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:
| Unnamed: 0                                      |
|:------------------------------------------------|
| composter (hand built + labour)                 |
| composter (store bought)                        |
| raised planter box wooden (6ft x 3ft x 2.5 ft)  |
| wooden raised bed and soil (6ft x 3ft x 1.5 ft) |
| fabric pots                                     |
| caja raised beds                                |
| deer fencing                                    |
| garden gloves                                   |
| garden hose                                     |
| hose nozzle                                     |
| plastic storage totes, with lids                |
| seeds for garden                                |
| shovels / rakes / hoes                          |
| trowels                                         |
| water timer                                     |
| watering cans                                   |
| wheelbarrow                                     |
| drip irrigation                                 |
| soaker hose                                     |
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           |
|:------------|
| fall 2003   |
| fall 2004   |
| fall 2005   |
| fall 2006 1 |
| fall 2007 2 |
| fall 2008   |
| fall 2009   |
| fall 2010   |
| fall 2011   |
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 1   |
|:----------|
| g3        |
| g4        |
| g5        |
| g6        |
| g7        |
| g8        |
| g9        |
| g10       |
| g11       |
| g12       |
| g13       |
| g14       |
| g15       |
| g16       |
| g17       |
| g18       |
| g19       |
| g20       |
| g21       |
| g22       |
| g23       |
| g24       |
| g25       |
| g26       |
| g27       |
| g28       |
| g29       |
| g30       |
| g31       |
| g32       |
| g33       |
| g34       |
| g35       |
| g36       |
| g37       |
| g38       |
| g39       |
| g40       |
| g41       |
| g42       |
| g43       |
| g44       |
| g45       |
| g46       |
| g47       |
| g48       |
| g49       |
| g50       |
| g51       |
| g52       |
| g53       |
| g54       |
| g55       |
| g56       |
| g57       |
| g58       |
| g59       |
| g60       |
| g61       |
| g62       |
| g63       |
| g64       |
| g65       |
| g66       |
| g67       |
| g68       |
| g69       |
| g70       |
| g71       |
| g72       |
| g73       |
| g74       |
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:
| fruit: ratio: height/ diameter   |
|:---------------------------------|
| small                            |
| small medium                     |
| small to medium                  |
| medium                           |
| large                            |
| large to very large              |
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:
| exhibitor                      |
|:-------------------------------|
| miss jonee phillips            |
| mrs deborah mulroney           |
| mr & ms b & k fallon & doyle   |
| mr chris  ravenhill            |
| mrs maureen  foss              |
| mr brett fallon                |
| mr & mrs p & c richards        |
| mr & mrs gd & jl smith         |
| mr michael holland             |
| mrs sue wiltshire              |
| mrs denise durrant             |
| mrs genine ashby               |
| ms larissa simmonds            |
| rivergum rise alpacas          |
| miss isabella  elliot-shircore |
| mrs verna townsend             |
| mrs elizabeth benwell          |
| mrs angela bartels             |
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   |
|:-------------|
| mtd          |
| 1 m          |
| 3 m          |
| 6 m          |
| ytd          |
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:
| area name              |
|:-----------------------|
| glenwood               |
| brooks                 |
| wainwright             |
| vauxhall               |
| empress                |
| red deer               |
| didsbury               |
| lethbridge             |
| provost                |
| wetaskiwin             |
| stavely                |
| fort macleod           |
| drayton valley         |
| swan hills             |
| seebe                  |
| medicine hat           |
| grande prairie         |
| peace river            |
| cold lake              |
| athabasca              |
| abraham lake           |
| alliance               |
| calgary                |
| fort mcmurray          |
| airdrie                |
| fort saskatchewan      |
| strathmore             |
| edmonton               |
| rainbow lake           |
| lake wabamun           |
| hanna                  |
| grande cache           |
| sheerness              |
| caroline               |
| high prairie           |
| valleyview             |
| athabasca/lac la biche |
| wscc                   |
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:
| имя       |
|:----------|
| яков      |
| дмитрий   |
| алексей   |
| григорий  |
