republican . among these rows , select the rows whose result record fuzzily matches to retired . there is only one such row in the table . the incumbent record of this unqiue row is jack fields .
eq { hop { argmin { all_rows ; rank } ; nation } ; turkey } = true
Output: select the row whose rank record of all rows is minimum . the nation record of this row is turkey .
and { only { filter_eq { all_rows ; party ; democratic } } ; eq { hop { filter_eq { all_rows ; party ; democratic } ; incumbent } ; robert cramer } } = true
Output: select the rows whose party record fuzzily matches to democratic . there is only one such row in the table . the incumbent record of this unqiue row is robert cramer .
most_greater { all_rows ; earnings ; 10000000 } = true
Output: for the earnings records of all rows , most of them are greater than 10000000 .
and { only { filter_eq { all_rows ; reason for change ; failure to elect } } ; eq { hop { filter_eq { all_rows ; reason for change ; failure to elect } ; state ( class ) } ; tennessee ( 2 ) } } = true
Output: select the rows whose reason for change record fuzzily matches to failure to elect . there is only one such row in the table . the state  class  record of this unqiue row is tennessee  2  .
eq { count { filter_greater { filter_eq { all_rows ; date ; november } ; attendance ; 60,000 } } ; 1 } = true
Output: select the rows whose date record fuzzily matches to november . among these rows , select the rows whose attendance record is greater than 60,000 . the number of such rows is 1 .
round_eq { avg { filter_eq { all_rows ; year ; freshman } ; weight } ; 215 } = true
Output: select the rows whose year record fuzzily matches to freshman . the average of the weight record of these rows is 215 .
eq { hop { nth_argmax { all_rows ; frequency mhz ; 2 } ; call sign } ; w294aj } = true
Output: select the row whose frequency mhz record of all rows is 2nd maximum . the call sign record of this row is w294aj .
round_eq { sum { all_rows ; national titles } ; 79 } = true
Output: the sum of the national titles record of all rows is 79 .
most_eq { all_rows ; format ; 2cd } = true
Output: for the format records of all rows , most of them fuzzily match to 2cd .
eq { hop { argmax { all_rows ; enrollment } ; institution } ; johnson & wales university } = true
Output: select the row whose enrollment record of all rows is maximum . the institution record of this row is johnson  wales university .
most_less { all_rows ; weight ; 200 } = true
Output: for the weight records of all rows , most of them are less than 200 .
round_eq { avg { all_rows ; crowd } ; 24303 } = true
Output: the average of the crowd record of all rows is 24303 .
greater { hop { filter_eq { all_rows ; player ; mitchell duke } ; goals } ; hop { filter_eq { all_rows ; player ; john hutchinson } ; goals } } = true
Output: select the rows whose player record fuzzily matches to mitchell duke . take the goals record of this row . select the rows whose player record fuzzily matches to john hutchinson . take the goals record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; original air date ; august } } ; eq { hop { filter_eq { all_rows ; original air date ; august } ; family / families } ; the hillhouse - docherty family } } = true
Output: select the rows whose original air date record fuzzily matches to august . there is only one such row in the table . the family  families record of this unqiue row is the hillhouse  docherty family .
round_eq { avg { all_rows ; rating } ; 0.6 } = true
Output: the average of the rating record of all rows is 0.6 .
round_eq { avg { all_rows ; hydropathy index } ; 2.6 } = true
Output: the average of the hydropathy index record of all rows is 2.6 .
and { only { filter_not_eq { all_rows ; dvd release ; - } } ; eq { hop { filter_not_eq { all_rows ; dvd release ; - } ; season } ; 1 } } = true
Output: select the rows whose dvd release record is not equal to  . there is only one such row in the table . the season record of this unqiue row is 1 .
round_eq { sum { all_rows ; gold } ; 70 } = true
Output: the sum of the gold record of all rows is 70 .
and { eq { min { filter_eq { all_rows ; label ; ! k7 } ; date } ; 14 may } ; and { eq { hop { argmin { filter_eq { all_rows ; label ; ! k7 } ; date } ; year } ; 2007 } ; eq { hop { argmin { filter_eq { all_rows ; label ; ! k7 } ; date } ; album } ; stateless } } } = true
Output: select the rows whose label record fuzzily matches to ! k7 . the minimum date record of these rows is 14 may . the year record of the row with superlative date record is 2007 . the album record of the row with superlative date record is stateless .
