MMTU-benchmark/MMTU huggingface dataset:
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| consequence type                      |
|:--------------------------------------|
| discretionary                         |
| mandatory/automatic                   |
| background check                      |
| discretionary; discretionary (waiver) |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| validazione progetto                    |
|:----------------------------------------|
| prot. 45541/2012                        |
| prot.94639/2013                         |
| prot.26080/2013                         |
| prot.75321/2014                         |
| prot.56882/2013                         |
| prot.12905/2013                         |
| prot.88330/2016                         |
| prot.203831/2015                        |
| prot.54828/2015                         |
| prot.14216/2012                         |
| prot.99432/2015                         |
| prot.172731/2015                        |
| prot.169264/2015                        |
| prot.193885/2015                        |
| prot.188675/2015                        |
| prot.195605/2015                        |
| prot.116968/2015                        |
| prot.194874/2015                        |
| prot.59067/2016                         |
| prot.57155/2016                         |
| prot.85599/2016                         |
| prot.101074/2013                        |
| prot.101131/2013                        |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| proyecto                                                                         |
|:---------------------------------------------------------------------------------|
| rehabilitación de la calle durango entre hidalgo y av. allende                   |
| rehabilitación de calles: quéretaro entre hidalgo y avenida allende              |
| construcción de camino la estanzuela ¿ el caracol                                |
| pav. calle ignacio lópez rayón tramo constitución de 1917 a anillo periferico 2a |
| modernización y ampliación del camino cofradía de acuitapilco                    |
| modernizacion y ampliacion de camino tipo ¿c¿ jicote-los salazares               |
| construcción de camino la estanzuela ¿ el caracol_ tramo: del km 0+000 al km 9+5 |
| conservacion del camino: las cruces ¿ e.c. salvatierra ¿ acámbaro _ gto          |
| conservacion del camino: san ignacio ¿ el fresno_ jerecuaro_ gto                 |
| reconstruccion del camino: caleras de ameche  ¿ ixtla  apaseo el grande_ gto.    |
| rehabilitación de calle revolución social                                        |
| rehabilitación de avenida                                                        |
| rehabilitación de la avenida victoria                                            |
| modernización: fresnillo - valparaíso_ del km. 13+700  al km 17+700              |
| modernización: fresnillo - valparaíso_  km 9+000 al km. 13+700                   |
| pavimentacion de calle 16 de septiembre                                          |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| b                |
|:-----------------|
| isla skedge      |
| poppy edwards    |
| layla scoffield  |
| charlotte turner |
| lexi asher       |
| alice mcmaster   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| category    |
|:------------|
| music       |
| printing    |
| photography |
| decorations |
| flowers     |
| gifts       |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| work experience   |
|:------------------|
| less than 5 years |
| 5 years or more   |
| none              |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| i      |
|:-------|
| û      |
| ü      |
| option |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| vrsta poziva            |
|:------------------------|
| otvoreni (trajni)       |
| ograničeni (trajni)     |
| otvoreni (privremeni)   |
| ograničeni (privremeni) |
| izravna dodjela         |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| ae               |
|:-----------------|
| brésil           |
| angleterre       |
| canada           |
| etats-unis       |
| norvège          |
| allemagne        |
| italie           |
| argentine        |
| cameroun         |
| thaïlande        |
| nigéria          |
| chine            |
| australie        |
| japon            |
| nouvelle-zélande |
| chili            |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| pricelist   |
|:------------|
| b           |
| c           |
| a           |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| notes                 |
|:----------------------|
| low branch            |
| single/low branch     |
| single                |
| two stem              |
| clump                 |
| heavy                 |
| low branch/rest grade |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| c                                         |
|:------------------------------------------|
| amif 2013, 21 cfr parts 175, 176, and 177 |
| ami                                       |
| nsf 5.1.8                                 |
| nsf 5.3.3                                 |
| nsf 4.1.1,###n###4.2.1.2, 4.3             |
| nsf 5.2.1                                 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| vertex 1        |
