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:
| раздел                                                                                        |
|:----------------------------------------------------------------------------------------------|
| приложение 1. исходные данные_ балльная шкала_ весовые коэффициенты                           |
| ii. расчет индекса готовности                                                                 |
| приложение 4. индикаторы                                                                      |
| i. общие положения                                                                            |
| приложение 3. балльная шкала отдельных показетелей                                            |
| приложение 5. сведения о несоблюдении времени включения оборудования_ неучастии в опрч и арчм |
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:
| empresa produtora           |
|:----------------------------|
| syngenta                    |
| monsanto                    |
| dow agrosciences            |
| dupont                      |
| monsanto e dow agrosciences |
| bayer                       |
| basf e embrapa              |
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:
| top words in tweet in g4   |
|:---------------------------|
| americanair                |
| delta                      |
| program                    |
| miles                      |
| wallethub                  |
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:
| problema a resolver   |
|:----------------------|
| pobreza               |
| educación             |
| corrupción            |
| desempleo             |
| inseguridad           |
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:
| top urls in tweet in g2                                                                             |
|:----------------------------------------------------------------------------------------------------|
| https://twitter.com/socialbakers/status/817357304777445376                                          |
| http://www.adweek.com/socialtimes/?p=648856                                                         |
| http://digitalage.com.tr/socialbakersdan-2017ye-dair-sosyal-medya-tahminleri/                       |
| https://www.socialbakers.com/blog/2650-how-marketers-joined-the-rogue-one-social-media-conversation |
| http://www.socialbakers.com/                                                                        |
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:
| bosque no bosque 2013            |
|:---------------------------------|
| bosque                           |
| cuerpos de agua                  |
| humedales                        |
| matorral                         |
| matorral arborescente            |
| nieves eternas y glaciares       |
| plantación                       |
| pradera                          |
| terrenos agricolas               |
| áreas desprovistas de vegetación |
| áreas urbanas e industriales     |
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:
| column4                 |
|:------------------------|
| debit                   |
| (reqired) account  code |
| (6 digits)              |


label: (reqired) account  code
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:
| resource category    |
|:---------------------|
| pf                   |
| et                   |
| rd                   |
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:
| type of filing  (e.g. 30-day filing_ instant mirroring_ or other)   |
|:--------------------------------------------------------------------|
| instant mirroring                                                   |
| other                                                               |
| replacement access tariff                                           |
| instant mirroring/concurrence                                       |
| 30-day filing                                                       |
| informational                                                       |
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:
|       taiwan |
|-------------:|
| -0.013411    |
|  0.0202623   |
|  0.00856946  |
| -0.00852766  |
|  0.00212679  |
| -0.0220967   |
| -0.021479    |
| -0.00577299  |
| -0.0448775   |
| -0.0025073   |
|  0.0242752   |
| -0.011497    |
| -0.026254    |
|  0.00223518  |
|  0.00303168  |
|  0.00277932  |
|  0.00502356  |
|  0.05126     |
| -0.0189861   |
|  0.0138049   |
| -0.00204418  |
| -0.0221356   |
| -0.0065545   |
|  0.00384301  |
| -0.0121625   |
|  0.0228411   |
|  0.00137473  |
|  0.0025113   |
| -0.000501002 |
| -0.00492948  |
| -0.0401084   |
|  0.0170717   |
|  0.0028088   |
|  0.00884149  |
| -0.0118749   |
|  0.0136425   |
|  0.00267174  |
| -0.000566233 |
| -0.00309938  |
| -0.0109986   |
| -0.00608437  |
|  0.00632567  |
|  0.00757013  |
|  0.017911    |
| -0.000757875 |
|  0.00468261  |
| -0.00817278  |
| -0.00744589  |
| -0.00433434  |
|  0.00508991  |
| -0.0035982   |
|  0.0178888   |
| -0.000295644 |
|  0.0169224   |
|  0.0226832   |
| -0.00309637  |
| -0.00221856  |
| -0.00613049  |
| -0.0220844   |
|  0.00431401  |
|  0.00423039  |
|  0.0254051   |
|  0.0279674   |
|  0.0103292   |
| -0.0129013   |
|  0.00631916  |
|  0.00646327  |
|  0.0169218   |
| -0.00332206  |
| -0.0332713   |
|  0.00285768  |
|  0.0102211   |
| -0.0124172   |
| -0.00878582  |
| -0.00645202  |
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   |
|:----|
| kpl |
| m   |
| kos |
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                                                                              |
|:------------------------------------------------------------------------------------------|