eq { hop { nth_argmin { filter_eq { all_rows ; date successor seated ; may } ; date successor seated ; 1 } ; district } ; california 10th } = true
Output: select the rows whose date successor seated record fuzzily matches to may . select the row whose date successor seated record of these rows is 1st minimum . the district record of this row is california 10th .
eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; candidates } ; charles edward bennett ( d ) unopposed } = true
Output: select the row whose first elected record of all rows is 2nd minimum . the candidates record of this row is charles edward bennett  d  unopposed .
all_greater { all_rows ; year ; 1970 } = true
Output: for the year records of all rows , all of them are greater than 1970 .
eq { hop { argmax { all_rows ; original airdate } ; episode } ; 13 } = true
Output: select the row whose original airdate record of all rows is maximum . the episode record of this row is 13 .
and { only { filter_eq { all_rows ; wins ; 6 } } ; eq { hop { filter_eq { all_rows ; wins ; 6 } ; team } ; montreal victorias } } = true
Output: select the rows whose wins record is equal to 6 . there is only one such row in the table . the team record of this unqiue row is montreal victorias .
all_eq { all_rows ; format ; news / talk } = true
Output: for the format records of all rows , all of them fuzzily match to news  talk .
eq { hop { argmax { all_rows ; total wins } ; nationality } ; united states } = true
Output: select the row whose total wins record of all rows is maximum . the nationality record of this row is united states .
and { only { filter_greater { all_rows ; frequency mhz ; 100 } } ; eq { hop { filter_greater { all_rows ; frequency mhz ; 100 } ; city of license } ; anderson , south carolina } } = true
Output: select the rows whose frequency mhz record is greater than 100 . there is only one such row in the table . the city of license record of this unqiue row is anderson , south carolina .
and { only { filter_eq { all_rows ; result ; draw } } ; eq { hop { filter_eq { all_rows ; result ; draw } ; opponent } ; cătălin zmărăndescu } } = true
Output: select the rows whose result record fuzzily matches to draw . there is only one such row in the table . the opponent record of this unqiue row is cătălin zmărăndescu .
eq { count { filter_greater_eq { all_rows ; height ( ft ) ; 800 } } ; 2 } = true
Output: select the rows whose height  ft  record is greater than or equal to 800 . the number of such rows is 2 .
and { only { filter_eq { all_rows ; channel ; 40 } } ; eq { hop { filter_eq { all_rows ; channel ; 40 } ; territory } ; indonesia } } = true
Output: select the rows whose channel record is equal to 40 . there is only one such row in the table . the territory record of this unqiue row is indonesia .
round_eq { avg { filter_eq { all_rows ; country ; united states } ; to par } ; 9.67 } = true
Output: select the rows whose country record fuzzily matches to united states . the average of the to par record of these rows is 9.67 .
eq { hop { argmin { all_rows ; date } ; race name } ; vi glover trophy } = true
Output: select the row whose date record of all rows is minimum . the race name record of this row is vi glover trophy .
eq { hop { argmin { all_rows ; final round } ; city / state } ; texas } = true
Output: select the row whose final round record of all rows is minimum . the city  state record of this row is texas .
and { eq { hop { argmax { all_rows ; crowd } ; home team } ; south melbourne } ; eq { hop { argmax { all_rows ; crowd } ; away team } ; carlton } } = true
Output: select the row whose crowd record of all rows is maximum . the home team record of this row is south melbourne . the away team record of this row is carlton .
and { eq { nth_min { all_rows ; launch date ; 2 } ; 25 january 1958 } ; eq { hop { nth_argmin { all_rows ; launch date ; 2 } ; name } ; rta tv - 5 } } = true
Output: the 2nd minimum launch date record of all rows is 25 january 1958 . the name record of the row with 2nd minimum launch date record is rta tv  5 .
