niryuu/sni-each-converted huggingface dataset:
select the rows whose goal difference record is equal to -1 . there is only one such row in the table . the club record of this unqiue row is ce sabadell fc .
Output: and { only { filter_eq { all_rows ; goal difference ; -1 } } ; eq { hop { filter_eq { all_rows ; goal difference ; -1 } ; club } ; ce sabadell fc } }
for the indole records of all rows , most of them fuzzily match to negative .
Output: most_eq { all_rows ; indole ; negative }
select the row whose opponents record of all rows is maximum . the opponent record of this row is new york jets .
Output: eq { hop { argmax { all_rows ; opponents } ; opponent } ; new york jets }
select the row whose attendance record of all rows is 2nd maximum . the home team record of this row is milton keynes dons .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; home team } ; milton keynes dons }
select the rows whose title record fuzzily matches to capybara . take the originalairdate record of this row . select the rows whose title record fuzzily matches to the sleep of babies . take the originalairdate record of this row . the second record is 7 days larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; title ; capybara } ; originalairdate } ; hop { filter_eq { all_rows ; title ; the sleep of babies } ; originalairdate } } ; -7 days }
select the rows whose rank record is less than or equal to 5 . the average of the bronze record of these rows is 4 .
Output: round_eq { avg { filter_less_eq { all_rows ; rank ; 5 } ; bronze } ; 4 }
select the rows whose bowling style record fuzzily matches to right arm fast - medium . select the row whose date of birth record of these rows is maximum . the first class team record of this row is guyana .
Output: eq { hop { argmax { filter_eq { all_rows ; bowling style ; right arm fast - medium } ; date of birth } ; first class team } ; guyana }
select the row whose employees ( world ) record of all rows is maximum . the name record of this row is volkswagen ag .
Output: eq { hop { argmax { all_rows ; employees ( world ) } ; name } ; volkswagen ag }
the sum of the payout ( us ) record of all rows is 16,030,000 .
Output: round_eq { sum { all_rows ; payout ( us ) } ; 16,030,000 }
select the row whose population record of all rows is 2nd minimum . the english name record of this row is liancheng county .
Output: eq { hop { nth_argmin { all_rows ; population ; 2 } ; english name } ; liancheng county }
select the rows whose chassis record fuzzily matches to williams fw04 . there is only one such row in the table . the year record of this unqiue row is 1975 . the entrant record of this unqiue row is frank williams racing cars .
Output: and { only { filter_eq { all_rows ; chassis ; williams fw04 } } ; and { eq { hop { filter_eq { all_rows ; chassis ; williams fw04 } ; year } ; 1975 } ; eq { hop { filter_eq { all_rows ; chassis ; williams fw04 } ; entrant } ; frank williams racing cars } } }
select the rows whose earnings record is greater than 4000000 . the number of such rows is 2 .
Output: eq { count { filter_greater { all_rows ; earnings ; 4000000 } } ; 2 }
select the rows whose high rebounds record fuzzily matches to andrew bynum . the average of the high rebounds record of these rows is 17.25 .
Output: round_eq { avg { filter_eq { all_rows ; high rebounds ; andrew bynum } ; high rebounds } ; 17.25 }
select the rows whose season record is equal to 2012 . the sum of the points record of these rows is 40 .
Output: round_eq { sum { filter_eq { all_rows ; season ; 2012 } ; points } ; 40 }
select the rows whose opposing teams record fuzzily matches to scotland . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; opposing teams ; scotland } } ; 2 }
select the rows whose result record fuzzily matches to won . there is only one such row in the table . the year record of this unqiue row is 1996 . the group record of this unqiue row is césar awards .
Output: and { only { filter_eq { all_rows ; result ; won } } ; and { eq { hop { filter_eq { all_rows ; result ; won } ; year } ; 1996 } ; eq { hop { filter_eq { all_rows ; result ; won } ; group } ; césar awards } } }
select the row whose points record of all rows is maximum . the series record of this row is formula renault 2.0 nec .
Output: eq { hop { argmax { all_rows ; points } ; series } ; formula renault 2.0 nec }
select the row whose height metres / ft record of all rows is maximum . the name record of this row is shard london bridge .
Output: eq { hop { argmax { all_rows ; height metres / ft } ; name } ; shard london bridge }
select the rows whose date record fuzzily matches to 6 - 95 . take the date record of this row . select the rows whose date record fuzzily matches to 10 - 95 . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; date ; 6 - 95 } ; date } ; hop { filter_eq { all_rows ; date ; 10 - 95 } ; date } }
select the row whose wins record of all rows is 2nd maximum . the player record of this row is tom watson .
Output: eq { hop { nth_argmax { all_rows ; wins ; 2 } ; player } ; tom watson }
the average of the home team score record of all rows is 12.07 .
Output: round_eq { avg { all_rows ; home team score } ; 12.07 }
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 .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; location ; launceston , tasmania } ; years ; 1 } ; original code } ; victorian rules }
select the row whose enrollment record of all rows is maximum . the institution record of this row is grove city college .
Output: eq { hop { argmax { all_rows ; enrollment } ; institution } ; grove city college }
for the most spoken language records of all rows , most of them fuzzily match to sotho .
