niryuu/sni-each-converted huggingface dataset:
all_greater_eq { all_rows ; cuts made ; 1 } = true
Output: for the cuts made records of all rows , all of them are greater than or equal to 1 .
round_eq { avg { all_rows ; total } ; 51 } = true
Output: the average of the total record of all rows is 51 .
less { hop { filter_eq { all_rows ; candidate ; ron strynadka } ; votes } ; hop { filter_eq { all_rows ; candidate ; patricia cordner } ; votes } } = true
Output: select the rows whose candidate record fuzzily matches to ron strynadka . take the votes record of this row . select the rows whose candidate record fuzzily matches to patricia cordner . take the votes record of this row . the first record is less than the second record .
most_eq { filter_eq { all_rows ; position ; d } ; nationality ; canada } = true
Output: select the rows whose position record fuzzily matches to d . for the nationality records of these rows , most of them fuzzily match to canada .
round_eq { sum { all_rows ; won } ; 128 } = true
Output: the sum of the won record of all rows is 128 .
eq { count { filter_eq { all_rows ; result ; re-elected } } ; 4 } = true
Output: select the rows whose result record fuzzily matches to reelected . the number of such rows is 4 .
eq { count { filter_greater { filter_greater { all_rows ; equatorial bulge ; 1000 } ; polar diameter ; 100,000 } } ; 2 } = true
Output: select the rows whose equatorial bulge record is greater than 1000 . among these rows , select the rows whose polar diameter record is greater than 100,000 . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; location ; briggs stadium } } ; 3 } = true
Output: select the rows whose location record fuzzily matches to briggs stadium . the number of such rows is 3 .
round_eq { avg { all_rows ; erp w } ; 66 } = true
Output: the average of the erp w record of all rows is 66 .
round_eq { avg { all_rows ; matches } ; 51 } = true
Output: the average of the matches record of all rows is 51 .
greater { hop { filter_eq { all_rows ; name ( year commissioned ) ; goldfields gas transmission pipeline ( 1996 ) } ; maximum diameter } ; hop { filter_eq { all_rows ; name ( year commissioned ) ; mid west gas pipeline ( 1999 ) } ; maximum diameter } } = true
Output: select the rows whose name  year commissioned  record fuzzily matches to goldfields gas transmission pipeline  1996  . take the maximum diameter record of this row . select the rows whose name  year commissioned  record fuzzily matches to mid west gas pipeline  1999  . take the maximum diameter record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; team ; toyota racing } } ; 2 } = true
Output: select the rows whose team record fuzzily matches to toyota racing . the number of such rows is 2 .
most_greater { all_rows ; year ; 2000 } = true
Output: for the year records of all rows , most of them are greater than 2000 .
eq { count { filter_eq { all_rows ; fate ; damaged } } ; 2 } = true
Output: select the rows whose fate record fuzzily matches to damaged . the number of such rows is 2 .
eq { hop { argmax { all_rows ; winnings } ; year } ; 1998 } = true
Output: select the row whose winnings record of all rows is maximum . the year record of this row is 1998 .
eq { diff { hop { filter_eq { all_rows ; year ; 1994 } ; wins } ; hop { filter_eq { all_rows ; year ; 1996 } ; wins } } ; 1 } = true
Output: select the rows whose year record fuzzily matches to 1994 . take the wins record of this row . select the rows whose year record fuzzily matches to 1996 . take the wins record of this row . the first record is 1 larger than the second record .
greater { hop { filter_eq { all_rows ; product ; grain } ; 2001 } ; hop { filter_eq { all_rows ; product ; timber } ; 2001 } } = true
Output: select the rows whose product record fuzzily matches to grain . take the 2001 record of this row . select the rows whose product record fuzzily matches to timber . take the 2001 record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; census ranking } ; official name } ; hampstead } = true
Output: select the row whose census ranking record of all rows is maximum . the official name record of this row is hampstead .
and { only { filter_eq { all_rows ; class ; c1 } } ; eq { hop { filter_eq { all_rows ; class ; c1 } ; year } ; 1992 } } = true
Output: select the rows whose class record fuzzily matches to c1 . there is only one such row in the table . the year record of this unqiue row is 1992 .
round_eq { avg { all_rows ; rank } ; 12.56 } = true
Output: the average of the rank record of all rows is 12.56 .
eq { count { filter_greater_eq { all_rows ; goals for ; 50 } } ; 7 } = true
Output: select the rows whose goals for record is greater than or equal to 50 . the number of such rows is 7 .
eq { hop { nth_argmax { all_rows ; car ; 2 } ; player } ; john ritcher } = true
Output: select the row whose car record of all rows is 2nd maximum . the player record of this row is john ritcher .
eq { hop { argmin { all_rows ; pick } ; player } ; mike gyetvai } = true
Output: select the row whose pick record of all rows is minimum . the player record of this row is mike gyetvai .
round_eq { avg { all_rows ; length } ; 3:43 } = true
Output: the average of the length record of all rows is 3:43 .
eq { count { filter_eq { all_rows ; colors ; white } } ; 3 } = true
Output: select the rows whose colors record fuzzily matches to white . the number of such rows is 3 .
eq { count { filter_all { all_rows ; player } } ; 10 } = true
Output: select the rows whose player record is arbitrary . the number of such rows is 10 .
eq { hop { argmax { all_rows ; obama % } ; county } ; essex } = true
Output: select the row whose obama  record of all rows is maximum . the county record of this row is essex .
all_eq { all_rows ; driver ; alessandro pier guidi } = true
Output: for the driver records of all rows , all of them fuzzily match to alessandro pier guidi .
round_eq { avg { all_rows ; crowd } ; 24000 } = true
Output: the average of the crowd record of all rows is 24000 .