| александр |
| карина    |
| анастасия |
| елизавета |
| ирина     |
| варвара   |
| шамиль    |
| василий   |
| дария     |
| денис     |
| илья      |
| дарья     |
| ярослава  |
| олег      |
| юсиф      |
| екатерина |
| анна      |
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:
| carrier   |
|:----------|
| tslines   |
| wan hai   |
| oocl      |
| evergreen |
| sitc      |
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    |
|:-----|
| j2   |
| j1 c |
| j1   |
| j3   |
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       |
|:-----------------|
| adult numeracy   |
| language         |
| digital literacy |
| vocational       |
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:
| local/ state   |
|:---------------|
| state          |
| local          |
| regional       |
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:
| thickness   |
|:------------|
| 2pt         |
| 3pt         |
| 4pt         |
| 1.5pt       |
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:
| ci             |
|:---------------|
| met            |
| not met        |
| not applicable |
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:
| regname                        |
|:-------------------------------|
| bar-vue bucky                  |
| bar-vue butch-et               |
| maple-dell burdette dempsey-et |
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:
| product name                    |
|:--------------------------------|
| prochlorperaz mal 01/1994 cs&   |
| migergot 01/2006 cs&            |
| fluocinolone actn               |
| fluocinolone actn 09/1990 cs&   |
| triamcinolone actn              |
| triamcinolone actn 03/1993 cs&  |
| ciclopirox                      |
| calcipotriene                   |
| betamethasone val               |
| betamethasone val 07/2016 cs&   |
| halobetasol prop 07/2007 cs&    |
| anucort-hc 03/1989 cs&          |
| anucort-hc                      |
| promethegan                     |
| terconazole                     |
| doxycycline monohyd             |
| doxycycline monohyd 06/2016 cs& |
| acyclovir                       |
| acyclovir 12/2017 cs&           |
| fluticasone prop 10/2006 cs&    |
| fluticasone prop                |
| metronidazole 03/2008 cs&       |
| mometasone furoate 10/2006 cs&  |
| metronidazole                   |
| ciclopirox 09/2007 cs&          |
| halobetasol prop                |
| clobetasol prop                 |
| clobetasol prop 09/2016 cs&     |
| betamethasone dipr 10/2016 cs&  |
| desonide 07/2015 cs&            |
| desonide                        |
| fluocinonide-e                  |
| fluocinonide-e 11/2018 cs&      |
| tazarotene 02/2018 cs&          |
| nystatin                        |
| nystatin 12/2015 cs&            |
| gentamicin sulf                 |
| gentamicin sulf 01/2017 cs&     |
| mometasone furoate              |
| betamethasone dipr              |
| tazarotene                      |
| hydrocortisone val 03/2015 cs&  |
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        |
| g4        |
| g5        |
| g6        |
| g7        |
| g1        |
| g2        |
| g8        |
| g9        |
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:
| industry     |
|:-------------|
| 卸売業       |
| 機械         |
| 運輸業       |
| 情報・通信業 |
| 電気機器     |
| 化学         |
| 建設業       |
| 金融業       |
| 食料品       |
| 医薬品       |
| 医療機関     |
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:
| j                |
|:-----------------|
| fragrance juices |
| wash             |
| body care        |
| eye color        |
| lip color        |
| face color       |
| nail color       |
| facial care      |
| face care        |
| hand care        |
| bath oil         |
| sun care         |
| hair care        |
| hair treatment   |
| shampoos         |
| feminine hygiene |
| foot care        |
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:
| position title                 |
|:-------------------------------|
| analyst                        |
| investment banking analyst     |
| portfolio operations associate |
| associate                      |
| support specialist             |
| senior analyst                 |
| consultant                     |
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:
| l                                                                                                                                                                                                                                                                                                                                   |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| a) releasing of existing cross arms in the existing single side tower and reerction of modified cross arm at tower location 3 & 4.###n######n###b) replacing of existing coyote to drake acsr or existing single side tower (8 spans)###n######n###note: every day evening from 18:00 hrs to morning 9:00 hrs line will be charged. |