Output: most_eq { all_rows ; most spoken language ; sotho }
select the rows whose format record fuzzily matches to country . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; format ; country } } ; 2 }
select the rows whose platform record fuzzily matches to wii . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; platform ; wii } } ; 5 }
select the rows whose writer record fuzzily matches to naomi janzen . there is only one such row in the table . the title record of this unqiue row is the tale of the frozen ghost .
Output: and { only { filter_eq { all_rows ; writer ; naomi janzen } } ; eq { hop { filter_eq { all_rows ; writer ; naomi janzen } ; title } ; the tale of the frozen ghost } }
select the rows whose chassis record fuzzily matches to ensign . there is only one such row in the table . the year record of this unqiue row is 1978 .
Output: and { only { filter_eq { all_rows ; chassis ; ensign } } ; eq { hop { filter_eq { all_rows ; chassis ; ensign } ; year } ; 1978 } }
select the rows whose date record fuzzily matches to october . among these rows , select the rows whose attendance record is greater than 50000 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_eq { all_rows ; date ; october } ; attendance ; 50000 } } ; 2 }
select the rows whose first elected record fuzzily matches to special . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; first elected ; special } } ; 4 }
for the builder records of all rows , most of them fuzzily match to avonside engine company .
Output: most_eq { all_rows ; builder ; avonside engine company }
select the row whose time record of all rows is 1st minimum . the rank record of this row is 1 .
Output: eq { hop { nth_argmin { all_rows ; time ; 1 } ; rank } ; 1 }
select the rows whose nbr class record fuzzily matches to 251 . take the total record of this row . select the rows whose nbr class record fuzzily matches to 229 . take the total record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; nbr class ; 251 } ; total } ; hop { filter_eq { all_rows ; nbr class ; 229 } ; total } }
select the rows whose won record is equal to 9 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; won ; 9 } } ; 2 }
the average of the attendance record of all rows is 55680.4 .
Output: round_eq { avg { all_rows ; attendance } ; 55680.4 }
the average of the crowd record of all rows is 19900 .
Output: round_eq { avg { all_rows ; crowd } ; 19900 }
select the rows whose rank record is less than 6 . the sum of the gold record of these rows is 42 .
Output: round_eq { sum { filter_less { all_rows ; rank ; 6 } ; gold } ; 42 }
select the rows whose gold record is greater than 0 . the number of such rows is 6 .
Output: eq { count { filter_greater { all_rows ; gold ; 0 } } ; 6 }
select the rows whose country record fuzzily matches to england . there is only one such row in the table .
Output: only { filter_eq { all_rows ; country ; england } }
select the rows whose home / away record fuzzily matches to home . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; home / away ; home } } ; 6 }
select the row whose goals for record of all rows is 2nd maximum . the team record of this row is blackpool mechanics .
Output: eq { hop { nth_argmax { all_rows ; goals for ; 2 } ; team } ; blackpool mechanics }
select the rows whose reason for change record fuzzily matches to died . there is only one such row in the table . the vacator record of this unqiue row is thomas blount ( dr ) .
Output: and { only { filter_eq { all_rows ; reason for change ; died } } ; eq { hop { filter_eq { all_rows ; reason for change ; died } ; vacator } ; thomas blount ( dr ) } }
select the row whose weight ( lbs ) record of all rows is maximum . the name record of this row is joseph barksdale .
Output: eq { hop { argmax { all_rows ; weight ( lbs ) } ; name } ; joseph barksdale }
select the rows whose venue record fuzzily matches to mcg . take the crowd record of this row . select the rows whose venue record fuzzily matches to vfl park . take the crowd record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; venue ; mcg } ; crowd } ; hop { filter_eq { all_rows ; venue ; vfl park } ; crowd } }
select the row whose time record of all rows is minimum . the name record of this row is tom malchow .
Output: eq { hop { argmin { all_rows ; time } ; name } ; tom malchow }
select the row whose gold record of all rows is 2nd maximum . the nation record of this row is mexico .
Output: eq { hop { nth_argmax { all_rows ; gold ; 2 } ; nation } ; mexico }
the sum of the crowd record of all rows is 123128 .
Output: round_eq { sum { all_rows ; crowd } ; 123128 }
the average of the enrollment record of all rows is 1432 .
Output: round_eq { avg { all_rows ; enrollment } ; 1432 }
for the game site records of all rows , most of them fuzzily match to kingdome .
Output: most_eq { all_rows ; game site ; kingdome }
select the rows whose away team score record is greater than 10 . among these rows , select the rows whose crowd record is greater than 20,000 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_greater { all_rows ; away team score ; 10 } ; crowd ; 20,000 } } ; 2 }
select the rows whose socket record fuzzily matches to socket m socket p bga479 . there is only one such row in the table . the model ( list ) record of this unqiue row is t5xxx t7xxx .
Output: and { only { filter_eq { all_rows ; socket ; socket m socket p bga479 } } ; eq { hop { filter_eq { all_rows ; socket ; socket m socket p bga479 } ; model ( list ) } ; t5xxx t7xxx } }
the 1st minimum date record of all rows is 18 may 1993 .
Output: eq { nth_min { all_rows ; date ; 1 } ; 18 may 1993 }
the average of the won record of all rows is 8.8 .
Output: round_eq { avg { all_rows ; won } ; 8.8 }
for the total records of all rows , all of them are greater than or equal to 1 .