eq { hop { argmax { all_rows ; points } ; driver } ; michael schumacher } = true
Output: select the row whose points record of all rows is maximum . the driver record of this row is michael schumacher .
and { only { filter_eq { all_rows ; director ( s ) ; gary ross } } ; eq { hop { filter_eq { all_rows ; director ( s ) ; gary ross } ; title } ; the hunger games } } = true
Output: select the rows whose director  s  record fuzzily matches to gary ross . there is only one such row in the table . the title record of this unqiue row is the hunger games .
eq { max { filter_eq { all_rows ; chairman ; j brayshaw } ; position } ; 13th } = true
Output: select the rows whose chairman record fuzzily matches to j brayshaw . the maximum position record of these rows is 13th .
all_eq { all_rows ; country ; soviet union } = true
Output: for the country records of all rows , all of them fuzzily match to soviet union .
all_eq { all_rows ; distance ( miles ) ; 1-3 / 16 } = true
Output: for the distance  miles  records of all rows , all of them fuzzily match to 13  16 .
round_eq { sum { filter_greater_eq { all_rows ; year ; 2011 } ; tournaments played } ; 33 } = true
Output: select the rows whose year record is greater than or equal to 2011 . the sum of the tournaments played record of these rows is 33 .
less { hop { filter_eq { all_rows ; developer ( s ) ; ea games } ; release date } ; hop { filter_eq { all_rows ; developer ( s ) ; masthead studios } ; release date } } = true
Output: select the rows whose developer  s  record fuzzily matches to ea games . take the release date record of this row . select the rows whose developer  s  record fuzzily matches to masthead studios . take the release date record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; school / club team ; duke } } ; eq { hop { filter_eq { all_rows ; school / club team ; duke } ; player } ; antonio lang } } = true
Output: select the rows whose school  club team record fuzzily matches to duke . there is only one such row in the table . the player record of this unqiue row is antonio lang .
eq { count { filter_eq { all_rows ; defensive ; matt disher } } ; 3 } = true
Output: select the rows whose defensive record fuzzily matches to matt disher . the number of such rows is 3 .
most_eq { all_rows ; website ; english } = true
Output: for the website records of all rows , most of them fuzzily match to english .
eq { hop { nth_argmin { all_rows ; - ; 2 } ; team } ; rahal letterman lanigan racing } = true
Output: select the row whose  record of all rows is 2nd minimum . the team record of this row is rahal letterman lanigan racing .
eq { count { filter_eq { all_rows ; number of fixtures ; 16 } } ; 2 } = true
Output: select the rows whose number of fixtures record fuzzily matches to 16 . the number of such rows is 2 .
only { filter_eq { all_rows ; start date / time ; july } } = true
Output: select the rows whose start date  time record fuzzily matches to july . there is only one such row in the table .
most_eq { all_rows ; event ; 110 m hurdles } = true
Output: for the event records of all rows , most of them fuzzily match to 110 m hurdles .
and { only { filter_eq { all_rows ; chassis ; lola t370 } } ; eq { hop { filter_eq { all_rows ; chassis ; lola t370 } ; year } ; 1974 } } = true
Output: select the rows whose chassis record fuzzily matches to lola t370 . there is only one such row in the table . the year record of this unqiue row is 1974 .
eq { count { filter_eq { all_rows ; original air date ; april } } ; 2 } = true
Output: select the rows whose original air date record fuzzily matches to april . the number of such rows is 2 .
eq { hop { argmin { all_rows ; attendance } ; home } ; ny islanders } = true
Output: select the row whose attendance record of all rows is minimum . the home record of this row is ny islanders .
eq { hop { nth_argmin { all_rows ; date ; 1 } ; opposing team } ; wellington } = true
Output: select the row whose date record of all rows is 1st minimum . the opposing team record of this row is wellington .
eq { count { filter_eq { all_rows ; time ; am } } ; 0 } = true
Output: select the rows whose time record fuzzily matches to am . the number of such rows is 0 .
round_eq { avg { filter_eq { all_rows ; country ; united states } ; score } ; 66 } = true
Output: select the rows whose country record fuzzily matches to united states . the average of the score record of these rows is 66 .
eq { diff { hop { filter_eq { all_rows ; incumbent ; clifton a woodrum } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; joseph whitehead } ; first elected } } ; -2 years } = true
Output: select the rows whose incumbent record fuzzily matches to clifton a woodrum . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to joseph whitehead . take the first elected record of this row . the second record is 2 years larger than the first record .
eq { count { filter_less { filter_less { all_rows ; matches ; 400 } ; goals ; 150 } } ; 6 } = true
Output: select the rows whose matches record is less than 400 . among these rows , select the rows whose goals record is less than 150 . the number of such rows is 6 .
round_eq { avg { all_rows ; crowd } ; 21221 } = true
Output: the average of the crowd record of all rows is 21221 .
most_eq { all_rows ; district ; belize } = true
Output: for the district records of all rows , most of them fuzzily match to belize .
eq { hop { nth_argmax { all_rows ; events ; 2 } ; tournament } ; pga championship } = true
Output: select the row whose events record of all rows is 2nd maximum . the tournament record of this row is pga championship .
round_eq { sum { all_rows ; top - 25 } ; 48 } = true
Output: the sum of the top  25 record of all rows is 48 .
eq { count { filter_eq { filter_eq { all_rows ; region ; hoseo } ; korean dialect ; chungcheong dialect } } ; 1 } = true
Output: select the rows whose region record fuzzily matches to hoseo . among these rows , select the rows whose korean dialect record fuzzily matches to chungcheong dialect . the number of such rows is 1 .