| quaterly maintenance work of tr-2                                                                                                                                                                                                                                                                                                   |
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:
| divison pcode   |
|:----------------|
| fj04            |
| fj03            |
| fj01            |
| fj02            |
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:
| concepto:                                           |
|:----------------------------------------------------|
| 2448052302-restaurante_cafeterias_hoteles 8.8*1000  |
| 2448052336-ss.otras actidades de ss. ncp 10*1000    |
| 2448052331-actividades inmobiliarias  10*1000       |
| 2448052323-ss.actividades postales naciona 8.8*1000 |
| 2448052306-servicios relacion.con transp.3_3*1000   |
| 2448052334-ss. mant.y rep.maquinaria y equi 10*1000 |
| 2448052340-ss actividades juridicas  6.6*1000       |
| 2448052403-las demas activ.comerciales 7_7*1000     |
| 2448052341-ss act.de ss aux dela interm fra 10*1000 |
| 2448052321-ss actv.investig.y seguridad 6.6*1000    |
| 2448052332-ss. alquiler maq.equi. ofi y c   10*1000 |
| 2448052401-droguerias_farmacias_depositos 3_3*1000  |
| 2448052106-fab.equipo medic.quirurgico 6.6*1000     |
| 2448052320-ss fin.actv.compra cartera factg.5*1000  |
| 2448052327-ss correo dist actv.posta.nal  8.8*1000  |
| 2448052305-servicios de consult.profes.6_6*1000     |
| 2448052326-ss act.arquit.ing.asse.tecnic.  6.6*1000 |
| 2448052311-ss de salud en general 6.6*1000          |
| 2448052324-ss obtencion y suminis.personal 3.3*1000 |
| 2448052337-ss.otros trabj. acondicionamient 10*1000 |
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:
| maps to oasis lcat name                                |
|:-------------------------------------------------------|
| manager_ group 3                                       |
| business and financial operations specialists_ group 3 |
| manager_ group 1                                       |
| public relations specialist                            |
| actuary                                                |
| business and financial operations specialists_ group 2 |
| administrative professional                            |
| manager_ group 4                                       |
| writer                                                 |
| engineer_ group 3                                      |
| scientists and science technicians_ group 5            |
| scientists and science technicians_ group 3            |
| engineer_ group 5                                      |
| manager_ group 2                                       |
| scientists and science technicians_ group 4            |
| operations research analyst                            |
| scientists and science technicians_ group 2            |
| engineer_ group 4                                      |
| editor                                                 |
| business and financial operations specialists_ group 4 |
| investigator                                           |
| business and financial operations specialists_ group 1 |
| statistician                                           |
| scientists and science technicians_ group 1            |
| engineer_ group 2                                      |
| engineer_ group 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:
| class code   |
|:-------------|
| ecalc        |
| esai         |
| ebam         |
| ecd1         |
| eudb         |
| euda         |
| ealt         |
| eud8         |
| ebox         |
| epai         |
| ealtb        |
| ealtw        |
| eud9         |
| eudacon      |
| ealtcon      |
| ecamcon      |
| epaicon      |
| epaiawdcon   |
| eud7con      |
| esaicon      |
| ecai         |
| eev          |
| eud7         |
| ecal         |
| eua3         |
| ecalcon      |
| eudh         |
| ecam         |
| eudd         |
| eudhb        |
| eudc         |
| eud7c        |
| euddc        |
| eudtc        |
| ecd1c        |
| ecamc        |
| eua2c        |
| eudt         |
| eudhc        |
| ebaf         |
| epaic        |
| espv         |
| eudbc        |
| estv         |
| ecd2c        |
| euacc        |
| ecms         |
| ebamc        |
| ebgdt        |
| eudac        |
| estvc        |
| eud1c        |
| ecd1con      |
| eua3c        |
| eudcc        |
| ecag         |
| eud1d        |
| ealtm        |
| eud7m        |
| eudam        |
| eudhm        |
| eud5         |
| eua2         |
| ecd2         |
| eud5con      |
| ebafcon      |
| epai awd     |
| ebamcon      |
| epap         |
| eman         |
| ebus         |
| epaim        |
| ewcm         |
| eudtm        |
| ecagm        |
| ebamm        |
| estvcon      |
| ebafm        |
| eboxc        |
| ecagcon      |
| ebuscon      |
| euac         |
| eudbcon      |
| ewcc         |
| eud7p        |
| ephev        |
| eud5dorm     |
