niryuu/sni-each-converted huggingface dataset:
for the chassis records of all rows , most of them fuzzily match to talbot-lago t26c da .
Output: most_eq { all_rows ; chassis ; talbot-lago t26c da }
select the row whose p1 diameter ( mm ) record of all rows is 2nd maximum . the chambering record of this row is .45 acp .
Output: eq { hop { nth_argmax { all_rows ; p1 diameter ( mm ) ; 2 } ; chambering } ; .45 acp }
for the type records of all rows , most of them fuzzily match to private .
Output: most_eq { all_rows ; type ; private }
select the rows whose executive director record fuzzily matches to jessie a montalbo . there is only one such row in the table .
Output: only { filter_eq { all_rows ; executive director ; jessie a montalbo } }
the average of the score record of all rows is 145 .
Output: round_eq { avg { all_rows ; score } ; 145 }
select the row whose founded record of all rows is minimum . the team record of this row is northern demons .
Output: eq { hop { argmin { all_rows ; founded } ; team } ; northern demons }
select the row whose points record of all rows is maximum . the club record of this row is wigan warriors .
Output: eq { hop { argmax { all_rows ; points } ; club } ; wigan warriors }
select the row whose time record of all rows is minimum . the opponent record of this row is jeff davis .
Output: eq { hop { argmin { all_rows ; time } ; opponent } ; jeff davis }
select the rows whose year record is equal to 2013 . among these rows , select the rows whose result record fuzzily matches to won . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; year ; 2013 } ; result ; won } } ; 3 }
select the rows whose result record fuzzily matches to ot . there is only one such row in the table . the date record of this unqiue row is november 24 , 1985 .
Output: and { only { filter_eq { all_rows ; result ; ot } } ; eq { hop { filter_eq { all_rows ; result ; ot } ; date } ; november 24 , 1985 } }
select the rows whose opponent record fuzzily matches to new england patriots . take the attendance record of this row . select the rows whose opponent record fuzzily matches to cleveland browns . take the attendance record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; new england patriots } ; attendance } ; hop { filter_eq { all_rows ; opponent ; cleveland browns } ; attendance } }
select the rows whose directed by record fuzzily matches to david grossman . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; directed by ; david grossman } } ; 5 }
select the rows whose location record fuzzily matches to japan . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; location ; japan } } ; 3 }
select the row whose first air date record of all rows is 2nd minimum . the city record of this row is cairns .
Output: eq { hop { nth_argmin { all_rows ; first air date ; 2 } ; city } ; cairns }
select the row whose goals scored record of all rows is maximum . the team record of this row is san salvador fc .
Output: eq { hop { argmax { all_rows ; goals scored } ; team } ; san salvador fc }
select the row whose viewing figure record of all rows is maximum . the episode record of this row is episode 11 .
Output: eq { hop { argmax { all_rows ; viewing figure } ; episode } ; episode 11 }
for the pole position records of all rows , most of them fuzzily match to rick mears .
Output: most_eq { all_rows ; pole position ; rick mears }
select the rows whose aspect record fuzzily matches to 16:9 . there is only one such row in the table . the channel record of this unqiue row is 26.1 .
Output: and { only { filter_eq { all_rows ; aspect ; 16:9 } } ; eq { hop { filter_eq { all_rows ; aspect ; 16:9 } ; channel } ; 26.1 } }
select the rows whose a - league record fuzzily matches to ( 0 ) . there is only one such row in the table . the name record of this unqiue row is kevin muscat .
Output: and { only { filter_eq { all_rows ; a - league ; ( 0 ) } } ; eq { hop { filter_eq { all_rows ; a - league ; ( 0 ) } ; name } ; kevin muscat } }
select the rows whose political party record fuzzily matches to democratic party of albania . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; political party ; democratic party of albania } } ; 2 }
select the rows whose vacator record fuzzily matches to ( d ) . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; vacator ; ( d ) } } ; 7 }
select the rows whose date record fuzzily matches to january 31 , 2008 . take the score record of this row . select the rows whose date record fuzzily matches to january 26 , 2008 . take the score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; january 31 , 2008 } ; score } ; hop { filter_eq { all_rows ; date ; january 26 , 2008 } ; score } }
select the rows whose title record fuzzily matches to the chipmunk adventure . take the year record of this row . select the rows whose title record fuzzily matches to alvin and the chipmunks meet the wolfman . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; title ; the chipmunk adventure } ; year } ; hop { filter_eq { all_rows ; title ; alvin and the chipmunks meet the wolfman } ; year } }
select the rows whose championship record fuzzily matches to wimbledon . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; championship ; wimbledon } } ; 2 }
select the rows whose class record fuzzily matches to b . the sum of the no built record of these rows is 58 .
Output: round_eq { sum { filter_eq { all_rows ; class ; b } ; no built } ; 58 }
select the rows whose country record fuzzily matches to england . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; country ; england } } ; 4 }
select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table . the tournament record of this unqiue row is eckental , germany .
Output: and { only { filter_eq { all_rows ; surface ; carpet } } ; eq { hop { filter_eq { all_rows ; surface ; carpet } ; tournament } ; eckental , germany } }
select the rows whose party record fuzzily matches to republican . among these rows , select the rows whose result record fuzzily matches to resigned . the number of such rows is 5 .