and { only { filter_eq { all_rows ; score ; 5-2 } } ; eq { hop { filter_eq { all_rows ; score ; 5-2 } ; opponent } ; boston bruins } } = true
Output: select the rows whose score record fuzzily matches to 52 . there is only one such row in the table . the opponent record of this unqiue row is boston bruins .
eq { hop { nth_argmax { all_rows ; goals for ; 2 } ; club } ; barcelona b } = true
Output: select the row whose goals for record of all rows is 2nd maximum . the club record of this row is barcelona b .
all_eq { all_rows ; date ; february } = true
Output: for the date records of all rows , all of them fuzzily match to february .
most_greater_eq { all_rows ; suites ; 125 } = true
Output: for the suites records of all rows , most of them are greater than or equal to 125 .
most_greater_eq { all_rows ; number of electorates ( 2009 ) ; 140000 } = true
Output: for the number of electorates  2009  records of all rows , most of them are greater than or equal to 140000 .
and { eq { max { all_rows ; top division titles } ; 41 } ; eq { hop { argmax { all_rows ; top division titles } ; club } ; south china } } = true
Output: the maximum top division titles record of all rows is 41 . the club record of the row with superlative top division titles record is south china .
eq { count { filter_eq { all_rows ; opponent ; brooklyn dodgers } } ; 2 } = true
Output: select the rows whose opponent record fuzzily matches to brooklyn dodgers . the number of such rows is 2 .
less { hop { filter_eq { all_rows ; sportsperson ; albert pujols } ; year } ; hop { filter_eq { all_rows ; sportsperson ; usain bolt } ; year } } = true
Output: select the rows whose sportsperson record fuzzily matches to albert pujols . take the year record of this row . select the rows whose sportsperson record fuzzily matches to usain bolt . take the year record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; silver ; 3 } } ; 2 } = true
Output: select the rows whose silver record is equal to 3 . the number of such rows is 2 .
most_greater { filter_eq { all_rows ; show ; dynamo : magician impossible } ; number of viewers ; 1000000 } = true
Output: select the rows whose show record fuzzily matches to dynamo : magician impossible . for the number of viewers records of these rows , most of them are greater than 1000000 .
and { only { filter_eq { all_rows ; college ; acadia } } ; eq { hop { filter_eq { all_rows ; college ; acadia } ; player } ; karl ortmanns } } = true
Output: select the rows whose college record fuzzily matches to acadia . there is only one such row in the table . the player record of this unqiue row is karl ortmanns .
eq { hop { argmax { all_rows ; gold } ; gold } ; 36 } = true
Output: select the row whose gold record of all rows is maximum . the gold record of this row is 36 .
eq { count { filter_eq { all_rows ; time / retired ; + 6 laps } } ; 3 } = true
Output: select the rows whose time  retired record fuzzily matches to + 6 laps . the number of such rows is 3 .
eq { hop { nth_argmin { all_rows ; date ; 2 } ; surface } ; clay } = true
Output: select the row whose date record of all rows is 2nd minimum . the surface record of this row is clay .
eq { hop { nth_argmax { all_rows ; height ; 2 } ; player } ; argo meresaar ( c ) } = true
Output: select the row whose height record of all rows is 2nd maximum . the player record of this row is argo meresaar  c  .
eq { count { filter_greater { all_rows ; run time ; 6:00 } } ; 3 } = true
Output: select the rows whose run time record is greater than 6:00 . the number of such rows is 3 .
eq { count { filter_not_eq { all_rows ; team ; n/a } } ; 6 } = true
Output: select the rows whose team record does not match to na . the number of such rows is 6 .
and { only { filter_eq { all_rows ; result ; injured } } ; eq { hop { filter_eq { all_rows ; result ; injured } ; week } ; 5 } } = true
Output: select the rows whose result record fuzzily matches to injured . there is only one such row in the table . the week record of this unqiue row is 5 .
round_eq { avg { filter_eq { all_rows ; manufacturer ; suzuki } ; laps } ; 21.5 } = true
Output: select the rows whose manufacturer record fuzzily matches to suzuki . the average of the laps record of these rows is 21.5 .
round_eq { sum { all_rows ; runs } ; 1974 } = true
Output: the sum of the runs record of all rows is 1974 .
round_eq { avg { all_rows ; mark } ; 47.05 } = true
Output: the average of the mark record of all rows is 47.05 .
round_eq { sum { all_rows ; score } ; 1554 } = true
Output: the sum of the score record of all rows is 1554 .
eq { count { filter_eq { all_rows ; event ; ifc wc 13 - warriors challenge } } ; 2 } = true
Output: select the rows whose event record fuzzily matches to ifc wc 13  warriors challenge . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; date made ; 1915 } } ; 3 } = true
Output: select the rows whose date made record is equal to 1915 . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; date } ; saturday , april 30 } = true
Output: select the row whose attendance record of all rows is 3rd maximum . the date record of this row is saturday , april 30 .
round_eq { avg { all_rows ; bronze } ; 1.67 } = true
Output: the average of the bronze record of all rows is 1.67 .
all_eq { all_rows ; 2008 status ; re-election } = true
Output: for the 2008 status records of all rows , all of them fuzzily match to reelection .
and { only { filter_eq { all_rows ; fcc info ; fcc } } ; eq { hop { filter_eq { all_rows ; fcc info ; fcc } ; call sign } ; k297au } } = true
Output: select the rows whose fcc info record fuzzily matches to fcc . there is only one such row in the table . the call sign record of this unqiue row is k297au .
and { only { filter_eq { all_rows ; aspect ; 16:9 } } ; eq { hop { filter_eq { all_rows ; aspect ; 16:9 } ; channel } ; 26.1 } } = true
Output: 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 .