eq { hop { argmin { all_rows ; years for grizzlies } ; player } ; bryant reeves } = true
Output: select the row whose years for grizzlies record of all rows is minimum . the player record of this row is bryant reeves .
eq { count { filter_eq { all_rows ; original air date ; april } } ; 4 } = true
Output: select the rows whose original air date record fuzzily matches to april . the number of such rows is 4 .
eq { count { filter_all { all_rows ; film } } ; 6 } = true
Output: select the rows whose film record is arbitrary . the number of such rows is 6 .
eq { hop { nth_argmin { all_rows ; time ; 3 } ; title } ; my lord what a mornin ' } = true
Output: select the row whose time record of all rows is 3rd minimum . the title record of this row is my lord what a mornin  .
most_eq { all_rows ; original air date ; 2008 } = true
Output: for the original air date records of all rows , most of them fuzzily match to 2008 .
eq { hop { argmax { all_rows ; floors } ; name } ; bank one tower } = true
Output: select the row whose floors record of all rows is maximum . the name record of this row is bank one tower .
eq { hop { nth_argmax { all_rows ; total ; 2 } ; player } ; djibril cisse } = true
Output: select the row whose total record of all rows is 2nd maximum . the player record of this row is djibril cisse .
and { greater { hop { filter_eq { all_rows ; name ; channel 3 } ; broadcasting hours } ; hop { filter_eq { all_rows ; name ; thai pbs } ; broadcasting hours } } ; and { eq { hop { filter_eq { all_rows ; name ; channel 3 } ; broadcasting hours } ; 24 - hours } ; eq { hop { filter_eq { all_rows ; name ; thai pbs } ; broadcasting hours } ; 21 - hours ( 5:00 am - 2:00 am ) } } } = true
Output: select the rows whose name record fuzzily matches to channel 3 . take the broadcasting hours record of this row . select the rows whose name record fuzzily matches to thai pbs . take the broadcasting hours record of this row . the first record is greater than the second record . the broadcasting hours record of the first row is 24  hours . the broadcasting hours record of the second row is 21  hours  5:00 am  2:00 am  .
greater { hop { filter_eq { all_rows ; venue ; mcg } ; crowd } ; hop { filter_eq { all_rows ; venue ; vfl park } ; crowd } } = true
Output: select the rows whose venue record fuzzily matches to mcg . take the crowd record of this row . select the rows whose venue record fuzzily matches to vfl park . take the crowd record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; lions points } ; opponent } ; minnesota vikings } = true
Output: select the row whose lions points record of all rows is maximum . the opponent record of this row is minnesota vikings .
and { eq { min { all_rows ; rank } ; 1 } ; eq { hop { argmin { all_rows ; rank } ; water park } ; typhoon lagoon at walt disney world resort } } = true
Output: the minimum rank record of all rows is 1 . the water park record of the row with superlative rank record is typhoon lagoon at walt disney world resort .
and { eq { max { all_rows ; points } ; 581 } ; eq { hop { argmax { all_rows ; points } ; player } ; kendall gill } } = true
Output: the maximum points record of all rows is 581 . the player record of the row with superlative points record is kendall gill .
most_greater { all_rows ; attendance ; 51,000 } = true
Output: for the attendance records of all rows , most of them are greater than 51,000 .
eq { count { filter_eq { filter_eq { all_rows ; result ; re - elected } ; first elected ; 1960 } } ; 2 } = true
Output: select the rows whose result record fuzzily matches to re  elected . among these rows , select the rows whose first elected record is equal to 1960 . the number of such rows is 2 .
and { only { filter_eq { all_rows ; chassis ; williams fw04 } } ; and { eq { hop { filter_eq { all_rows ; chassis ; williams fw04 } ; year } ; 1975 } ; eq { hop { filter_eq { all_rows ; chassis ; williams fw04 } ; entrant } ; frank williams racing cars } } } = true
Output: select the rows whose chassis record fuzzily matches to williams fw04 . there is only one such row in the table . the year record of this unqiue row is 1975 . the entrant record of this unqiue row is frank williams racing cars .
and { only { filter_eq { all_rows ; position ; test driver } } ; eq { hop { filter_eq { all_rows ; position ; test driver } ; series } ; formula one } } = true
Output: select the rows whose position record fuzzily matches to test driver . there is only one such row in the table . the series record of this unqiue row is formula one .