|:----------------|
| jaykannan       |
| pinkshadez50    |
| yenimedya       |
| nathanmart      |
| cbre_emea       |
| etondigital     |
| iamtrendbot     |
| ashishkaushik83 |
| rhour           |
| nicole_bedard   |
| hussainalhilli  |
| pingwifi        |
| delaina__j      |
| naturaway947    |
| timsportal      |
| lottebalnave    |
| scotteblin      |
| spk_search      |
| stepped__leader |
| whereisandra    |
| rf_jenkins      |
| walleneuroauto  |
| disruptcyprus   |
| yaseminelmastwt |
| stanleysuen     |
| kingdomof_s     |
| upcomingninja   |
| komi_88         |
| samij8721       |
| hsc10           |
| tortugared      |
| holoxica        |
| content3six0    |
| ibitian         |
| sosvnews        |
| the_inno_daily  |
| iurialencar     |
| alx_blog        |
| enjore_com      |
| nictaranto      |
| ugdhar          |
| soundclashfever |
| ultramiami2017  |
| pubnub          |
| sasankadias     |
| matsbe          |
| nedpike3        |
| leodragonike    |
| acalderon52     |
| rockitlaunch    |
| careernice      |
| srossignol      |
| pierrepinna     |
| rhyska__bumbum  |
| charlesdba      |
| bkh_leung       |
| ccsdevapps      |
| thomasjlebens   |
| jamesbregenzer  |
| ihorizonuk      |
| rlghthealthy    |
| cbre            |
| turgayakar      |
| spaceoneers     |
| steveboogar     |
| sourcefusellc   |
| jacobussystems  |
| graphical_force |
| the_vc_bot      |
| digital_world_n |
| ibnmascob       |
| icurrencyplus   |
| webjobbing      |
| mlstyswitzer    |
| degersey        |
| bhumharit       |
| laertonedu      |
| bimoadip        |
| cherylferrell   |
| sjkaczmarek     |
| garretgallowey  |
| techchannel     |
| theconnectedkid |
| ajadams962      |
| ddondero        |
| shailendra_nair |
| saravrbinc      |
| scooperuk       |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| bureau                                                  |
|:--------------------------------------------------------|
| office of elementary and secondary education            |
| office of special education and rehabilitative services |
| office of federal student aid                           |
| office of vocational and adult education                |
| office of english language acquisition                  |
| departmental management                                 |
| institute of education sciences                         |
| office of postsecondary education                       |
| office of innovation and improvement                    |
| hurricane education recovery                            |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| sector/space type   |
|:--------------------|
| hotel               |
| campus              |
| k-12 school         |
| fast food           |
| institutional       |
| supermarket         |
| restaurant          |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| d                 |
|:------------------|
| elena m. adames   |
| matthew a. rieger |
| alberto milo, jr. |
| lewis v swezy     |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| b                                            |
|:---------------------------------------------|
| autumn west safe haven                       |
| milwaukee coc hmis                           |
| fy 2019-rrh-bonus                            |
| st. catherine residence                      |
| homelinc                                     |
| fy 2019 milw cnty mercy housing s+c          |
| johnston center                              |
| otp/psh milwaukee                            |
| veterans gardens                             |
| fy 2019 milwaukee county tbra ii-st. anthony |
| fy 2019 milw cnty heartland s+c              |
| community based coordinated entry 2019       |
| fy 2019-milw cnty-hf-tra-iii                 |
| rapid rehousing for youth                    |
| hope house joint th-rrh project              |
| hope house psh for families                  |
| fy 2019 milw. cnty s+c/tra                   |
| rapid rehousing for recovery-singles         |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| maanantai                        |
|:---------------------------------|
| jäävuori - kurkku - kesäkurpitsa |
| vadelmakiisseli                  |
| keksejä                          |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| vervolgvraag_ vergelijking of naar volgende vraag     |
|:------------------------------------------------------|
| is aanvoer nog bezig                                  |
| art. 42 lid 2                                         |
| betreft het grond die valt onder de bia/ria regeling! |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| Unnamed: 0                                                                    |
|:------------------------------------------------------------------------------|
| ne 7th st & 36th ave next to muni-golf course                                 |
| ne 5th pl & 39th ave at dead end on 5th pl                                    |
| ne 5th pl & 39th ave thompson estates                                         |
| 200 blk of ne 45th ter - stonewood ii                                         |
| ne 40th ave & silver springs blvd - enter thru 39th ave & silver springs blvd |