all_eq { all_rows ; played ; 14 } = true
Output: for the played records of all rows , all of them are equal to 14 .
eq { max { all_rows ; took office } ; 27 july 2011 } = true
Output: the maximum took office record of all rows is 27 july 2011 .
eq { hop { nth_argmin { all_rows ; competition ; 1 } ; date } ; 29 october 1975 } = true
Output: select the row whose competition record of all rows is 1st minimum . the date record of this row is 29 october 1975 .
eq { count { filter_greater { all_rows ; percentage ; 3 % } } ; 1 } = true
Output: select the rows whose percentage record is greater than 3  . the number of such rows is 1 .
eq { hop { nth_argmax { all_rows ; pts ; 4 } ; year } ; 1986 } = true
Output: select the row whose pts record of all rows is 4th maximum . the year record of this row is 1986 .
greater { hop { filter_eq { all_rows ; class ; f - 20 } ; quantity made } ; hop { filter_eq { all_rows ; class ; f - 22 } ; quantity made } } = true
Output: select the rows whose class record fuzzily matches to f  20 . take the quantity made record of this row . select the rows whose class record fuzzily matches to f  22 . take the quantity made record of this row . the first record is greater than the second record .
most_eq { all_rows ; manner of departure ; sacked } = true
Output: for the manner of departure records of all rows , most of them fuzzily match to sacked .
eq { count { filter_eq { all_rows ; tie no ; replay } } ; 2 } = true
Output: select the rows whose tie no record fuzzily matches to replay . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; score ; 2 } ; player } ; mark boucher } = true
Output: select the row whose score record of all rows is 2nd maximum . the player record of this row is mark boucher .
most_eq { all_rows ; content ; televendita } = true
Output: for the content records of all rows , most of them fuzzily match to televendita .
most_not_eq { all_rows ; primary sponsor ( s ) ; n / a } = true
Output: for the primary sponsor  s  records of all rows , most of them do not match to n  a .
greater { hop { filter_eq { all_rows ; date ; november 10 , 1975 } ; attendance } ; hop { filter_eq { all_rows ; date ; december 7 , 1975 } ; attendance } } = true
Output: select the rows whose date record fuzzily matches to november 10 , 1975 . take the attendance record of this row . select the rows whose date record fuzzily matches to december 7 , 1975 . take the attendance record of this row . the first record is greater than the second record .
most_not_eq { all_rows ; region 4 ; n / a } = true
Output: for the region 4 records of all rows , most of them do not match to n  a .
eq { hop { argmin { filter_eq { all_rows ; no of series ; 3 } ; episodes } ; actor } ; ellen thomas } = true
Output: select the rows whose no of series record is equal to 3 . select the row whose episodes record of these rows is minimum . the actor record of this row is ellen thomas .
eq { count { filter_eq { all_rows ; status ; tour match } } ; 5 } = true
Output: select the rows whose status record fuzzily matches to tour match . the number of such rows is 5 .
and { only { filter_eq { all_rows ; weight ( lb ) ; 150 } } ; eq { hop { filter_eq { all_rows ; weight ( lb ) ; 150 } ; name } ; karon abraham } } = true
Output: 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 .
eq { count { filter_eq { all_rows ; city ; london } } ; 2 } = true
Output: select the rows whose city record fuzzily matches to london . the number of such rows is 2 .
round_eq { avg { all_rows ; results } ; 66 % } = true
Output: the average of the results record of all rows is 66  .
and { only { filter_eq { all_rows ; lost ; 2 } } ; eq { hop { filter_eq { all_rows ; lost ; 2 } ; club } ; tumble rfc } } = true
Output: select the rows whose lost record is equal to 2 . there is only one such row in the table . the club record of this unqiue row is tumble rfc .
and { eq { nth_min { all_rows ; date ; 2 } ; 22 february 1998 } ; eq { hop { nth_argmin { all_rows ; date ; 2 } ; tournament } ; hannover , germany } } = true
Output: the 2nd minimum date record of all rows is 22 february 1998 . the tournament record of the row with 2nd minimum date record is hannover , germany .
round_eq { sum { filter_eq { all_rows ; country ; russia } ; uci points } ; 90 } = true
Output: select the rows whose country record fuzzily matches to russia . the sum of the uci points record of these rows is 90 .
eq { hop { argmax { all_rows ; number } ; unit numbers } ; 254001 - 254032 } = true
Output: select the row whose number record of all rows is maximum . the unit numbers record of this row is 254001  254032 .
round_eq { sum { filter_eq { all_rows ; season ; 2012 } ; points } ; 40 } = true
Output: select the rows whose season record is equal to 2012 . the sum of the points record of these rows is 40 .
and { only { filter_eq { all_rows ; original west end performer ; n / a } } ; eq { hop { filter_eq { all_rows ; original west end performer ; n / a } ; character } ; ben weatherstaff } } = true
Output: select the rows whose original west end performer record fuzzily matches to n  a . there is only one such row in the table . the character record of this unqiue row is ben weatherstaff .
greater { hop { filter_eq { all_rows ; date ; september 25 } ; attendance } ; hop { filter_eq { all_rows ; date ; september 18 } ; attendance } } = true
Output: select the rows whose date record fuzzily matches to september 25 . take the attendance record of this row . select the rows whose date record fuzzily matches to september 18 . take the attendance record of this row . the first record is greater than the second record .
only { filter_eq { all_rows ; event ; metallica } } = true
Output: select the rows whose event record fuzzily matches to metallica . there is only one such row in the table .
eq { hop { argmax { all_rows ; silver } ; nation } ; japan } = true
Output: select the row whose silver record of all rows is maximum . the nation record of this row is japan .