Output: all_greater_eq { all_rows ; total ; 1 }
select the rows whose time record is greater than 6:00 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; time ; 6:00 } } ; 3 }
select the rows whose team name record fuzzily matches to isport international . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; team name ; isport international } } ; 4 }
select the rows whose title record fuzzily matches to smart girl . take the original air date record of this row . select the rows whose title record fuzzily matches to number 1 fan . take the original air date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; title ; smart girl } ; original air date } ; hop { filter_eq { all_rows ; title ; number 1 fan } ; original air date } }
select the row whose time record of all rows is minimum . the athlete record of this row is veronica campbell - brown .
Output: eq { hop { argmin { all_rows ; time } ; athlete } ; veronica campbell - brown }
select the rows whose 2003 - 04 pts record is equal to - . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; 2003 - 04 pts ; - } } ; 4 }
select the rows whose rank record is less than or equal to 3 . the average of the time record of these rows is 10.73 .
Output: round_eq { avg { filter_less_eq { all_rows ; rank ; 3 } ; time } ; 10.73 }
select the rows whose incumbent record fuzzily matches to ivor d fenton . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to hardie scott . take the first elected record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; incumbent ; ivor d fenton } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; hardie scott } ; first elected } }
select the rows whose cause of destruction record fuzzily matches to still in existence . there is only one such row in the table . the name record of this unqiue row is great pyramid of giza .
Output: and { only { filter_eq { all_rows ; cause of destruction ; still in existence } } ; eq { hop { filter_eq { all_rows ; cause of destruction ; still in existence } ; name } ; great pyramid of giza } }
select the rows whose date record fuzzily matches to june 11 . take the high assists record of this row . select the rows whose date record fuzzily matches to june 10 . take the high assists record of this row . the first record fuzzily matches to the second record .
Output: eq { hop { filter_eq { all_rows ; date ; june 11 } ; high assists } ; hop { filter_eq { all_rows ; date ; june 10 } ; high assists } }
select the rows whose points margin record is less than 10 . there is only one such row in the table . the proceed to quarter - final record of this unqiue row is montauban . the eliminated from competition record of this unqiue row is borders .
Output: and { only { filter_less { all_rows ; points margin ; 10 } } ; and { eq { hop { filter_less { all_rows ; points margin ; 10 } ; proceed to quarter - final } ; montauban } ; eq { hop { filter_less { all_rows ; points margin ; 10 } ; eliminated from competition } ; borders } } }
select the row whose capacity record of all rows is maximum . the club record of this row is real madrid cf .
Output: eq { hop { argmax { all_rows ; capacity } ; club } ; real madrid cf }
select the row whose potential prize money record of all rows is maximum . the season record of this row is 5 .
Output: eq { hop { argmax { all_rows ; potential prize money } ; season } ; 5 }
select the row whose total goals record of all rows is maximum . the name record of this row is billy price .
Output: eq { hop { argmax { all_rows ; total goals } ; name } ; billy price }
select the rows whose home team record fuzzily matches to melbourne . the average of the crowd record of these rows is 15750 .
Output: round_eq { avg { filter_eq { all_rows ; home team ; melbourne } ; crowd } ; 15750 }
the average of the frequency record of all rows is 2200 mhz .
Output: round_eq { avg { all_rows ; frequency } ; 2200 mhz }
select the rows whose points record is equal to 0 . there is only one such row in the table . the opponent record of this unqiue row is new jersey devils .
Output: and { only { filter_eq { all_rows ; points ; 0 } } ; eq { hop { filter_eq { all_rows ; points ; 0 } ; opponent } ; new jersey devils } }
for the series premiere records of all rows , most of them fuzzily match to unknown .
Output: most_eq { all_rows ; series premiere ; unknown }
select the rows whose name record fuzzily matches to martin schmitt . take the points record of this row . select the rows whose name record fuzzily matches to denis kornilov . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ; martin schmitt } ; points } ; hop { filter_eq { all_rows ; name ; denis kornilov } ; points } }
select the row whose children together record of all rows is maximum . the name record of this row is martin van buren .
Output: eq { hop { argmax { all_rows ; children together } ; name } ; martin van buren }
select the rows whose candidates record fuzzily matches to 100 % . there is only one such row in the table . the district record of this unqiue row is new york 15 . the incumbent record of this unqiue row is michael hoffman .
Output: and { only { filter_eq { all_rows ; candidates ; 100 % } } ; and { eq { hop { filter_eq { all_rows ; candidates ; 100 % } ; district } ; new york 15 } ; eq { hop { filter_eq { all_rows ; candidates ; 100 % } ; incumbent } ; michael hoffman } } }
select the rows whose player record fuzzily matches to jeff feniak . take the round record of this row . select the rows whose player record fuzzily matches to konstantin rudenko . take the round record of this row . the second record is 2 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; player ; jeff feniak } ; round } ; hop { filter_eq { all_rows ; player ; konstantin rudenko } ; round } } ; -2 }
the sum of the electorate record of all rows is 2633581 .
Output: round_eq { sum { all_rows ; electorate } ; 2633581 }
select the rows whose name record fuzzily matches to northern eagle . take the year record of this row . select the rows whose name record fuzzily matches to northern hawk . take the year record of this row . the second record is 15 years larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; name ; northern eagle } ; year } ; hop { filter_eq { all_rows ; name ; northern hawk } ; year } } ; -15 years }
the average of the julian clary record of all rows is 8 .