Output: eq { count { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; resigned } } ; 5 }
the sum of the number of families record of all rows is 30630 .
Output: round_eq { sum { all_rows ; number of families } ; 30630 }
select the rows whose language record fuzzily matches to french . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; language ; french } } ; 4 }
select the row whose pick record of all rows is 1st minimum . the player record of this row is toni stãhl .
Output: eq { hop { nth_argmin { all_rows ; pick ; 1 } ; player } ; toni stãhl }
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 .
Output: greater { hop { filter_eq { all_rows ; service area ; sw ireland } ; 2fm ( mhz ) } ; hop { filter_eq { all_rows ; service area ; se ireland } ; 2fm ( mhz ) } }
the average of the individual winners record of all rows is 2.08 .
Output: round_eq { avg { all_rows ; individual winners } ; 2.08 }
for the method records of all rows , most of them fuzzily match to punches .
Output: most_eq { all_rows ; method ; punches }
the average of the races record of all rows is 8.73 .
Output: round_eq { avg { all_rows ; races } ; 8.73 }
select the rows whose score record fuzzily matches to 0 . among these rows , select the rows whose attendance record is greater than 4000 . there is only one such row in the table . the home team record of this unqiue row is milton keynes dons .
Output: and { only { filter_greater { filter_eq { all_rows ; score ; 0 } ; attendance ; 4000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; score ; 0 } ; attendance ; 4000 } ; home team } ; milton keynes dons } }
the average of the length ( ft ) record of all rows is 86.25 .
Output: round_eq { avg { all_rows ; length ( ft ) } ; 86.25 }
select the row whose floors record of all rows is maximum . the name record of this row is kingdom tower .
Output: eq { hop { argmax { all_rows ; floors } ; name } ; kingdom tower }
select the row whose commissioned record of all rows is maximum . the name record of this row is chivalrous .
Output: eq { hop { argmax { all_rows ; commissioned } ; name } ; chivalrous }
select the rows whose manufacturer record fuzzily matches to pontiac . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; manufacturer ; pontiac } } ; 2 }
select the rows whose high assists record fuzzily matches to steve blake . among these rows , select the rows whose score record fuzzily matches to w . there is only one such row in the table . the date record of this unqiue row is april 21 .
Output: and { only { filter_eq { filter_eq { all_rows ; high assists ; steve blake } ; score ; w } } ; eq { hop { filter_eq { filter_eq { all_rows ; high assists ; steve blake } ; score ; w } ; date } ; april 21 } }
select the rows whose sport record fuzzily matches to association football . among these rows , select the rows whose country record fuzzily matches to england . the number of such rows is 6 .
Output: eq { count { filter_eq { filter_eq { all_rows ; sport ; association football } ; country ; england } } ; 6 }
select the row whose semifinalists record of all rows is 4th maximum . the country / territory record of this row is australia .
Output: eq { hop { nth_argmax { all_rows ; semifinalists ; 4 } ; country / territory } ; australia }
select the row whose date record of all rows is 2nd maximum . the venue record of this row is vasil levski national stadium , sofia .
Output: eq { hop { nth_argmax { all_rows ; date ; 2 } ; venue } ; vasil levski national stadium , sofia }
select the rows whose height record fuzzily matches to 6 ' 2 . select the row whose weight record of these rows is 1st minimum . the college record of this row is hawaii .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; height ; 6 ' 2 } ; weight ; 1 } ; college } ; hawaii }
select the rows whose total matches record is equal to 5 . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; total matches ; 5 } } ; 6 }
select the rows whose team record fuzzily matches to forsythe racing . the sum of the points record of these rows is 8 .
Output: round_eq { sum { filter_eq { all_rows ; team ; forsythe racing } ; points } ; 8 }
the average of the us viewers ( millions ) record of all rows is 8.55 million .
Output: round_eq { avg { all_rows ; us viewers ( millions ) } ; 8.55 million }
select the rows whose first publisher record fuzzily matches to shodensha non - novels . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; first publisher ; shodensha non - novels } } ; 2 }
the 2nd minimum years for grizzlies record of all rows is 1997 - 1999 . the player record of the row with 2nd minimum years for grizzlies record is michael smith .
Output: and { eq { nth_min { all_rows ; years for grizzlies ; 2 } ; 1997 - 1999 } ; eq { hop { nth_argmin { all_rows ; years for grizzlies ; 2 } ; player } ; michael smith } }
select the rows whose l3 cache record fuzzily matches to 8 mb . among these rows , select the rows whose frequency record fuzzily matches to 3 ghz . there is only one such row in the table . the model number record of this unqiue row is core i7 - 3940xm .
Output: and { only { filter_eq { filter_eq { all_rows ; l3 cache ; 8 mb } ; frequency ; 3 ghz } } ; eq { hop { filter_eq { filter_eq { all_rows ; l3 cache ; 8 mb } ; frequency ; 3 ghz } ; model number } ; core i7 - 3940xm } }
for the silver records of all rows , most of them are greater than or equal to 1 .
Output: most_greater_eq { all_rows ; silver ; 1 }
the average of the to par record of all rows is 3 .
Output: round_eq { avg { all_rows ; to par } ; 3 }
select the rows whose edition record fuzzily matches to 2008 davis cup europe / africa group ii . take the surface record of this row . select the rows whose edition record fuzzily matches to 2013 davis cup europe / africa group ii . take the surface record of this row . the first record fuzzily matches to the second record . the surface record of the first row is clay . the surface record of the second row is clay .