| netbackup opt shared storage opt xplat 1 drive onpremise standard perpetual license       |
| netbackup std client xplat 1 server onpremise standard perpetual license                  |
| netbackup ent client wls 1 server hardware tier 2 onpremise standard perpetual license    |
| netbackup ent server wls 1 server hardware tier 1 onpremise standard perpetual license    |
| netbackup opt library based tape drive xplat 1 drive onpremise standard perpetual license |
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:
| top words in tweet in g10   |
|:----------------------------|
| mazda_uk                    |
| shiyuu1207                  |
| japan                       |
| jdm                         |
| miata                       |
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:
| 銷售地區   |
|:-----------|
| 北區       |
| 中區       |
| 南區       |
| 東區       |
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:
| situación actual rpc   |
|:-----------------------|
| protegido              |
| público                |
| protección en trámite  |
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:
| columna24                          |
|:-----------------------------------|
| 7815 lic.. ricardo azuela espinoza |
| 7816 lic.. ricardo azuela espinoza |
| 7643 lic.. ricardo azuela espinoza |
| 7650 lic.. ricardo azuela espinoza |
| 7059 lic.. ricardo azuela espinoza |
| 7651 lic.. ricardo azuela espinoza |
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:
| funktionsbeschreibung   |
|:------------------------|
| geschäftsführerin       |
| malerlehrling           |
| lohn                    |
| kundenmaler             |
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                 |
|:------------------|
| sh                |
| hmis              |
| ph                |
| sso               |
| joint th & ph-rrh |
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:
| address                         |
|:--------------------------------|
| 2720 8th st sw                  |
| 711 8th st se                   |
| 404 8th st sw                   |
| 1400 northridge cir ne          |
| 301 center place sw_ ste f      |
| 523 bass pro dr nw_ ste 915     |
| 406 34th ave sw                 |
| 3160 8th st sw                  |
| 2951 adventureland dr nw        |
| 739 bass pro dr nw_ ste 565     |
| 501 bass pro dr nw_ ste 415     |
| 600 1st ave n                   |
| 101 8th st sw                   |
| 3053 8th st sw_ ste 500         |
| 771 bass pro dr nw_ ste 220     |
| 3053 8th st sw_ ste 200         |
| 3200 adventureland dr nw        |
| 1599 6th ave se                 |
| 1798 8th ave se                 |
| 3280 8th st sw                  |
| 1050 1st ave s                  |
| 3418 8th st sw_ #4              |
| 3053 8th st sw_ #100            |
| 1625 adventureland dr nw_ ste b |
| 602 5th ave sw                  |
| 8267 ne 46th ave                |
| 1005 8th st sw                  |
| 140 8th st se                   |
| 0001 prairie fire nw            |
| 611 bass pro dr nw_ #800        |
| 509 8th st se                   |
| 3231 adventureland dr nw        |
| 550 36th ave sw_ ste p          |
| 2639 adventureland dr           |
| 614 8th st sw                   |
| 370 36th ave sw                 |
| 545 bass pro dr nw_ #620        |
| 435 adventureland dr ne         |
| 5697 56th st ne                 |
| 500 bass pro dr nw              |
| 480 bass pro dr nw              |
| 1402 19th st se                 |
| 567 bass pro dr nw_ #300        |
| 708 1st ave s                   |
| 107 6th st sw                   |
| 1427 34th ave sw                |
| 160 adventureland dr nw_ ste h  |
| 301 8th st sw                   |
| 2023 adventureland dr nw        |
| 120 adventureland dr ne         |
| 108 8th st sw                   |
| 2437 adventureland dr nw_ #a    |
| 3515 adventureland dr           |
| 907 8th st sw                   |
| 2414 14th st sw                 |
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:
| difficulty obtaining?        |
|:-----------------------------|
| 5 very easy                  |
| 2 somewhat difficult         |
| 4 somewhat easy              |
| 3 neither difficult nor easy |
| 1 very difficult             |
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        |
|:------------------|
| cooldrinks        |
| cleaning material |
| food product      |
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    |
|:-----|
| o-3  |
| o-4  |
| o-5  |
| o-6  |
| w-4  |
| o-8  |
| o-9  |
| o-10 |
| w-2  |
| e-4  |
| e-5  |
| e-6  |
| e-7  |
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:
| top hashtags in tweet in g3   |
|:------------------------------|
| digitalmosaic                 |
| realisticmedicine             |
| innovators                    |
| opendata                      |
| isd                           |
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      |
|:-------|
| scc    |
| us     |
| csc    |
| fp     |
| np     |
| ucsc   |
| school |
| bc     |
| safra  |
| scgs   |
| noble  |
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                |
|:--------------------------|
| ashc                      |
| auto door closers         |
| ecm                       |
| freezer                   |
| refrigerator              |
| evaporator fan            |
| led case lighting         |
| led cooler space lighting |
| controls                  |
| strip curtains            |
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:
| abbrebiazione        |
|:---------------------|
| msci usa             |
| dow jones            |
| nasdaq 100           |
| s&p500               |
| dax                  |
| uk                   |