Output: round_eq { avg { all_rows ; julian clary } ; 8 }
the sum of the attendance record of all rows is 791,469 .
Output: round_eq { sum { all_rows ; attendance } ; 791,469 }
select the rows whose gold record is greater than 0 . among these rows , select the rows whose bronze record is equal to 2 . there is only one such row in the table . the nation record of this unqiue row is soviet union ( urs ) .
Output: and { only { filter_eq { filter_greater { all_rows ; gold ; 0 } ; bronze ; 2 } } ; eq { hop { filter_eq { filter_greater { all_rows ; gold ; 0 } ; bronze ; 2 } ; nation } ; soviet union ( urs ) } }
select the rows whose directed by record fuzzily matches to sam montes . the 1st minimum original airdate record of these rows is april 23 , 2010 . the title record of the row with 1st minimum original airdate record is the day that everything changed .
Output: and { eq { nth_min { filter_eq { all_rows ; directed by ; sam montes } ; original airdate ; 1 } ; april 23 , 2010 } ; eq { hop { nth_argmin { filter_eq { all_rows ; directed by ; sam montes } ; original airdate ; 1 } ; title } ; the day that everything changed } }
select the row whose attendance record of all rows is maximum . the game site record of this row is los angeles memorial coliseum .
Output: eq { hop { argmax { all_rows ; attendance } ; game site } ; los angeles memorial coliseum }
select the row whose shot % record of all rows is 2nd maximum . the skip record of this row is david murdoch .
Output: eq { hop { nth_argmax { all_rows ; shot % ; 2 } ; skip } ; david murdoch }
select the rows whose election record fuzzily matches to 2001 . take the of candidates nominated record of this row . select the rows whose election record fuzzily matches to 2004 . take the of candidates nominated record of this row . the second record is 39 larger than the first record . the of candidates nominated record of the first row is 10 . the of candidates nominated record of the second row is 49 .
Output: and { eq { diff { hop { filter_eq { all_rows ; election ; 2001 } ; of candidates nominated } ; hop { filter_eq { all_rows ; election ; 2004 } ; of candidates nominated } } ; -39 } ; and { eq { hop { filter_eq { all_rows ; election ; 2001 } ; of candidates nominated } ; 10 } ; eq { hop { filter_eq { all_rows ; election ; 2004 } ; of candidates nominated } ; 49 } } }
select the rows whose airborne unit record fuzzily matches to pathfinders . select the row whose drop zone time record of these rows is 1st minimum . the drop zone record of this row is a .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; airborne unit ; pathfinders } ; drop zone time ; 1 } ; drop zone } ; a }
select the rows whose 1st ship delivery date record fuzzily matches to may 1942 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; 1st ship delivery date ; may 1942 } } ; 3 }
select the rows whose chassis record fuzzily matches to hesketh 308e . take the year record of this row . select the rows whose chassis record fuzzily matches to surtees ts19 . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; chassis ; hesketh 308e } ; year } ; hop { filter_eq { all_rows ; chassis ; surtees ts19 } ; year } }
select the rows whose year record is equal to 1985 . the sum of the points record of these rows is 8 .
Output: round_eq { sum { filter_eq { all_rows ; year ; 1985 } ; points } ; 8 }
select the rows whose artist record fuzzily matches to kerri burnett . there is only one such row in the table . the theme record of this unqiue row is trumpeter swan .
Output: and { only { filter_eq { all_rows ; artist ; kerri burnett } } ; eq { hop { filter_eq { all_rows ; artist ; kerri burnett } ; theme } ; trumpeter swan } }
select the row whose attendance record of all rows is maximum . the date record of this row is october 10 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; october 10 }
select the rows whose career win - loss record fuzzily matches to 3 - 5 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; career win - loss ; 3 - 5 } } ; 2 }
select the rows whose surface record fuzzily matches to grass . there is only one such row in the table .
Output: only { filter_eq { all_rows ; surface ; grass } }
select the rows whose chassis record fuzzily matches to wildcat . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; chassis ; wildcat } } ; 2 }
select the row whose score record of all rows is minimum . the player record of this row is ben hogan .
Output: eq { hop { argmin { all_rows ; score } ; player } ; ben hogan }
select the rows whose nation record fuzzily matches to united states . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; nation ; united states } } ; 5 }
for the result records of all rows , all of them fuzzily match to not nominated .
Output: all_eq { all_rows ; result ; not nominated }
for the entrant records of all rows , most of them fuzzily match to clarke - mordaunt - guthrie .
Output: most_eq { all_rows ; entrant ; clarke - mordaunt - guthrie }
the average of the silver record of all rows is 5.2 .
Output: round_eq { avg { all_rows ; silver } ; 5.2 }
select the row whose year record of all rows is 2nd minimum . the author record of this row is anthony browne .
Output: eq { hop { nth_argmin { all_rows ; year ; 2 } ; author } ; anthony browne }
select the rows whose champion record fuzzily matches to aktobe . for the season records of these rows , all of them are greater than 2004 .