| 3700 blk of ne 8th pl. just east of marathon on 36th ave & hwy 40             |
| ne 36th ave & hwy 40 behind marathon                                          |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| travel_and_per_diem                       |
|:------------------------------------------|
| visa fees                                 |
| per diem for international missions/trips |
| per diem for domestic missions/trips      |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| c                              |
|:-------------------------------|
| bluffton village treasurer     |
| cairo village treasurer        |
| delphos city treasurer         |
| elida village treasurer        |
| harrod village treasurer       |
| lafayette village treasurer    |
| lima city treasurer            |
| spencerville village treasurer |
| amanda twp., allen county      |
| american twp, allen county     |
| auglaize twp, allen county     |
| bath twp, allen county         |
| jackson twp, allen county      |
| marion twp, allen county       |
| monroe twp, allen county       |
| perry twp, allen county        |
| richland twp, allen county     |
| shawnee twp, allen county      |
| spencer township               |
| sugar creek twp, allen county  |
| allen county council on aging  |
| allen cty children services    |
| bluffton public library        |
| delphos library                |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| 44            |
|:--------------|
| zen           |
| young         |
| zippy         |
| yandanooka    |
| yitpi         |
| wyalkatchem   |
| wedin         |
| tammarin rock |
| trojan        |
| westonia      |
| supreme       |
| scout         |
| wmaa13cali6   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| a                                                             |
|:--------------------------------------------------------------|
| - postpaid                                                    |
| minutes of use (mou) per average customer                     |
| blended arpu 4)###n###(monthly average for the period in hrk) |
| - prepaid                                                     |
| sac per gross add in hrk                                      |
| churn rate (%)                                                |
| penetration (%) 1)                                            |
| market share of customers (%)  1)                             |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| f                    |
|:---------------------|
| aoo license          |
| dvd set              |
| new upgrade plan     |
| renewal upgrade plan |
| tech sol mgmt rnw    |
| tech solution mgmt   |
| gold                 |
| gold renewal         |
| sol dev supp (inc)   |
| cd set               |
| doc. set             |
| upgrade license      |
| adobe sw assuran rnw |
| adobe sw assurance   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| group 2   |
|:----------|
| g3        |
| g5        |
| g1        |
| g2        |
| g4        |
| g6        |
| g7        |
| g8        |
| g9        |
| g10       |
| g11       |
| g12       |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| runbookname                  |
|:-----------------------------|
| getprocesses                 |
| helloworld                   |
| sample-managing-orchestrator |
| testhash                     |
| testrunbook                  |
| testsql                      |
| checkfor-events              |
| found-event                  |
| sample                       |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| place bounding box                                                                             |
|:-----------------------------------------------------------------------------------------------|
| 77.096236_23.871941  84.638873_23.871941  84.638873_30.407576  77.096236_30.407576             |
| 86.7043528_22.2644924  86.7344351_22.2644924  86.7344351_22.2876586  86.7043528_22.2876586     |
| -0.3229167_51.3428601  -0.234011_51.3428601  -0.234011_51.437266  -0.3229167_51.437266         |
| 76.84252_28.397657  77.347652_28.397657  77.347652_28.879322  76.84252_28.879322               |
| 76.7038129_30.665455  76.8492359_30.665455  76.8492359_30.795316  76.7038129_30.795316         |
| 76.262727_30.411123  76.594041_30.411123  76.594041_30.765079  76.262727_30.765079             |
| 30.7657511_2.5373432  31.536709_2.5373432  31.536709_3.1998644  30.7657511_3.1998644           |
| -58.0865631_1.831414  -53.968391_1.831414  -53.968391_5.9972545  -58.0865631_5.9972545         |
| 19.7483728_-31.4799628  19.8056667_-31.4799628  19.8056667_-31.3962328  19.7483728_-31.3962328 |
| 72.206516_27.698326  73.741376_27.698326  73.741376_28.903288  72.206516_28.903288             |
| 77.4584291_25.2654439  77.92709_25.2654439  77.92709_25.8883015  77.4584291_25.8883015         |
| 72.74484_18.845343  73.003648_18.845343  73.003648_19.502937  72.74484_19.502937               |
| -79.639319_43.403221  -78.90582_43.403221  -78.90582_43.855401  -79.639319_43.855401           |
| 85.137099_19.46377  86.377664_19.46377  86.377664_20.195729  85.137099_20.195729               |
| 50.3251128_25.5704959  50.8226344_25.5704959  50.8226344_26.3341076  50.3251128_26.3341076     |