| eua2con      |
| eud1con      |
| eudccon      |
| euddcon      |
| eudhcon      |
| eudtcon      |
| eud8con      |
| eudtd        |
| eud2d        |
| ewcccon      |
| eua4con      |
| eudsb        |
| eud1         |
| euaccon      |
| eua3con      |
| eua3d        |
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:
| departamento   |
|:---------------|
| chimaltenango  |
| sacatepequez   |
| chiquimula     |
| guatemala      |
| santa rosa     |
| huehuetenango  |
| san marcos     |
| suchitepequez  |
| el progreso    |
| quetzaltenango |
| retalhuleu     |
| escuintla      |
| zacapa         |
| baja verapaz   |
| alta verapaz   |
| jutiapa        |
| totonicapan    |
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   |
|:----------|
| g1        |
| g2        |
| g3        |
| g4        |
| g6        |
| g7        |
| g14       |
| g15       |
| g17       |
| g19       |
| g24       |
| g26       |
| g5        |
| g12       |
| g22       |
| g20       |
| g8        |
| g9        |
| g10       |
| g11       |
| g13       |
| g16       |
| g18       |
| g21       |
| g23       |
| g25       |
| g27       |
| g28       |
| g29       |
| g30       |
| g31       |
| g32       |
| g33       |
| g34       |
| g35       |
| g36       |
| g37       |
| g38       |
| g39       |
| g40       |
| g41       |
| g42       |
| g43       |
| g44       |
| g45       |
| g46       |
| g47       |
| g48       |
| g49       |
| g50       |
| g51       |
| g52       |
| g53       |
| g54       |
| g55       |
| g56       |
| g57       |
| g58       |
| g59       |
| g60       |
| g61       |
| g62       |
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:
| stagingtable        |
|:--------------------|
| sql server          |
| psb_data            |
| sql server\psb_data |
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:
| column1   |
|:----------|
| cavan     |
| clare     |
| cork      |
| donegal   |
| dublin 16 |
| dublin 24 |
| dublin 8  |
| dublin 9  |
| galway    |
| kerry     |
| kildare   |
| kilkenny  |
| laois     |
| leitrim   |
| limerick  |
| longford  |
| louth     |
| mayo      |
| meath     |
| monaghan  |
| offaly    |
| roscommon |
| sligo     |
| tipperary |
| waterford |
| westmeath |
| wexford   |
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:
| 列29    |
|:--------|
| 3:53.83 |
| 4:15.46 |
| 4:34.11 |
| 3:44.50 |
| 4:21.69 |
| 4:18.43 |
| 5:00.38 |
| 5:25.85 |
| 5:38.91 |
| 4:08.50 |
| 4:43.57 |
| 3:40.49 |
| 4:06.06 |
| 3:42.09 |
| 4:27.23 |
| 4:56.40 |
| 3:54.99 |
| 3:30.28 |
| 4:02.56 |
| 4:56.80 |
| 4:05.46 |
| 4:43.69 |
| 4:45.67 |
| 4:55.59 |
| 4:59.65 |
| 5:00.19 |
| 3:35.90 |
| 4:08.73 |
| 4:41.30 |
| 4:45.56 |
| 5:15.31 |
| 3:57.77 |
| 4:50.19 |
| 4:03.14 |
| 4:07.08 |
| 4:36.33 |
| 5:01.40 |
| 5:08.09 |
| 5:28.03 |
| 3:49.49 |
| 4:22.22 |
| 4:45.27 |
| 3:49.17 |
| 4:11.10 |
| 4:58.33 |
| 5:02.52 |
| 3:34.17 |
| 4:05.55 |
| 5:33.77 |
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              |
|:---------------|
| 1,0486 1051,64 |
| 1,0507 1053,75 |
| 1,0534 1056,45 |
| 1,0520 1055,05 |
| 1,0533 1056,35 |
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:
| list of hobbies                   |
|:----------------------------------|
| jogging_ sleeping                 |
| goofing                           |
| music_ goofing_ jogging_ sleeping |
| reading_ goofing                  |
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:
| estimated annual value   |
|:-------------------------|
| £27_543.00               |
| £0.00                    |
| £45_120.00               |
| £19_000.00               |
| £7_000.00                |
| £3_268.24                |
| £150_000.00              |
| £10_000.00               |
| £13_398.85               |
| £30_000.00               |
| £16_000.00               |
| £11_220.00               |
| £700.00                  |
| £6_000.00                |
| £2_550.00                |
| £100_000.00              |
| £40_000.00               |
| £33_000.00               |
| £37_559.15               |
| £78_090.00               |
| £29_006.00               |
| £57_110.00               |
| £18_500.00               |
| £25_700.00               |
| £3_750.00                |
| £20_000.00               |
| £10_450.00               |
| £5_000.00                |
| £46_675.00               |
| £120_000.00              |
| £18_000.00               |
| £9_600.00                |
| £16_560.00               |
| £25_000.00               |
| £6_429.42                |
| £6_775.00                |
| £60_000.00               |
| £8_500.00                |
| £11_000.00               |
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:
| part number - mileage - mech name      |
|:---------------------------------------|
| fl8205--------------same------------tt |
| ftr011---------------same-----------tt |
| motor oil---------same------------tt   |
| 31p30-------------179602----------tt   |
| 9008b1---------180918------------rg    |