Output: and { eq { hop { filter_eq { all_rows ; edition ; 2008 davis cup europe / africa group ii } ; surface } ; hop { filter_eq { all_rows ; edition ; 2013 davis cup europe / africa group ii } ; surface } } ; and { eq { hop { filter_eq { all_rows ; edition ; 2008 davis cup europe / africa group ii } ; surface } ; clay } ; eq { hop { filter_eq { all_rows ; edition ; 2013 davis cup europe / africa group ii } ; surface } ; clay } } }
for the crowd records of all rows , most of them are greater than or equal to 13000 .
Output: most_greater_eq { all_rows ; crowd ; 13000 }
select the rows whose partner record fuzzily matches to brian fairlie . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; partner ; brian fairlie } } ; 4 }
the average of the losses record of all rows is 10.94 .
Output: round_eq { avg { all_rows ; losses } ; 10.94 }
select the rows whose polling firm record fuzzily matches to environics . there is only one such row in the table . the date of polling record of this unqiue row is september 29 - october 4 , 2011 .
Output: and { only { filter_eq { all_rows ; polling firm ; environics } } ; eq { hop { filter_eq { all_rows ; polling firm ; environics } ; date of polling } ; september 29 - october 4 , 2011 } }
select the rows whose wins record is equal to 10 . there is only one such row in the table . the club record of this unqiue row is deportivo alavés .
Output: and { only { filter_eq { all_rows ; wins ; 10 } } ; eq { hop { filter_eq { all_rows ; wins ; 10 } ; club } ; deportivo alavés } }
select the rows whose rating record is less than 3 . there is only one such row in the table . the ride record of this unqiue row is taxi jam .
Output: and { only { filter_less { all_rows ; rating ; 3 } } ; eq { hop { filter_less { all_rows ; rating ; 3 } ; ride } ; taxi jam } }
select the rows whose result record fuzzily matches to w . among these rows , select the rows whose opponent record fuzzily matches to oakland raiders . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; result ; w } ; opponent ; oakland raiders } } ; 2 }
select the row whose goals against record of all rows is maximum . the team record of this row is montreal crystals .
Output: eq { hop { argmax { all_rows ; goals against } ; team } ; montreal crystals }
select the rows whose winner record fuzzily matches to john bowe . there is only one such row in the table . the race title record of this unqiue row is winton .
Output: and { only { filter_eq { all_rows ; winner ; john bowe } } ; eq { hop { filter_eq { all_rows ; winner ; john bowe } ; race title } ; winton } }
the average of the capacity record of all rows is 7000-7500 .
Output: round_eq { avg { all_rows ; capacity } ; 7000-7500 }
select the row whose original air date record of all rows is minimum . the nick prod record of this row is 1001 .
Output: eq { hop { argmin { all_rows ; original air date } ; nick prod } ; 1001 }
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 ) .
Output: 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 ) } } }
select the rows whose home team score record is greater than 10 . for the crowd records of these rows , most of them are less than 20000 .
Output: most_less { filter_greater { all_rows ; home team score ; 10 } ; crowd ; 20000 }
for the paper type records of all rows , most of them fuzzily match to tullis russell coatings .
Output: most_eq { all_rows ; paper type ; tullis russell coatings }
select the rows whose platform ( s ) record fuzzily matches to gamecube . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; platform ( s ) ; gamecube } } ; 5 }
select the rows whose venue record fuzzily matches to park . the maximum away team score record of these rows is 21.10 ( 136 ) .
Output: eq { max { filter_eq { all_rows ; venue ; park } ; away team score } ; 21.10 ( 136 ) }
select the rows whose city of license record fuzzily matches to syracuse , ny . there is only one such row in the table . the call sign record of this unqiue row is wmhr .
Output: and { only { filter_eq { all_rows ; city of license ; syracuse , ny } } ; eq { hop { filter_eq { all_rows ; city of license ; syracuse , ny } ; call sign } ; wmhr } }
select the row whose enrollment record of all rows is maximum . the institution record of this row is nova southeastern university .
Output: eq { hop { argmax { all_rows ; enrollment } ; institution } ; nova southeastern university }
for the type records of all rows , most of them fuzzily match to switcher .
Output: most_eq { all_rows ; type ; switcher }
select the rows whose winners record fuzzily matches to cliftonville fc . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; winners ; cliftonville fc } } ; 2 }
select the rows whose event record fuzzily matches to masters . take the 2012 - 13 record of this row . select the rows whose event record fuzzily matches to autumn gold . take the 2012 - 13 record of this row . the first record fuzzily matches to the second record .
Output: eq { hop { filter_eq { all_rows ; event ; masters } ; 2012 - 13 } ; hop { filter_eq { all_rows ; event ; autumn gold } ; 2012 - 13 } }
the sum of the mintage record of all rows is 418918 .
Output: round_eq { sum { all_rows ; mintage } ; 418918 }
select the rows whose manufacturer record fuzzily matches to ford . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; manufacturer ; ford } } ; 3 }
select the rows whose city of license record fuzzily matches to ga . among these rows , select the rows whose class record fuzzily matches to a . the number of such rows is 4 .