Output: all_greater { filter_eq { all_rows ; champion ; aktobe } ; season ; 2004 }
select the row whose points record of all rows is maximum . the year record of this row is 1989 .
Output: eq { hop { argmax { all_rows ; points } ; year } ; 1989 }
select the row whose number of electorates ( 2009 ) record of all rows is 2nd maximum . the name record of this row is susner .
Output: eq { hop { nth_argmax { all_rows ; number of electorates ( 2009 ) ; 2 } ; name } ; susner }
for the distance records of all rows , all of them fuzzily match to 240.5 km .
Output: all_eq { all_rows ; distance ; 240.5 km }
for the written by records of all rows , all of them fuzzily match to michael hirst .
Output: all_eq { all_rows ; written by ; michael hirst }
select the rows whose date record fuzzily matches to april 19 . take the high points record of this row . select the rows whose date record fuzzily matches to april 20 . take the high points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; april 19 } ; high points } ; hop { filter_eq { all_rows ; date ; april 20 } ; high points } }
select the rows whose entrant record fuzzily matches to british racing partnership . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; entrant ; british racing partnership } } ; 2 }
select the rows whose earpads record fuzzily matches to circumaural bowl . there is only one such row in the table . the headphone model record of this unqiue row is gs1000i .
Output: and { only { filter_eq { all_rows ; earpads ; circumaural bowl } } ; eq { hop { filter_eq { all_rows ; earpads ; circumaural bowl } ; headphone model } ; gs1000i } }
for the time records of all rows , most of them are less than 4:00 .
Output: most_less { all_rows ; time ; 4:00 }
select the row whose home team score record of all rows is 2nd maximum . the home team record of this row is essendon .
Output: eq { hop { nth_argmax { all_rows ; home team score ; 2 } ; home team } ; essendon }
select the rows whose venue record fuzzily matches to lake oval . there is only one such row in the table . the home team record of this unqiue row is south melbourne .
Output: and { only { filter_eq { all_rows ; venue ; lake oval } } ; eq { hop { filter_eq { all_rows ; venue ; lake oval } ; home team } ; south melbourne } }
select the rows whose name record fuzzily matches to walter llerena ( ecu ) . take the total ( kg ) record of this row . select the rows whose name record fuzzily matches to guy hamilton ( can ) . take the total ( kg ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ; walter llerena ( ecu ) } ; total ( kg ) } ; hop { filter_eq { all_rows ; name ; guy hamilton ( can ) } ; total ( kg ) } }
select the rows whose party record fuzzily matches to democratic . there is only one such row in the table . the incumbent record of this unqiue row is james g polk .
Output: and { only { filter_eq { all_rows ; party ; democratic } } ; eq { hop { filter_eq { all_rows ; party ; democratic } ; incumbent } ; james g polk } }
select the row whose laid down record of all rows is minimum . the name record of this row is chanticleer .
Output: eq { hop { argmin { all_rows ; laid down } ; name } ; chanticleer }
select the rows whose player record fuzzily matches to tim jones . take the pick record of this row . select the rows whose player record fuzzily matches to lew kamanu . take the pick record of this row . the first record is greater than the second record . the position record of the first row is quarterback . the position record of the second row is defensive end .
Output: and { greater { hop { filter_eq { all_rows ; player ; tim jones } ; pick } ; hop { filter_eq { all_rows ; player ; lew kamanu } ; pick } } ; and { eq { hop { filter_eq { all_rows ; player ; tim jones } ; position } ; quarterback } ; eq { hop { filter_eq { all_rows ; player ; lew kamanu } ; position } ; defensive end } } }
the 8th minimum round record of all rows is 7 . the player record of the row with 8th minimum round record is bob chandler .
Output: and { eq { nth_min { all_rows ; round ; 8 } ; 7 } ; eq { hop { nth_argmin { all_rows ; round ; 8 } ; player } ; bob chandler } }
select the rows whose class / type record fuzzily matches to passenger ship . there is only one such row in the table . the builder record of this unqiue row is john roach and son .
Output: and { only { filter_eq { all_rows ; class / type ; passenger ship } } ; eq { hop { filter_eq { all_rows ; class / type ; passenger ship } ; builder } ; john roach and son } }
select the rows whose 1st round record fuzzily matches to 2 - 0 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; 1st round ; 2 - 0 } } ; 3 }
select the rows whose site / stadium record fuzzily matches to rosenblatt stadium . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; site / stadium ; rosenblatt stadium } } ; 7 }
select the rows whose event record fuzzily matches to espn great outdoor games . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; event ; espn great outdoor games } } ; 3 }
select the rows whose outcome record fuzzily matches to runner - up . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; outcome ; runner - up } } ; 2 }
select the rows whose wins record is greater than or equal to 5 . for the last runners - up records of these rows , most of them are greater than or equal to 1987 .
Output: most_greater_eq { filter_greater_eq { all_rows ; wins ; 5 } ; last runners - up ; 1987 }
select the rows whose metres record is greater than 300 . there is only one such row in the table . the name record of this unqiue row is the shard .
Output: and { only { filter_greater { all_rows ; metres ; 300 } } ; eq { hop { filter_greater { all_rows ; metres ; 300 } ; name } ; the shard } }
for the power kw records of all rows , most of them fuzzily match to 10 kw .