eq { count { filter_eq { all_rows ; first elected ; 1974 } } ; 2 } = true
Output: select the rows whose first elected record is equal to 1974 . the number of such rows is 2 .
most_greater_eq { filter_eq { all_rows ; date ; december } ; final score ; 20 for both teams } = true
Output: select the rows whose date record fuzzily matches to december . for the final score records of these rows , most of them are greater than or equal to 20 for both teams .
round_eq { sum { all_rows ; against } ; 119 } = true
Output: the sum of the against record of all rows is 119 .
round_eq { avg { all_rows ; score } ; 71 } = true
Output: the average of the score record of all rows is 71 .
eq { hop { argmin { all_rows ; regular season 1 } ; season } ; 2004 - 05 } = true
Output: select the row whose regular season 1 record of all rows is minimum . the season record of this row is 2004  05 .
round_eq { avg { all_rows ; time } ; 6:43.99 } = true
Output: the average of the time record of all rows is 6:43.99 .
eq { hop { argmax { all_rows ; 1st leg } ; team 2 } ; al - sekka al - hadid } = true
Output: select the row whose 1st leg record of all rows is maximum . the team 2 record of this row is al  sekka al  hadid .
eq { count { filter_eq { all_rows ; bronze ; 2 } } ; 5 } = true
Output: select the rows whose bronze record is equal to 2 . the number of such rows is 5 .
all_eq { all_rows ; date ; march } = true
Output: for the date records of all rows , all of them fuzzily match to march .
eq { hop { nth_argmin { all_rows ; time ; 2 } ; event } ; gladiator challenge 87 : collision course } = true
Output: select the row whose time record of all rows is 2nd minimum . the event record of this row is gladiator challenge 87 : collision course .
round_eq { sum { all_rows ; points } ; 473 } = true
Output: the sum of the points record of all rows is 473 .
eq { count { filter_eq { all_rows ; segment a ; plastic } } ; 3 } = true
Output: select the rows whose segment a record fuzzily matches to plastic . the number of such rows is 3 .
and { only { filter_not_eq { all_rows ; award ; saturn award } } ; and { eq { hop { filter_not_eq { all_rows ; award ; saturn award } ; year } ; 2010 } ; eq { hop { filter_not_eq { all_rows ; award ; saturn award } ; result } ; nominated } } } = true
Output: select the rows whose award record does not match to saturn award . there is only one such row in the table . the year record of this unqiue row is 2010 . the result record of this unqiue row is nominated .
eq { diff { hop { filter_eq { all_rows ; venue ; athens , greece } ; year } ; hop { filter_eq { all_rows ; venue ; helsinki , finland } ; year } } ; -1 } = true
Output: select the rows whose venue record fuzzily matches to athens , greece . take the year record of this row . select the rows whose venue record fuzzily matches to helsinki , finland . take the year record of this row . the second record is 1 larger than the first record .
round_eq { avg { all_rows ; blackberry } ; 3.19 % } = true
Output: the average of the blackberry record of all rows is 3.19  .
and { only { filter_eq { all_rows ; date ; 1939 } } ; eq { hop { filter_eq { all_rows ; date ; 1939 } ; name } ; garrett } } = true
Output: select the rows whose date record is equal to 1939 . there is only one such row in the table . the name record of this unqiue row is garrett .
eq { count { filter_eq { all_rows ; cores ; 6 } } ; 2 } = true
Output: select the rows whose cores record is equal to 6 . the number of such rows is 2 .
and { only { filter_eq { filter_eq { all_rows ; constructor ; cooper - climax } ; circuit ; goodwood } } ; eq { hop { filter_eq { filter_eq { all_rows ; constructor ; cooper - climax } ; circuit ; goodwood } ; date } ; 30 march } } = true
Output: select the rows whose constructor record fuzzily matches to cooper  climax . among these rows , select the rows whose circuit record fuzzily matches to goodwood . there is only one such row in the table . the date record of this unqiue row is 30 march .
most_eq { all_rows ; competition ; league } = true
Output: for the competition records of all rows , most of them fuzzily match to league .
most_eq { all_rows ; high points ; lennox } = true
Output: for the high points records of all rows , most of them fuzzily match to lennox .
eq { hop { argmax { all_rows ; matches } ; year } ; 2012 } = true
Output: select the row whose matches record of all rows is maximum . the year record of this row is 2012 .
and { only { filter_eq { all_rows ; recipient ; debby ryan } } ; and { eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; year } ; 2010 } ; and { eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; award } ; hollywood teen tv awards } ; eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; result } ; nominated } } } } = true
Output: select the rows whose recipient record fuzzily matches to debby ryan . there is only one such row in the table . the year record of this unqiue row is 2010 . the award record of this unqiue row is hollywood teen tv awards . the result record of this unqiue row is nominated .
eq { hop { nth_argmin { filter_eq { all_rows ; location ; launceston , tasmania } ; years ; 1 } ; original code } ; victorian rules } = true
Output: select the rows whose location record fuzzily matches to launceston , tasmania . select the row whose years record of these rows is 1st minimum . the original code record of this row is victorian rules .
and { only { filter_eq { all_rows ; college ; clark university } } ; eq { hop { filter_eq { all_rows ; college ; clark university } ; player } ; millard hamilton } } = true
Output: select the rows whose college record fuzzily matches to clark university . there is only one such row in the table . the player record of this unqiue row is millard hamilton .
eq { count { filter_all { all_rows ; club } } ; 20 } = true
Output: select the rows whose club record is arbitrary . the number of such rows is 20 .
all_eq { all_rows ; nation ; usa } = true
Output: for the nation records of all rows , all of them fuzzily match to usa .