Output: eq { count { filter_eq { filter_eq { all_rows ; city of license ; ga } ; class ; a } } ; 4 }
select the rows whose date record is less than jun 27 . among these rows , select the rows whose set 5 record fuzzily matches to na . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_less { all_rows ; date ; jun 27 } ; set 5 ; na } } ; 2 }
select the rows whose model record fuzzily matches to nd4 . take the top speed ( in operation ) （ km / h ） record of this row . select the rows whose model record fuzzily matches to nd5 . take the top speed ( in operation ) （ km / h ） record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; model ; nd4 } ; top speed ( in operation ) （ km / h ） } ; hop { filter_eq { all_rows ; model ; nd5 } ; top speed ( in operation ) （ km / h ） } }
select the rows whose competition record fuzzily matches to friendly match . the minimum date record of these rows is 7 november 1997 .
Output: eq { min { filter_eq { all_rows ; competition ; friendly match } ; date } ; 7 november 1997 }
select the rows whose high points record fuzzily matches to richard jefferson . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; high points ; richard jefferson } } ; 5 }
select the rows whose location attendance record fuzzily matches to memorial coliseum . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; location attendance ; memorial coliseum } } ; 3 }
for the opponent records of all rows , all of them fuzzily match to angels .
Output: all_eq { all_rows ; opponent ; angels }
select the row whose central rate record of all rows is maximum . the currency record of this row is danish krone .
Output: eq { hop { argmax { all_rows ; central rate } ; currency } ; danish krone }
select the rows whose gold record is greater than 200 . for the total records of these rows , all of them are greater than 1000 .
Output: all_greater { filter_greater { all_rows ; gold ; 200 } ; total ; 1000 }
select the rows whose first broadcast record fuzzily matches to october . among these rows , select the rows whose seans team record fuzzily matches to joe wilkinson . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; first broadcast ; october } ; seans team ; joe wilkinson } } ; 2 }
for the us viewers ( million ) records of all rows , most of them are greater than 7 .
Output: most_greater { all_rows ; us viewers ( million ) ; 7 }
select the rows whose nat record fuzzily matches to ury . there is only one such row in the table . the name record of this unqiue row is garcía .
Output: and { only { filter_eq { all_rows ; nat ; ury } } ; eq { hop { filter_eq { all_rows ; nat ; ury } ; name } ; garcía } }
select the rows whose institution record fuzzily matches to south georgia technical college . take the founded record of this row . select the rows whose institution record fuzzily matches to southern crescent technical college . take the founded record of this row . the second record is 13 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; institution ; south georgia technical college } ; founded } ; hop { filter_eq { all_rows ; institution ; southern crescent technical college } ; founded } } ; -13 }
the average of the total points record of all rows is 962.2 .
Output: round_eq { avg { all_rows ; total points } ; 962.2 }
select the rows whose league record fuzzily matches to aba . there is only one such row in the table . the season record of this unqiue row is 2004 - 05 .
Output: and { only { filter_eq { all_rows ; league ; aba } } ; eq { hop { filter_eq { all_rows ; league ; aba } ; season } ; 2004 - 05 } }
select the rows whose gpu frequency record fuzzily matches to 320 mhz . for the release price ( usd ) records of these rows , all of them are greater than 60 .
Output: all_greater { filter_eq { all_rows ; gpu frequency ; 320 mhz } ; release price ( usd ) ; 60 }
select the rows whose winner record fuzzily matches to rangers . the maximum season record of these rows is 1944 - 45 .
Output: eq { max { filter_eq { all_rows ; winner ; rangers } ; season } ; 1944 - 45 }
select the row whose pieces record of all rows is 2nd maximum . the release record of this row is 1999 .
Output: eq { hop { nth_argmax { all_rows ; pieces ; 2 } ; release } ; 1999 }
select the row whose televote record of all rows is maximum . the song record of this row is dj , take me away .
Output: eq { hop { argmax { all_rows ; televote } ; song } ; dj , take me away }
select the row whose year established record of all rows is 2nd maximum . the members record of this row is university of otago .
Output: eq { hop { nth_argmax { all_rows ; year established ; 2 } ; members } ; university of otago }
select the rows whose apps record is equal to 38 . among these rows , select the rows whose points record is equal to 91 . there is only one such row in the table . the club record of this unqiue row is barcelona .
Output: and { only { filter_eq { filter_eq { all_rows ; apps ; 38 } ; points ; 91 } } ; eq { hop { filter_eq { filter_eq { all_rows ; apps ; 38 } ; points ; 91 } ; club } ; barcelona } }
the maximum total record of all rows is 45:40 .
Output: eq { max { all_rows ; total } ; 45:40 }
for the winner records of all rows , most of them fuzzily match to dick johnson .
Output: most_eq { all_rows ; winner ; dick johnson }
select the row whose date record of all rows is 1st minimum . the opponent record of this row is cincinnati bengals .
Output: eq { hop { nth_argmin { all_rows ; date ; 1 } ; opponent } ; cincinnati bengals }
the average of the population ( 2010 census ) record of all rows is 187514 .
Output: round_eq { avg { all_rows ; population ( 2010 census ) } ; 187514 }
select the rows whose date of polls record fuzzily matches to december . there is only one such row in the table . the state record of this unqiue row is mizoram .