Output: most_eq { all_rows ; power kw ; 10 kw }
for the socket records of all rows , most of them fuzzily match to lga 1156 .
Output: most_eq { all_rows ; socket ; lga 1156 }
select the rows whose 8:30 record fuzzily matches to parks and recreation . there is only one such row in the table .
Output: only { filter_eq { all_rows ; 8:30 ; parks and recreation } }
the average of the lost record of all rows is 9.7 .
Output: round_eq { avg { all_rows ; lost } ; 9.7 }
the 3rd maximum crowd record of all rows is 13805 .
Output: eq { nth_max { all_rows ; crowd ; 3 } ; 13805 }
select the rows whose nationality record fuzzily matches to united states . there is only one such row in the table .
Output: only { filter_eq { all_rows ; nationality ; united states } }
select the rows whose position record fuzzily matches to quarterback . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; position ; quarterback } } ; 2 }
select the row whose crowd record of all rows is 1st maximum . the home team record of this row is carlton . the away team record of this row is richmond . the date record of this row is 10 may 1930 .
Output: and { eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; home team } ; carlton } ; and { eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; away team } ; richmond } ; eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; date } ; 10 may 1930 } } }
select the row whose agg record of all rows is 2nd maximum . the team 2 record of this row is defence force .
Output: eq { hop { nth_argmax { all_rows ; agg ; 2 } ; team 2 } ; defence force }
select the rows whose venue record fuzzily matches to hong kong . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; venue ; hong kong } } ; 4 }
select the rows whose outcome record fuzzily matches to runner-up . there is only one such row in the table . the year record of this unqiue row is 1993 .
Output: and { only { filter_eq { all_rows ; outcome ; runner-up } } ; eq { hop { filter_eq { all_rows ; outcome ; runner-up } ; year } ; 1993 } }
select the rows whose player record fuzzily matches to steve pepin . take the nhl team record of this row . select the rows whose player record fuzzily matches to john miner . take the nhl team record of this row . the first record does not match to the second record . the nhl team record of the first row is chicago black hawks . the nhl team record of the second row is edmonton oilers .
Output: and { not_eq { hop { filter_eq { all_rows ; player ; steve pepin } ; nhl team } ; hop { filter_eq { all_rows ; player ; john miner } ; nhl team } } ; and { eq { hop { filter_eq { all_rows ; player ; steve pepin } ; nhl team } ; chicago black hawks } ; eq { hop { filter_eq { all_rows ; player ; john miner } ; nhl team } ; edmonton oilers } } }
select the rows whose gold record is greater than 1 . there is only one such row in the table . the nation record of this unqiue row is germany .
Output: and { only { filter_greater { all_rows ; gold ; 1 } } ; eq { hop { filter_greater { all_rows ; gold ; 1 } ; nation } ; germany } }
for the rank records of all rows , most of them fuzzily match to unranked .
Output: most_eq { all_rows ; rank ; unranked }
the average of the first elected record of all rows is 1992 .
Output: round_eq { avg { all_rows ; first elected } ; 1992 }
select the rows whose opponent record fuzzily matches to dallas cowboys . take the date record of this row . select the rows whose opponent record fuzzily matches to green bay packers . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; opponent ; dallas cowboys } ; date } ; hop { filter_eq { all_rows ; opponent ; green bay packers } ; date } }
select the rows whose country record fuzzily matches to canada . there is only one such row in the table . the year record of this unqiue row is 2011 .
Output: and { only { filter_eq { all_rows ; country ; canada } } ; eq { hop { filter_eq { all_rows ; country ; canada } ; year } ; 2011 } }
select the rows whose of which currently forests , km square record is greater than 1000 . there is only one such row in the table .
Output: only { filter_greater { all_rows ; of which currently forests , km square ; 1000 } }
select the rows whose year record is equal to 1990 . the sum of the pages record of these rows is 1135 .
Output: round_eq { sum { filter_eq { all_rows ; year ; 1990 } ; pages } ; 1135 }
select the row whose notes record of all rows is 3rd maximum . the year record of this row is 2004 .
Output: eq { hop { nth_argmax { all_rows ; notes ; 3 } ; year } ; 2004 }
select the rows whose venue record fuzzily matches to new zealand . there is only one such row in the table . the year record of this unqiue row is 1988 .
Output: and { only { filter_eq { all_rows ; venue ; new zealand } } ; eq { hop { filter_eq { all_rows ; venue ; new zealand } ; year } ; 1988 } }
the average of the qual record of all rows is 141.562 .
Output: round_eq { avg { all_rows ; qual } ; 141.562 }
select the rows whose cfl team record fuzzily matches to hamilton tiger - cats . take the pick record of this row . select the rows whose cfl team record fuzzily matches to british columbia lions . take the pick record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; cfl team ; hamilton tiger - cats } ; pick } ; hop { filter_eq { all_rows ; cfl team ; british columbia lions } ; pick } }
select the row whose points record of all rows is 2nd maximum . the club record of this row is chepstow rfc .
Output: eq { hop { nth_argmax { all_rows ; points ; 2 } ; club } ; chepstow rfc }
for the socialist ticket records of all rows , most of them fuzzily match to man .