most_eq { all_rows ; first issue ; august 2008 } = true
Output: for the first issue records of all rows , most of them fuzzily match to august 2008 .
eq { hop { nth_argmax { all_rows ; max pressure ; 4 } ; cartridge } ; .38 super } = true
Output: select the row whose max pressure record of all rows is 4th maximum . the cartridge record of this row is .38 super .
and { only { filter_eq { all_rows ; team ; 499cc matchless g50 } } ; eq { hop { filter_eq { all_rows ; team ; 499cc matchless g50 } ; rider } ; mark herbertson } } = true
Output: select the rows whose team record fuzzily matches to 499cc matchless g50 . there is only one such row in the table . the rider record of this unqiue row is mark herbertson .
eq { hop { nth_argmax { all_rows ; votes ; 3 } ; candidate 's name } ; michael mackay } = true
Output: select the row whose votes record of all rows is 3rd maximum . the candidate s name record of this row is michael mackay .
and { eq { nth_max { all_rows ; margin ; 1 } ; 66 } ; eq { hop { nth_argmax { all_rows ; margin ; 1 } ; opponent } ; south sydney rabbitohs } } = true
Output: the 1st maximum margin record of all rows is 66 . the opponent record of the row with 1st maximum margin record is south sydney rabbitohs .
most_eq { all_rows ; competition ; league cup } = true
Output: for the competition records of all rows , most of them fuzzily match to league cup .
eq { count { filter_eq { all_rows ; frequency ; 150 mhz } } ; 1 } = true
Output: select the rows whose frequency record fuzzily matches to 150 mhz . the number of such rows is 1 .
and { only { filter_eq { all_rows ; time / retired ; oil leak } } ; eq { hop { filter_eq { all_rows ; time / retired ; oil leak } ; driver } ; jack brabham } } = true
Output: select the rows whose time  retired record fuzzily matches to oil leak . there is only one such row in the table . the driver record of this unqiue row is jack brabham .
greater { hop { filter_eq { all_rows ; institution ; boise state university } ; enrollment } ; hop { filter_eq { all_rows ; institution ; california state university , bakersfield } ; enrollment } } = true
Output: select the rows whose institution record fuzzily matches to boise state university . take the enrollment record of this row . select the rows whose institution record fuzzily matches to california state university , bakersfield . take the enrollment record of this row . the first record is greater than the second record .
eq { min { filter_greater { all_rows ; tournaments played ; 15 } ; cuts made } ; 12 } = true
Output: select the rows whose tournaments played record is greater than 15 . the minimum cuts made record of these rows is 12 .
eq { count { filter_eq { all_rows ; air date ; 2008 } } ; 11 } = true
Output: select the rows whose air date record fuzzily matches to 2008 . the number of such rows is 11 .
round_eq { avg { all_rows ; goals } ; 120.8 } = true
Output: the average of the goals record of all rows is 120.8 .
eq { count { filter_eq { all_rows ; crowd ; 15000 } } ; 3 } = true
Output: select the rows whose crowd record is equal to 15000 . the number of such rows is 3 .
and { only { filter_eq { all_rows ; host location ; plainview , tx } } ; eq { hop { filter_eq { all_rows ; host location ; plainview , tx } ; year } ; 1952 } } = true
Output: select the rows whose host location record fuzzily matches to plainview , tx . there is only one such row in the table . the year record of this unqiue row is 1952 .
round_eq { avg { all_rows ; number of contestants } ; 11.4 } = true
Output: the average of the number of contestants record of all rows is 11.4 .
eq { hop { argmax { all_rows ; away } ; season } ; 2001 - 02 } = true
Output: select the row whose away record of all rows is maximum . the season record of this row is 2001  02 .
and { eq { nth_min { filter_greater { all_rows ; total ( kg ) ; 195.0 } ; bodyweight ; 1 } ; 57.05 } ; eq { hop { nth_argmin { filter_greater { all_rows ; total ( kg ) ; 195.0 } ; bodyweight ; 1 } ; name } ; rusmeris villar ( col ) } } = true
Output: select the rows whose total  kg  record is greater than 195.0 . the 1st minimum bodyweight record of these rows is 57.05 . the name record of the row with 1st minimum bodyweight record is rusmeris villar  col  .
eq { hop { nth_argmin { all_rows ; episode ; 1 } ; original airdate ( uk ) } ; 26 march 2005 } = true
Output: select the row whose episode record of all rows is 1st minimum . the original airdate  uk  record of this row is 26 march 2005 .
eq { hop { nth_argmax { all_rows ; silver ; 1 } ; nation } ; france } = true
Output: select the row whose silver record of all rows is 1st maximum . the nation record of this row is france .
and { only { filter_less { all_rows ; points ; 40 } } ; eq { hop { filter_less { all_rows ; points ; 40 } ; song } ; cold shoulder } } = true
Output: select the rows whose points record is less than 40 . there is only one such row in the table . the song record of this unqiue row is cold shoulder .
most_eq { all_rows ; of seats won ; 0 } = true
Output: for the of seats won records of all rows , most of them are equal to 0 .
less { hop { filter_eq { all_rows ; name ; sally foster } ; time } ; hop { filter_eq { all_rows ; name ; anne poleska } ; time } } = true
Output: select the rows whose name record fuzzily matches to sally foster . take the time record of this row . select the rows whose name record fuzzily matches to anne poleska . take the time record of this row . the first record is less than the second record .
eq { count { filter_eq { filter_eq { all_rows ; gold ; 0 } ; total ; 1 } } ; 2 } = true
Output: select the rows whose gold record is equal to 0 . among these rows , select the rows whose total record is equal to 1 . the number of such rows is 2 .