| italia               |
| north america        |
| india                |
| estern europe        |
| cina                 |
| hongkong             |
| brasile              |
| far east ex-japan    |
| russia               |
| korea                |
| turchia              |
| gold mining          |
| oro                  |
| sx600 auto           |
| sx600 chimici        |
| sx600  costruzioni   |
| sx600 alimentari     |
| sx600 utilties       |
| sx600 banks          |
| sx600 basic resource |
| sx600 industrial     |
| sx600 insurance      |
| sx600 oil & gax      |
| sx personal & house  |
| sx600 tecnology      |
| sx600 telecom        |
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   |
|:----|
| pk  |
| dz  |
| se  |
| bx  |
| ea  |
| cs  |
| ct  |
| ro  |
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:
| top words in tweet in g7   |
|:---------------------------|
| dzapatillas                |
| marianoheller              |
| ni                         |
| pañales                    |
| use                        |
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:
| we need rural roads that can move agricultural products and farm equipment without being crowded by commuters  :how much do you agree or disagree with the following statements. we need to invest more in transportation because…    |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| agree                                                                                                                                                                                                                                 |
| neutral                                                                                                                                                                                                                               |
| strongly agree                                                                                                                                                                                                                        |
| disagree                                                                                                                                                                                                                              |
| strongly disagree                                                                                                                                                                                                                     |
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:
| f2_user_id   |
|:-------------|
| asmal502     |
| cwebe509     |
| jcrews02     |
| abuck500     |
| tcook009     |
| bmcco505     |
| mdiln500     |
| rmyain01     |
| opak0500     |
| jcohen03     |
| pnarb500     |
| ssmit600     |
| mbant504     |
| akize500     |
| sdanne01     |
| knorm500     |
| gkufuo01     |
| asingl03     |
| cwatki03     |
| rchan519     |
| cboll502     |
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:
| função              |
|:--------------------|
| vigilante           |
| vigilante lider     |
| servente de limpeza |
| servicos gerais     |
| porteiro            |
| zelador             |
| inspetor            |
| recepcionista       |
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:
| unit title                               |
|:-----------------------------------------|
| entrepreneurial skills                   |
| employability skills                     |
| environmental literacy                   |
| occupational safety and health practices |
| technical drawing                        |
| engineering mathematics                  |
| application of scientific principles     |
| workshop technology                      |
| basic scaffold sketches                  |
| scheduling scaffold works                |
| scaffolds erection and dismantling       |
| scaffolds integrity                      |
| safety compliance                        |
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                         |
|:--------------------------|
| overtime                  |
| pension fund contribution |
| allowances                |
| housing susbsidy          |


label: housing susbsidy
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       |
|:--------|
| rosdahl |
| mccann  |
| stacey  |
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    |
|:-----|
| j1   |
| j2   |
| j3   |
| j4   |
| j6   |
| j9   |
| u1   |
| u2   |
| u3   |
| u4   |
| u5   |
| u6   |
| u7   |
| u8   |
| u9   |
| u10  |
| u11  |
| c1   |
| c2   |
| c3   |
| c4   |
| c5   |
| c6   |
| c7   |
| c8   |
| c9   |
| c10  |
| c11  |
| c12  |
| c13  |
| c14  |
| c15  |
| c16  |
| c17  |
| c18  |
| c19  |
| c20  |
| c21  |
| c22  |
| c23  |
| c24  |
| c25  |
| c26  |
| c27  |
| c28  |
| c29  |
| c30  |
| c31  |
| c32  |
| c33  |
| c34  |
| c35  |
| c36  |
| c37  |
| c38  |
| c39  |
| c40  |
| c41  |
| c42  |
| c43  |
| c44  |
| c45  |
| c46  |
| c47  |
| c48  |
| c49  |
| c50  |
| c51  |
| c52  |
| c53  |
| c54  |
| c55  |
| c101 |
| d1   |
| d2   |
| d3   |
| d4   |
| d5   |
| d6   |
| d7   |
| d8   |
| d9   |
| d10  |
| h1   |
| h2   |
| h3   |
| h4   |
| l1   |
| l2   |
| l3   |
| l4   |
| r1   |
| r2   |
| r3   |
| r4   |
| r5   |
| r6   |
| r7   |
| r8   |
| r9   |
| r10  |
| r11  |
| r12  |
| r13  |
| r14  |
| r15  |
| r16  |
| r17  |
| r18  |
| r19  |
| r20  |
| r23  |
| r24  |
| r25  |
| r26  |
| r27  |
| r28  |
| r29  |
| r30  |
| r31  |
| r32  |
| r34  |
| r35  |
| r36  |
| r37  |
| r38  |
| r39  |
| r40  |
| r41  |
| r42  |
| r43  |
| r44  |
| r45  |
| r46  |
| r49  |
| s1   |
| s2   |
| t1   |
| t2   |
| t3   |