Output: and { only { filter_eq { all_rows ; date of polls ; december } } ; eq { hop { filter_eq { all_rows ; date of polls ; december } ; state } ; mizoram } }
for the prothrombin time records of all rows , most of them fuzzily match to unaffected .
Output: most_eq { all_rows ; prothrombin time ; unaffected }
select the row whose total record of all rows is 2nd maximum . the nation record of this row is argentina .
Output: eq { hop { nth_argmax { all_rows ; total ; 2 } ; nation } ; argentina }
select the rows whose dates record is less than or equal to 2005 . select the row whose dates record of these rows is 1st minimum . the squadron record of this row is squadron 33 .
Output: eq { hop { nth_argmin { filter_less_eq { all_rows ; dates ; 2005 } ; dates ; 1 } ; squadron } ; squadron 33 }
select the row whose result record of all rows is 3rd minimum . the year record of this row is 2007 .
Output: eq { hop { nth_argmin { all_rows ; result ; 3 } ; year } ; 2007 }
select the rows whose original air date record fuzzily matches to april . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; original air date ; april } } ; 2 }
select the rows whose position record fuzzily matches to forward . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; position ; forward } } ; 3 }
select the rows whose season record is arbitrary . the number of such rows is 6 .
Output: eq { count { filter_all { all_rows ; season } } ; 6 }
select the row whose points 1 record of all rows is 2nd maximum . the team record of this row is eastwood hanley .
Output: eq { hop { nth_argmax { all_rows ; points 1 ; 2 } ; team } ; eastwood hanley }
the average of the total flights record of all rows is 16.63 .
Output: round_eq { avg { all_rows ; total flights } ; 16.63 }
select the rows whose nationality record is arbitrary . the number of such rows is 9 .
Output: eq { count { filter_all { all_rows ; nationality } } ; 9 }
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 .
Output: and { only { filter_not_eq { all_rows ; top - 10 ; 0 } } ; eq { hop { filter_not_eq { all_rows ; top - 10 ; 0 } ; tournament } ; the open championship } }
select the row whose rank record of all rows is 6th minimum . the name record of this row is emilio falla ( ecu ) .
Output: eq { hop { nth_argmin { all_rows ; rank ; 6 } ; name } ; emilio falla ( ecu ) }
select the rows whose origin record fuzzily matches to france . there is only one such row in the table . the aircraft record of this unqiue row is aerospatiale as 365 dauphin .
Output: and { only { filter_eq { all_rows ; origin ; france } } ; eq { hop { filter_eq { all_rows ; origin ; france } ; aircraft } ; aerospatiale as 365 dauphin } }
select the rows whose weight ( lb ) record is equal to 150 . there is only one such row in the table . the name record of this unqiue row is karon abraham .
Output: and { only { filter_eq { all_rows ; weight ( lb ) ; 150 } } ; eq { hop { filter_eq { all_rows ; weight ( lb ) ; 150 } ; name } ; karon abraham } }
select the row whose rec record of all rows is maximum . the player record of this row is chris cooley .
Output: eq { hop { argmax { all_rows ; rec } ; player } ; chris cooley }
select the rows whose type record fuzzily matches to village . select the row whose population ( 2011 ) record of these rows is maximum . the settlement record of this row is kać .
Output: eq { hop { argmax { filter_eq { all_rows ; type ; village } ; population ( 2011 ) } ; settlement } ; kać }
the average of the socialist record of all rows is 45 % .
Output: round_eq { avg { all_rows ; socialist } ; 45 % }
select the rows whose men 's singles record fuzzily matches to kenneth jonassen . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; men 's singles ; kenneth jonassen } } ; 2 }
select the rows whose name / designation record fuzzily matches to saturn machine pistol . take the year of intro record of this row . select the rows whose name / designation record fuzzily matches to serlea . take the year of intro record of this row . the second record is 5 years larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; name / designation ; saturn machine pistol } ; year of intro } ; hop { filter_eq { all_rows ; name / designation ; serlea } ; year of intro } } ; -5 years }
for the time of day records of all rows , most of them fuzzily match to night .
Output: most_eq { all_rows ; time of day ; night }
select the rows whose team record fuzzily matches to aa das palmeiras . take the drawn record of this row . select the rows whose team record fuzzily matches to minas gerais . take the drawn record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; team ; aa das palmeiras } ; drawn } ; hop { filter_eq { all_rows ; team ; minas gerais } ; drawn } }
select the rows whose 1973 democratic initial primary record fuzzily matches to mario biaggi . take the brooklyn record of this row . select the rows whose 1973 democratic initial primary record fuzzily matches to albert h blumenthal . take the brooklyn record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; 1973 democratic initial primary ; mario biaggi } ; brooklyn } ; hop { filter_eq { all_rows ; 1973 democratic initial primary ; albert h blumenthal } ; brooklyn } }
select the rows whose laps record is equal to 64 . among these rows , select the rows whose points record is greater than 30 . there is only one such row in the table . the driver record of this unqiue row is sébastien bourdais .
Output: and { only { filter_greater { filter_eq { all_rows ; laps ; 64 } ; points ; 30 } } ; eq { hop { filter_greater { filter_eq { all_rows ; laps ; 64 } ; points ; 30 } ; driver } ; sébastien bourdais } }
select the rows whose s wicket record is greater than 50 . there is only one such row in the table . the player record of this unqiue row is damien wright .