and { only { filter_eq { all_rows ; points ; 6-0 } } ; eq { hop { filter_eq { all_rows ; points ; 6-0 } ; team 1 } ; fluminense } } = true
Output: select the rows whose points record fuzzily matches to 60 . there is only one such row in the table . the team 1 record of this unqiue row is fluminense .
eq { count { filter_eq { all_rows ; losing bonus ; 3 } } ; 5 } = true
Output: select the rows whose losing bonus record is equal to 3 . the number of such rows is 5 .
eq { count { filter_eq { all_rows ; home / away ; home } } ; 6 } = true
Output: select the rows whose home  away record fuzzily matches to home . the number of such rows is 6 .
and { only { filter_eq { all_rows ; school / club team ; maynard evans hs } } ; eq { hop { filter_eq { all_rows ; school / club team ; maynard evans hs } ; player } ; darryl dawkins } } = true
Output: select the rows whose school  club team record fuzzily matches to maynard evans hs . there is only one such row in the table . the player record of this unqiue row is darryl dawkins .
and { only { filter_eq { all_rows ; partner ; sergio galdós } } ; eq { hop { filter_eq { all_rows ; partner ; sergio galdós } ; tournament } ; panama city } } = true
Output: select the rows whose partner record fuzzily matches to sergio galdós . there is only one such row in the table . the tournament record of this unqiue row is panama city .
greater { hop { filter_eq { all_rows ; athlete ; thuso mpuang } ; time } ; hop { filter_eq { all_rows ; athlete ; wallace spearmon } ; time } } = true
Output: select the rows whose athlete record fuzzily matches to thuso mpuang . take the time record of this row . select the rows whose athlete record fuzzily matches to wallace spearmon . take the time record of this row . the first record is greater than the second record .
round_eq { sum { all_rows ; yards } ; 192 } = true
Output: the sum of the yards record of all rows is 192 .
most_greater_eq { all_rows ; time / retired ; 2 } = true
Output: for the time  retired records of all rows , most of them are greater than or equal to 2 .
eq { hop { argmax { all_rows ; overall } ; player } ; quentin shore } = true
Output: select the row whose overall record of all rows is maximum . the player record of this row is quentin shore .
eq { hop { nth_argmax { filter_greater { all_rows ; fa cup ; 0 } ; championship ; 2 } ; name } ; stern john } = true
Output: select the rows whose fa cup record is greater than 0 . select the row whose championship record of these rows is 2nd maximum . the name record of this row is stern john .
eq { hop { argmin { all_rows ; rank } ; year } ; 2012 } = true
Output: select the row whose rank record of all rows is minimum . the year record of this row is 2012 .
round_eq { avg { all_rows ; tournaments played } ; 16.1 } = true
Output: the average of the tournaments played record of all rows is 16.1 .
round_eq { sum { filter_eq { all_rows ; opponent ; new york giants } ; attendance } ; 144706 } = true
Output: select the rows whose opponent record fuzzily matches to new york giants . the sum of the attendance record of these rows is 144706 .
greater { hop { filter_eq { all_rows ; year ; 1965 } ; overall record } ; hop { filter_eq { all_rows ; year ; 1959 } ; overall record } } = true
Output: select the rows whose year record fuzzily matches to 1965 . take the overall record record of this row . select the rows whose year record fuzzily matches to 1959 . take the overall record record of this row . the first record is greater than the second record .
round_eq { sum { all_rows ; length ( mi ) } ; 2,336.25 } = true
Output: the sum of the length  mi  record of all rows is 2,336.25 .
eq { hop { nth_argmin { all_rows ; time ; 1 } ; goal } ; denis platonov ( 1 ) } = true
Output: select the row whose time record of all rows is 1st minimum . the goal record of this row is denis platonov  1  .
less { hop { filter_eq { all_rows ; name ; shareese woods } ; react } ; hop { filter_eq { all_rows ; name ; natalya nazarova } ; react } } = true
Output: select the rows whose name record fuzzily matches to shareese woods . take the react record of this row . select the rows whose name record fuzzily matches to natalya nazarova . take the react record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; crowd } ; 20826 } = true
Output: the average of the crowd record of all rows is 20826 .
all_eq { all_rows ; date ; november } = true
Output: for the date records of all rows , all of them fuzzily match to november .
eq { hop { argmax { all_rows ; number } ; language } ; polish } = true
Output: select the row whose number record of all rows is maximum . the language record of this row is polish .
all_eq { all_rows ; original air date ; 2013 } = true
Output: for the original air date records of all rows , all of them fuzzily match to 2013 .
eq { count { filter_eq { all_rows ; round ; 3 } } ; 10 } = true
Output: select the rows whose round record is equal to 3 . the number of such rows is 10 .
and { only { filter_eq { all_rows ; type ; nursery } } ; eq { hop { filter_eq { all_rows ; type ; nursery } ; name } ; heath lane } } = true
Output: select the rows whose type record fuzzily matches to nursery . there is only one such row in the table . the name record of this unqiue row is heath lane .
round_eq { avg { all_rows ; round } ; 3.7 } = true
Output: the average of the round record of all rows is 3.7 .
most_eq { all_rows ; party ; democratic } = true
Output: for the party records of all rows , most of them fuzzily match to democratic .
less { hop { filter_eq { all_rows ; incumbent ; frank wolf } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; rick boucher } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to frank wolf . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to rick boucher . take the first elected record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; country ; luxembourg } } ; eq { hop { filter_eq { all_rows ; country ; luxembourg } ; season } ; 1978 - 79 } } = true
Output: select the rows whose country record fuzzily matches to luxembourg . there is only one such row in the table . the season record of this unqiue row is 1978  79 .