Output: most_eq { all_rows ; socialist ticket ; man }
select the rows whose trim record fuzzily matches to xe ( 2009 ) . take the transmission record of this row . select the rows whose trim record fuzzily matches to xr ( 2009 ) . take the transmission record of this row . the first record fuzzily matches to the second record . the transmission record of the first row is 6 - speed 6t40 . the transmission record of the second row is 6 - speed 6t40 .
Output: and { eq { hop { filter_eq { all_rows ; trim ; xe ( 2009 ) } ; transmission } ; hop { filter_eq { all_rows ; trim ; xr ( 2009 ) } ; transmission } } ; and { eq { hop { filter_eq { all_rows ; trim ; xe ( 2009 ) } ; transmission } ; 6 - speed 6t40 } ; eq { hop { filter_eq { all_rows ; trim ; xr ( 2009 ) } ; transmission } ; 6 - speed 6t40 } } }
select the rows whose high points record fuzzily matches to richard hamilton . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; high points ; richard hamilton } } ; 4 }
select the row whose results record of all rows is maximum . the week record of this row is 10 .
Output: eq { hop { argmax { all_rows ; results } ; week } ; 10 }
select the row whose overall wc points ( rank ) record of all rows is maximum . the name record of this row is thomas morgenstern .
Output: eq { hop { argmax { all_rows ; overall wc points ( rank ) } ; name } ; thomas morgenstern }
select the rows whose tv station record fuzzily matches to fuji tv . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; tv station ; fuji tv } } ; 5 }
select the rows whose english name record fuzzily matches to capital region of denmark . take the population ( january 1 , 2008 ) record of this row . select the rows whose english name record fuzzily matches to region of southern denmark . take the population ( january 1 , 2008 ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; english name ; capital region of denmark } ; population ( january 1 , 2008 ) } ; hop { filter_eq { all_rows ; english name ; region of southern denmark } ; population ( january 1 , 2008 ) } }
select the rows whose player record fuzzily matches to chad campbell . take the score record of this row . select the rows whose player record fuzzily matches to jim furyk . take the score record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; chad campbell } ; score } ; hop { filter_eq { all_rows ; player ; jim furyk } ; score } }
select the rows whose competition record fuzzily matches to friendly match . there is only one such row in the table . the date record of this unqiue row is september 27 , 1993 .
Output: and { only { filter_eq { all_rows ; competition ; friendly match } } ; eq { hop { filter_eq { all_rows ; competition ; friendly match } ; date } ; september 27 , 1993 } }
select the rows whose venue record fuzzily matches to glenferrie oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to victoria park . take the crowd record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; venue ; glenferrie oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; victoria park } ; crowd } }
the average of the crowd record of all rows is 20000 .
Output: round_eq { avg { all_rows ; crowd } ; 20000 }
for the conversions records of all rows , most of them are greater than 50 .
Output: most_greater { all_rows ; conversions ; 50 }
select the row whose year record of all rows is 1st minimum . the engine record of this row is m67d40 .
Output: eq { hop { nth_argmin { all_rows ; year ; 1 } ; engine } ; m67d40 }
for the class records of all rows , most of them fuzzily match to 250cc .
Output: most_eq { all_rows ; class ; 250cc }
select the rows whose rebounds per game record is equal to 3.4 . there is only one such row in the table . the tournament record of this unqiue row is 2011 eurobasket .
Output: and { only { filter_eq { all_rows ; rebounds per game ; 3.4 } } ; eq { hop { filter_eq { all_rows ; rebounds per game ; 3.4 } ; tournament } ; 2011 eurobasket } }
the average of the viewers record of all rows is 872100 .
Output: round_eq { avg { all_rows ; viewers } ; 872100 }
select the rows whose hometown record fuzzily matches to ca . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; hometown ; ca } } ; 2 }
select the rows whose location record fuzzily matches to minsk . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; location ; minsk } } ; 3 }
select the rows whose moving parts record fuzzily matches to yes . there is only one such row in the table . the technology record of this unqiue row is flow .
Output: and { only { filter_eq { all_rows ; moving parts ; yes } } ; eq { hop { filter_eq { all_rows ; moving parts ; yes } ; technology } ; flow } }
select the row whose apps record of all rows is maximum . the name record of this row is jürgen grabowski .
Output: eq { hop { argmax { all_rows ; apps } ; name } ; jürgen grabowski }
for the high assists records of all rows , most of them fuzzily match to joe johnson .
Output: most_eq { all_rows ; high assists ; joe johnson }
select the rows whose opponent record fuzzily matches to toronto maple leafs . the sum of the score record of these rows is 12 .
Output: round_eq { sum { filter_eq { all_rows ; opponent ; toronto maple leafs } ; score } ; 12 }
select the rows whose year aired record fuzzily matches to 2007 . the maximum top prize record of these rows is 250000 .
Output: eq { max { filter_eq { all_rows ; year aired ; 2007 } ; top prize } ; 250000 }
select the row whose date of birth record of all rows is 2nd minimum . the president record of this row is jules rimet .
Output: eq { hop { nth_argmin { all_rows ; date of birth ; 2 } ; president } ; jules rimet }
select the rows whose 1st leg record fuzzily matches to 1 - 1 . there is only one such row in the table . the team 1 record of this unqiue row is bolton wanderers . the team 2 record of this unqiue row is sporting cp .