Output: and { only { filter_greater { all_rows ; s wicket ; 50 } } ; eq { hop { filter_greater { all_rows ; s wicket ; 50 } ; player } ; damien wright } }
select the rows whose position record fuzzily matches to qb . there is only one such row in the table . the cfl team record of this unqiue row is montreal alouettes .
Output: and { only { filter_eq { all_rows ; position ; qb } } ; eq { hop { filter_eq { all_rows ; position ; qb } ; cfl team } ; montreal alouettes } }
select the rows whose event record fuzzily matches to 2004 summer paralympics . take the total record of this row . select the rows whose event record fuzzily matches to 2008 summer paralympics . take the total record of this row . the first record is equal to the second record . the total record of the first row is 1 . the total record of the second row is 1 .
Output: and { eq { hop { filter_eq { all_rows ; event ; 2004 summer paralympics } ; total } ; hop { filter_eq { all_rows ; event ; 2008 summer paralympics } ; total } } ; and { eq { hop { filter_eq { all_rows ; event ; 2004 summer paralympics } ; total } ; 1 } ; eq { hop { filter_eq { all_rows ; event ; 2008 summer paralympics } ; total } ; 1 } } }
the average of the seed record of all rows is 13.16 .
Output: round_eq { avg { all_rows ; seed } ; 13.16 }
the sum of the points record of all rows is 14 .
Output: round_eq { sum { all_rows ; points } ; 14 }
the average of the votes record of all rows is 46.4 .
Output: round_eq { avg { all_rows ; votes } ; 46.4 }
select the rows whose date record fuzzily matches to 27 november 1997 . take the attendance record of this row . select the rows whose date record fuzzily matches to 10 december 1997 . take the attendance record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; 27 november 1997 } ; attendance } ; hop { filter_eq { all_rows ; date ; 10 december 1997 } ; attendance } }
the average of the gold record of all rows is .8 .
Output: round_eq { avg { all_rows ; gold } ; .8 }
select the row whose number of bearers 2009 record of all rows is 2nd maximum . the surname record of this row is nielsen . the number of bearers 1971 record of this row is 349.126 .
Output: and { eq { hop { nth_argmax { all_rows ; number of bearers 2009 ; 2 } ; surname } ; nielsen } ; eq { hop { nth_argmax { all_rows ; number of bearers 2009 ; 2 } ; number of bearers 1971 } ; 349.126 } }
select the row whose result f - a record of all rows is maximum . the opponents record of this row is dumbarton .
Output: eq { hop { argmax { all_rows ; result f - a } ; opponents } ; dumbarton }
select the rows whose champion record fuzzily matches to karrie webb . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; champion ; karrie webb } } ; 2 }
select the row whose average record of all rows is maximum . the professional partner record of this row is janja lesar .
Output: eq { hop { argmax { all_rows ; average } ; professional partner } ; janja lesar }
select the rows whose region record fuzzily matches to imereti . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; region ; imereti } } ; 8 }
select the rows whose l3 cache record fuzzily matches to 8 mb . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; l3 cache ; 8 mb } } ; 3 }
select the rows whose kiplinger 's top 100 values record fuzzily matches to n / a . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; kiplinger 's top 100 values ; n / a } } ; 6 }
select the rows whose points record is equal to 6 . for the lost records of these rows , most of them are equal to 6 .
Output: most_eq { filter_eq { all_rows ; points ; 6 } ; lost ; 6 }
the average of the crowd record of all rows is 23522 .
Output: round_eq { avg { all_rows ; crowd } ; 23522 }
select the row whose record record of all rows is 2nd minimum . the nationality record of this row is russia .
Output: eq { hop { nth_argmin { all_rows ; record ; 2 } ; nationality } ; russia }
select the row whose capacity record of all rows is maximum . the club record of this row is standard liège .
Output: eq { hop { argmax { all_rows ; capacity } ; club } ; standard liège }
select the row whose total record of all rows is 3rd maximum . the nation record of this row is mongolia .
Output: eq { hop { nth_argmax { all_rows ; total ; 3 } ; nation } ; mongolia }
for the matches records of all rows , most of them are less than 100 .
Output: most_less { all_rows ; matches ; 100 }
select the rows whose nation record fuzzily matches to kenya . among these rows , select the rows whose time record is less than 30:34 . the number of such rows is 2 .
Output: eq { count { filter_less { filter_eq { all_rows ; nation ; kenya } ; time ; 30:34 } } ; 2 }
select the rows whose location record fuzzily matches to liberty . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; location ; liberty } } ; 2 }
select the row whose duration record of all rows is 2nd minimum . the actor record of this row is nina soldano .
Output: eq { hop { nth_argmin { all_rows ; duration ; 2 } ; actor } ; nina soldano }
select the rows whose year joined record is equal to 1932 . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; year joined ; 1932 } } ; 7 }
select the row whose 2012 record of all rows is maximum . the location record of this row is lake buena vista , florida , usa .
Output: eq { hop { argmax { all_rows ; 2012 } ; location } ; lake buena vista , florida , usa }
select the rows whose champion record fuzzily matches to laura davies . select the row whose year record of these rows is 2nd minimum . the margin of victory record of this row is 4 strokes .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; champion ; laura davies } ; year ; 2 } ; margin of victory } ; 4 strokes }
the sum of the solo record of all rows is 153 .