| 80.658613_26.642291  81.110202_26.642291  81.110202_27.008048  80.658613_27.008048             |
| -3.1315162_53.3105549  -3.072142_53.3105549  -3.072142_53.360533  -3.1315162_53.360533         |
| -75.908759_45.366368  -75.3413168_45.366368  -75.3413168_45.599121  -75.908759_45.599121       |
| 30.2563496_-29.6999989  30.4512134_-29.6999989  30.4512134_-29.5328007  30.2563496_-29.5328007 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| e        |
|:---------|
| s.23.02  |
| s.02.02  |
| s.25.01  |
| s.06.02  |
| s.08.01  |
| s.12.01  |
| s.23.01  |
| s.31.01  |
| s.35.01  |
| sr.02.01 |
| sr.12.01 |
| sr.17.01 |
| sr.25.01 |
| sr.26.01 |
| s.26.02  |
| sr.26.02 |
| sr.26.04 |
| sr.26.05 |
| sr.26.06 |
| sr.27.01 |
| s.01.02  |
| s.17.01  |
| s.03.01  |
| s.06.03  |
| s.07.01  |
| s.28.01  |
| s.28.02  |
| s.15.01  |
| s.15.02  |
| s.17.02  |
| s.29.03  |
| s.29.04  |
| s.37.01  |
| s.26.01  |
| sr.26.03 |
| s.26.03  |
| s.26.04  |
| s.26.05  |
| e.01.01  |
| e.02.01  |
| e.03.01  |
| sr.01.01 |
| s.22.01  |
| s.02.01  |
| s.01.03  |
| s.22.04  |
| s.22.05  |
| s.11.01  |
| s.05.01  |
| s.04.01  |
| s.14.01  |
| s.12.02  |
| s.22.06  |
| s.25.03  |
| s.25.02  |
| s.23.04  |
| se.06.02 |
| sr.25.02 |
| sr.25.03 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| Unnamed: 0   |
|:-------------|
| rbs          |
| rtk          |
| rf4          |
| rff          |
| r1h          |
| rc9          |
| rq3          |
| rxl          |
| rmc          |
| rae          |
| rxh          |
| rxq          |
| rwy          |
| rgt          |
| rqm          |
| rfs          |
| rjr          |
| rxp          |
| rj6          |
| rn7          |
| rp5          |
| rbd          |
| rwh          |
| rjn          |
| rvv          |
| rxr          |
| rde          |
| rxc          |
| rvr          |
| rdu          |
| rr7          |
| rlt          |
| rte          |
| rp4          |
| rn3          |
| rj1          |
| rn5          |
| rcd          |
| rqx          |
| rwa          |
| ryj          |
| r1f          |
| rgp          |
| rnq          |
| rjz          |
| rax          |
| rxn          |
| rr8          |
| rj2          |
| rem          |
| r1k          |
| rwf          |
| r0a          |
| rpa          |
| raj          |
| rxf          |
| rd8          |
| rm1          |
| rvj          |
| rnn          |
| rap          |
| rvw          |
| rgn          |
| rns          |
| rbz          |
| rjl          |
| rtf          |
| rx1          |
| rth          |
| rw6          |
| rd3          |
| rhu          |
| rhw          |
| ref          |
| rh8          |
| ral          |
| ra2          |
| rd1          |
| rm3          |
| rnz          |
| rcu          |
| rhq          |
| rk5          |
| rh5          |
| rtr          |
| r0b          |
| rjc          |
| rj7          |
| rbn          |
| rwj          |
| rtp          |
| rmp          |
| rbv          |
| ren          |
| rna          |
| rtd          |
| rqw          |
| rcx          |
| rfr          |
| rdz          |
| rpy          |
| rl4          |
| rxw          |
| ra9          |
| rwd          |
| rrv          |
| rhm          |
| rrk          |
| ra7          |
| rkb          |
| rtg          |
| rwe          |
| rtx          |
| rje          |
| rk9          |
| rbk          |
| rww          |
| rwg          |
| rgr          |
| ryr          |
| rke          |
| rbl          |
| rwp          |
| rrf          |
| rlq          |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| městské obvody   |
|:-----------------|
| umob03           |
| umob04           |
| umob05           |
| umob06           |
| umob07           |
| umob08           |
| umob09           |
| umob10           |
| umob11           |
| umob12           |
| umob13           |
| umob14           |
| umob15           |
| umob16           |
| umob17           |
| umob18           |
| umob19           |
| umob20           |
| umob21           |
| umob22           |
| umob23           |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| b                    |
|:---------------------|
| california           |
| new jersey           |
| washington           |
| new york             |
| nevada               |
| massachusetts        |
| virginia             |
| maryland             |
| alaska               |
| illinois             |
| minnesota            |
| texas                |
| connecticut          |
| oregon               |
| district of columbia |
| georgia              |
| delaware             |
| pennsylvania         |
| rhode island         |
| arizona              |
| colorado             |
| michigan             |
| kansas               |
| north carolina       |
| wisconsin            |
| florida              |
| new hampshire        |
| indiana              |
| nebraska             |
| iowa                 |
| utah                 |
| ohio                 |
| oklahoma             |
| missouri             |
| louisiana            |
| tennessee            |
| new mexico           |
| south carolina       |
| kentucky             |
| arkansas             |
| idaho                |
| north dakota         |
| vermont              |
| alabama              |
| south dakota         |
| maine                |
| mississippi          |
| montana              |
| west virginia        |