Output: and { only { filter_eq { all_rows ; 1st leg ; 1 - 1 } } ; and { eq { hop { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; team 1 } ; bolton wanderers } ; eq { hop { filter_eq { all_rows ; 1st leg ; 1 - 1 } ; team 2 } ; sporting cp } } }
for the points records of all rows , most of them are greater than 10 .
Output: most_greater { all_rows ; points ; 10 }
the average of the episodes record of all rows is 10.72 .
Output: round_eq { avg { all_rows ; episodes } ; 10.72 }
the average of the attendance record of all rows is 19119 .
Output: round_eq { avg { all_rows ; attendance } ; 19119 }
the average of the goals record of all rows is 26.2 .
Output: round_eq { avg { all_rows ; goals } ; 26.2 }
select the row whose pick record of all rows is 3rd maximum . the player record of this row is jean - fredric tremblay .
Output: eq { hop { nth_argmax { all_rows ; pick ; 3 } ; player } ; jean - fredric tremblay }
the average of the rank record of all rows is 20 .
Output: round_eq { avg { all_rows ; rank } ; 20 }
for the country records of all rows , most of them fuzzily match to soviet union .
Output: most_eq { all_rows ; country ; soviet union }
select the rows whose 3d support record fuzzily matches to no . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; 3d support ; no } } ; 2 }
select the row whose difference record of all rows is 2nd maximum . the team record of this row is americano - sp .
Output: eq { hop { nth_argmax { all_rows ; difference ; 2 } ; team } ; americano - sp }
the maximum frequency record of all rows is 1000 mhz .
Output: eq { max { all_rows ; frequency } ; 1000 mhz }
select the row whose time record of all rows is minimum . the country record of this row is australia .
Output: eq { hop { argmin { all_rows ; time } ; country } ; australia }
select the rows whose winner record fuzzily matches to sergio rivero . take the year record of this row . select the rows whose winner record fuzzily matches to vicente seguí porres . take the year record of this row . the first record is 2 years larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; winner ; sergio rivero } ; year } ; hop { filter_eq { all_rows ; winner ; vicente seguí porres } ; year } } ; 2 years }
for the 2nd run records of all rows , most of them are less than 40 .
Output: most_less { all_rows ; 2nd run ; 40 }
select the row whose number of seasons in top division record of all rows is 3rd maximum . the club record of this row is atlante .
Output: eq { hop { nth_argmax { all_rows ; number of seasons in top division ; 3 } ; club } ; atlante }
select the rows whose games record is greater than 30 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; games ; 30 } } ; 3 }
for the round records of all rows , most of them are greater than 3 .
Output: most_greater { all_rows ; round ; 3 }
select the rows whose ethnic group record fuzzily matches to tigrigna . take the christians record of this row . select the rows whose ethnic group record fuzzily matches to saho . take the christians record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; ethnic group ; tigrigna } ; christians } ; hop { filter_eq { all_rows ; ethnic group ; saho } ; christians } }
select the rows whose round record is equal to 2 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; round ; 2 } } ; 2 }
select the rows whose position record is greater than 15 . there is only one such row in the table . the year record of this unqiue row is 2005 . the competition record of this unqiue row is world youth championships .
Output: and { only { filter_greater { all_rows ; position ; 15 } } ; and { eq { hop { filter_greater { all_rows ; position ; 15 } ; year } ; 2005 } ; eq { hop { filter_greater { all_rows ; position ; 15 } ; competition } ; world youth championships } } }
select the rows whose away team score record is less than 20 . among these rows , select the rows whose crowd record is less than 20000 . the number of such rows is 3 .
Output: eq { count { filter_less { filter_less { all_rows ; away team score ; 20 } ; crowd ; 20000 } } ; 3 }
select the rows whose artist record fuzzily matches to mariana popova . take the televote / sms record of this row . select the rows whose artist record fuzzily matches to stefan dobrev . take the televote / sms record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; artist ; mariana popova } ; televote / sms } ; hop { filter_eq { all_rows ; artist ; stefan dobrev } ; televote / sms } }
select the rows whose attendance record is greater than 20,000 . the number of such rows is 5 .
Output: eq { count { filter_greater { all_rows ; attendance ; 20,000 } } ; 5 }
for the location records of all rows , most of them fuzzily match to arlington .
Output: most_eq { all_rows ; location ; arlington }
select the rows whose president record fuzzily matches to josiah bartlet . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; president ; josiah bartlet } } ; 5 }
for the time records of all rows , most of them are greater than or equal to 2:00.00 .
Output: most_greater_eq { all_rows ; time ; 2:00.00 }
select the row whose departure pune record of all rows is 2nd minimum . the train number record of this row is 99806 .
Output: eq { hop { nth_argmin { all_rows ; departure pune ; 2 } ; train number } ; 99806 }
select the rows whose city record fuzzily matches to atlanta , ga . take the passengers record of this row . select the rows whose city record fuzzily matches to orlando , fl . take the passengers record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; city ; atlanta , ga } ; passengers } ; hop { filter_eq { all_rows ; city ; orlando , fl } ; passengers } }
select the row whose date record of all rows is minimum . the location / attendance record of this row is conseco fieldhouse 8214 .
Output: eq { hop { argmin { all_rows ; date } ; location / attendance } ; conseco fieldhouse 8214 }