Output: round_eq { sum { all_rows ; solo } ; 153 }
select the rows whose weeks on top record is equal to 2 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; weeks on top ; 2 } } ; 4 }
select the rows whose player record is arbitrary . the number of such rows is 17 .
Output: eq { count { filter_all { all_rows ; player } } ; 17 }
select the rows whose season record is arbitrary . the number of such rows is 5 .
Output: eq { count { filter_all { all_rows ; season } } ; 5 }
for the second quarter records of all rows , most of them are less than 250000 .
Output: most_less { all_rows ; second quarter ; 250000 }
select the rows whose date record is greater than april 8 . among these rows , select the rows whose attendance record is greater than 35000 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_greater { all_rows ; date ; april 8 } ; attendance ; 35000 } } ; 2 }
select the rows whose unit record fuzzily matches to novovoronezh - 2 . take the shutdown record of this row . select the rows whose unit record fuzzily matches to novovoronezh - 1 . take the shutdown record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; unit ; novovoronezh - 2 } ; shutdown } ; hop { filter_eq { all_rows ; unit ; novovoronezh - 1 } ; shutdown } }
select the row whose attendance record of all rows is maximum . the date record of this row is october 27 , 2002 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; october 27 , 2002 }
select the row whose max power record of all rows is minimum . the vehicle record of this row is nissan y11 ad van .
Output: eq { hop { argmin { all_rows ; max power } ; vehicle } ; nissan y11 ad van }
select the rows whose year record fuzzily matches to 1976 . take the points record of this row . select the rows whose year record fuzzily matches to 1975 . take the points record of this row . the first record is 6 points larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; year ; 1976 } ; points } ; hop { filter_eq { all_rows ; year ; 1975 } ; points } } ; 6 points }
for the date records of all rows , most of them are less than 1 september .
Output: most_less { all_rows ; date ; 1 september }
select the rows whose nationality record fuzzily matches to russian . there is only one such row in the table . the ship record of this unqiue row is isabel browne .
Output: and { only { filter_eq { all_rows ; nationality ; russian } } ; eq { hop { filter_eq { all_rows ; nationality ; russian } ; ship } ; isabel browne } }
select the rows whose result record fuzzily matches to loss . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; result ; loss } } ; 3 }
select the row whose silver record of all rows is maximum . the nation record of this row is united states ( usa ) .
Output: eq { hop { argmax { all_rows ; silver } ; nation } ; united states ( usa ) }
select the rows whose capacity record is less than 2000 . there is only one such row in the table . the city record of this unqiue row is scafati .
Output: and { only { filter_less { all_rows ; capacity ; 2000 } } ; eq { hop { filter_less { all_rows ; capacity ; 2000 } ; city } ; scafati } }
select the rows whose college record fuzzily matches to arizona state . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; college ; arizona state } } ; 2 }
select the row whose public vote record of all rows is maximum . the couple record of this row is coleen & stuart .
Output: eq { hop { argmax { all_rows ; public vote } ; couple } ; coleen & stuart }
for the begin records of all rows , most of them are less than 2009 .
Output: most_less { all_rows ; begin ; 2009 }
select the rows whose location attendance record fuzzily matches to air canada centre . among these rows , select the rows whose team record fuzzily matches to dallas . there is only one such row in the table . the date record of this unqiue row is february 6 .
Output: and { only { filter_eq { filter_eq { all_rows ; location attendance ; air canada centre } ; team ; dallas } } ; eq { hop { filter_eq { filter_eq { all_rows ; location attendance ; air canada centre } ; team ; dallas } ; date } ; february 6 } }
select the rows whose player record fuzzily matches to eric king . take the round record of this row . select the rows whose player record fuzzily matches to lionel gates . take the round record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; eric king } ; round } ; hop { filter_eq { all_rows ; player ; lionel gates } ; round } }
select the rows whose losses record is equal to 9 . there is only one such row in the table . the club record of this unqiue row is sestao .
Output: and { only { filter_eq { all_rows ; losses ; 9 } } ; eq { hop { filter_eq { all_rows ; losses ; 9 } ; club } ; sestao } }
for the erp kw records of all rows , most of them are equal to 60 .
Output: most_eq { all_rows ; erp kw ; 60 }
select the rows whose club record is arbitrary . the number of such rows is 20 .
Output: eq { count { filter_all { all_rows ; club } } ; 20 }
the average of the crowd record of all rows is 13333 .
Output: round_eq { avg { all_rows ; crowd } ; 13333 }
select the rows whose date record is equal to - . there is only one such row in the table . the week record of this unqiue row is 6 .
Output: and { only { filter_eq { all_rows ; date ; - } } ; eq { hop { filter_eq { all_rows ; date ; - } ; week } ; 6 } }
select the rows whose venue record fuzzily matches to aberdeen . there is only one such row in the table . the date record of this unqiue row is 5 december 1978 .
Output: and { only { filter_eq { all_rows ; venue ; aberdeen } } ; eq { hop { filter_eq { all_rows ; venue ; aberdeen } ; date } ; 5 december 1978 } }
select the rows whose top 10s record is equal to 0 . there is only one such row in the table . the year record of this unqiue row is 2007 .