round_eq { avg { all_rows ; speed } ; 87.75 } = true
Output: the average of the speed record of all rows is 87.75 .
and { only { filter_eq { filter_eq { all_rows ; method ; tko } ; method ; strikes } } ; eq { hop { filter_eq { filter_eq { all_rows ; method ; tko } ; method ; strikes } ; opponent } ; kevin dolan } } = true
Output: select the rows whose method record fuzzily matches to tko . among these rows , select the rows whose method record fuzzily matches to strikes . there is only one such row in the table . the opponent record of this unqiue row is kevin dolan .
eq { hop { nth_argmin { filter_eq { all_rows ; nationality ; jamaica } ; reaction ; 2 } ; name } ; brigitte foster - hylton } = true
Output: select the rows whose nationality record fuzzily matches to jamaica . select the row whose reaction record of these rows is 2nd minimum . the name record of this row is brigitte foster  hylton .
eq { count { filter_eq { all_rows ; 1st round ; 2 - 0 } } ; 3 } = true
Output: select the rows whose 1st round record fuzzily matches to 2  0 . the number of such rows is 3 .
eq { hop { argmax { all_rows ; mintage } ; theme } ; steam buggy } = true
Output: select the row whose mintage record of all rows is maximum . the theme record of this row is steam buggy .
round_eq { avg { all_rows ; weight ( kg ) } ; 55.75 } = true
Output: the average of the weight  kg  record of all rows is 55.75 .
and { only { filter_eq { all_rows ; frequency ; 830 khz } } ; eq { hop { filter_eq { all_rows ; frequency ; 830 khz } ; calls } ; wcrn } } = true
Output: select the rows whose frequency record fuzzily matches to 830 khz . there is only one such row in the table . the calls record of this unqiue row is wcrn .
round_eq { sum { all_rows ; tournaments played } ; 38 } = true
Output: the sum of the tournaments played record of all rows is 38 .
eq { count { filter_eq { all_rows ; notes ; not published in book form } } ; 9 } = true
Output: select the rows whose notes record fuzzily matches to not published in book form . the number of such rows is 9 .
eq { hop { nth_argmax { all_rows ; singapore gross ; 2 } ; title } ; the eye 2 } = true
Output: select the row whose singapore gross record of all rows is 2nd maximum . the title record of this row is the eye 2 .
round_eq { sum { all_rows ; races } ; 123 } = true
Output: the sum of the races record of all rows is 123 .
most_eq { all_rows ; position ; wing } = true
Output: for the position records of all rows , most of them fuzzily match to wing .
and { only { filter_eq { all_rows ; college ; arizona state } } ; eq { hop { filter_eq { all_rows ; college ; arizona state } ; player } ; tony lorick } } = true
Output: select the rows whose college record fuzzily matches to arizona state . there is only one such row in the table . the player record of this unqiue row is tony lorick .
greater { hop { filter_eq { all_rows ; date ; 28 october 2003 } ; score } ; hop { filter_eq { all_rows ; date ; 3 january 2010 } ; score } } = true
Output: select the rows whose date record fuzzily matches to 28 october 2003 . take the score record of this row . select the rows whose date record fuzzily matches to 3 january 2010 . take the score record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; to par ; -4 } } ; eq { hop { filter_eq { all_rows ; to par ; -4 } ; player } ; jim colbert } } = true
Output: select the rows whose to par record is equal to 4 . there is only one such row in the table . the player record of this unqiue row is jim colbert .
eq { count { filter_less { filter_greater { all_rows ; total goals ; 250 } ; total appearances ; 400 } } ; 2 } = true
Output: select the rows whose total goals record is greater than 250 . among these rows , select the rows whose total appearances record is less than 400 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; points } ; artist } ; kalomira } = true
Output: select the row whose points record of all rows is maximum . the artist record of this row is kalomira .
eq { hop { argmax { all_rows ; bronze } ; nation } ; germany ( ger ) } = true
Output: select the row whose bronze record of all rows is maximum . the nation record of this row is germany  ger  .