all_eq { all_rows ; c apps ; 0 } = true
Output: for the c apps records of all rows , all of them are equal to 0 .
greater { hop { filter_eq { all_rows ; institution ; saint leo university } ; enrollment } ; hop { filter_eq { all_rows ; institution ; barry university } ; enrollment } } = true
Output: select the rows whose institution record fuzzily matches to saint leo university . take the enrollment record of this row . select the rows whose institution record fuzzily matches to barry university . take the enrollment record of this row . the first record is greater than the second record .
and { only { filter_eq { all_rows ; 2012 club ; diablo water polo } } ; eq { hop { filter_eq { all_rows ; 2012 club ; diablo water polo } ; name } ; maggie steffens } } = true
Output: select the rows whose 2012 club record fuzzily matches to diablo water polo . there is only one such row in the table . the name record of this unqiue row is maggie steffens .
eq { hop { nth_argmax { all_rows ; avg attend ; 2 } ; indoor year } ; 1978 } = true
Output: select the row whose avg attend record of all rows is 2nd maximum . the indoor year record of this row is 1978 .
round_eq { avg { all_rows ; wins } ; .44 } = true
Output: the average of the wins record of all rows is .44 .
eq { hop { nth_argmin { all_rows ; notes ; 2 } ; competition } ; prague marathon } = true
Output: select the row whose notes record of all rows is 2nd minimum . the competition record of this row is prague marathon .
round_eq { avg { all_rows ; prize money } ; 4831665 } = true
Output: the average of the prize money record of all rows is 4831665 .
most_eq { all_rows ; crime ; murder } = true
Output: for the crime records of all rows , most of them fuzzily match to murder .
and { eq { nth_min { all_rows ; date ; 1 } ; 03 oct } ; eq { hop { nth_argmin { all_rows ; date ; 1 } ; time } ; 12:00 } } = true
Output: the 1st minimum date record of all rows is 03 oct . the time record of the row with 1st minimum date record is 12:00 .
most_greater_eq { all_rows ; matches ; 5 } = true
Output: for the matches records of all rows , most of them are greater than or equal to 5 .
eq { hop { argmax { all_rows ; events } ; tournament } ; pga championship } = true
Output: select the row whose events record of all rows is maximum . the tournament record of this row is pga championship .
eq { count { filter_eq { all_rows ; leading scorer ; lee gregory } } ; 3 } = true
Output: select the rows whose leading scorer record fuzzily matches to lee gregory . the number of such rows is 3 .
round_eq { avg { all_rows ; score in final } ; 4.6 } = true
Output: the average of the score in final record of all rows is 4.6 .
and { only { filter_eq { all_rows ; result ; lost } } ; eq { hop { filter_eq { all_rows ; result ; lost } ; date } ; october 7 , 1994 } } = true
Output: select the rows whose result record fuzzily matches to lost . there is only one such row in the table . the date record of this unqiue row is october 7 , 1994 .
eq { hop { nth_argmax { all_rows ; original air date ; 2 } ; title } ; the understudy } = true
Output: select the row whose original air date record of all rows is 2nd maximum . the title record of this row is the understudy .
less { hop { filter_eq { all_rows ; kit supplier ; rapido } ; year } ; hop { filter_eq { all_rows ; kit supplier ; adidas } ; year } } = true
Output: select the rows whose kit supplier record fuzzily matches to rapido . take the year record of this row . select the rows whose kit supplier record fuzzily matches to adidas . take the year record of this row . the first record is less than the second record .
eq { hop { nth_argmin { all_rows ; capacity ; 2 } ; stadium } ; stadio italia } = true
Output: select the row whose capacity record of all rows is 2nd minimum . the stadium record of this row is stadio italia .
eq { hop { argmin { all_rows ; year ( ceremony ) } ; film title used in nomination } ; the child of man } = true
Output: select the row whose year  ceremony  record of all rows is minimum . the film title used in nomination record of this row is the child of man .
eq { hop { argmax { all_rows ; wins } ; club } ; ud salamanca } = true
Output: select the row whose wins record of all rows is maximum . the club record of this row is ud salamanca .
most_eq { all_rows ; result ; re - elected } = true
Output: for the result records of all rows , most of them fuzzily match to re  elected .
and { only { filter_greater { all_rows ; points ; 0 } } ; eq { hop { filter_greater { all_rows ; points ; 0 } ; year } ; 1974 } } = true
Output: select the rows whose points record is greater than 0 . there is only one such row in the table . the year record of this unqiue row is 1974 .
and { only { filter_eq { all_rows ; status ; retired republican hold } } ; eq { hop { filter_eq { all_rows ; status ; retired republican hold } ; district } ; minnesota3 } } = true
Output: select the rows whose status record fuzzily matches to retired republican hold . there is only one such row in the table . the district record of this unqiue row is minnesota3 .
eq { hop { nth_argmin { all_rows ; date ; 1 } ; championship } ; merion , us } = true
Output: select the row whose date record of all rows is 1st minimum . the championship record of this row is merion , us .
eq { hop { argmax { all_rows ; quantity made } ; class } ; 2 } = true
Output: select the row whose quantity made record of all rows is maximum . the class record of this row is 2 .
greater { hop { filter_eq { all_rows ; date ; 13 may 1998 } ; weight ( kg ) } ; hop { filter_eq { all_rows ; date ; 23 aug 1997 } ; weight ( kg ) } } = true
Output: 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 .
round_eq { avg { all_rows ; appearances } ; 98.2 } = true
Output: the average of the appearances record of all rows is 98.2 .
eq { count { filter_eq { all_rows ; goals ; 0 } } ; 2 } = true
Output: select the rows whose goals record is equal to 0 . the number of such rows is 2 .