Output: and { only { filter_eq { all_rows ; top 10s ; 0 } } ; eq { hop { filter_eq { all_rows ; top 10s ; 0 } ; year } ; 2007 } }
select the rows whose event record is less than 65 kg . for the quarterfinal records of these rows , most of them fuzzily match to did not advance .
Output: most_eq { filter_less { all_rows ; event ; 65 kg } ; quarterfinal ; did not advance }
select the rows whose date record fuzzily matches to 13 may 1998 . take the weight ( kg ) record of this row . select the rows whose date record fuzzily matches to 23 aug 1997 . take the weight ( kg ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; 13 may 1998 } ; weight ( kg ) } ; hop { filter_eq { all_rows ; date ; 23 aug 1997 } ; weight ( kg ) } }
select the rows whose attendance record is greater than 70000 . the number of such rows is 4 .
Output: eq { count { filter_greater { all_rows ; attendance ; 70000 } } ; 4 }
select the rows whose venue record fuzzily matches to nuremberg . there is only one such row in the table . the date record of this unqiue row is 3 june 2000 .
Output: and { only { filter_eq { all_rows ; venue ; nuremberg } } ; eq { hop { filter_eq { all_rows ; venue ; nuremberg } ; date } ; 3 june 2000 } }
the average of the points won record of all rows is 198 .
Output: round_eq { avg { all_rows ; points won } ; 198 }
select the rows whose owner record fuzzily matches to four roses thoroughbreds llc . there is only one such row in the table . the horse record of this unqiue row is anak nakal .
Output: and { only { filter_eq { all_rows ; owner ; four roses thoroughbreds llc } } ; eq { hop { filter_eq { all_rows ; owner ; four roses thoroughbreds llc } ; horse } ; anak nakal } }
select the rows whose round record fuzzily matches to 1 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; round ; 1 } } ; 4 }
the average of the high points record of all rows is 29.5 .
Output: round_eq { avg { all_rows ; high points } ; 29.5 }
select the rows whose player record fuzzily matches to jack kemp ( la ) . take the yards record of this row . select the rows whose player record fuzzily matches to tom flores ( oak ) . take the yards record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; jack kemp ( la ) } ; yards } ; hop { filter_eq { all_rows ; player ; tom flores ( oak ) } ; yards } }
for the date records of all rows , all of them fuzzily match to 10 july .
Output: all_eq { all_rows ; date ; 10 july }
the average of the crowd record of all rows is 12000 .
Output: round_eq { avg { all_rows ; crowd } ; 12000 }
the average of the episodes record of all rows is 38.167 .
Output: round_eq { avg { all_rows ; episodes } ; 38.167 }
select the rows whose leader at the summit record fuzzily matches to ( usa ) . there is only one such row in the table .
Output: only { filter_eq { all_rows ; leader at the summit ; ( usa ) } }
for the label records of all rows , most of them fuzzily match to parlophone .
Output: most_eq { all_rows ; label ; parlophone }
select the rows whose power ( kw ) record fuzzily matches to 5 kw . the number of such rows is 12 .
Output: eq { count { filter_eq { all_rows ; power ( kw ) ; 5 kw } } ; 12 }
select the rows whose title record fuzzily matches to family quarrel . take the air date record of this row . select the rows whose title record fuzzily matches to the award . take the air date record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; title ; family quarrel } ; air date } ; hop { filter_eq { all_rows ; title ; the award } ; air date } }
select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the track record of this unqiue row is little valley speedway .
Output: and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; track } ; little valley speedway } }
select the row whose attendance record of all rows is maximum . the opponent record of this row is denver broncos .
Output: eq { hop { argmax { all_rows ; attendance } ; opponent } ; denver broncos }
select the rows whose date record fuzzily matches to 12 november . the average of the crowd record of these rows is 2663 .
Output: round_eq { avg { filter_eq { all_rows ; date ; 12 november } ; crowd } ; 2663 }
select the rows whose ladder position record fuzzily matches to 9 / 16 . there is only one such row in the table . the competition record of this unqiue row is 2013 nrl season .
Output: and { only { filter_eq { all_rows ; ladder position ; 9 / 16 } } ; eq { hop { filter_eq { all_rows ; ladder position ; 9 / 16 } ; competition } ; 2013 nrl season } }
select the row whose elevated record of all rows is minimum . the elector record of this row is gregorio papareschi , canreglat .
Output: eq { hop { argmin { all_rows ; elevated } ; elector } ; gregorio papareschi , canreglat }
the average of the of candidates nominated record of all rows is 31.2 .
Output: round_eq { avg { all_rows ; of candidates nominated } ; 31.2 }
the sum of the to par record of all rows is -40 .
Output: round_eq { sum { all_rows ; to par } ; -40 }
the average of the duration record of all rows is 6 months 7 days .
Output: round_eq { avg { all_rows ; duration } ; 6 months 7 days }
select the row whose date record of all rows is 2nd minimum . the race name record of this row is iv glover trophy .
Output: eq { hop { nth_argmin { all_rows ; date ; 2 } ; race name } ; iv glover trophy }
select the row whose total poll ( % ) record of all rows is maximum . the constituency record of this row is oslo .
Output: eq { hop { argmax { all_rows ; total poll ( % ) } ; constituency } ; oslo }