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:
| l   |
|:----|
| u   |
| r   |
| d   |
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     |
|:------|
| fin14 |
| fin15 |
| fin16 |
| fin17 |
| fin18 |
| fin19 |
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:
| exportación         |
|:--------------------|
| no                  |
| si                  |
| si (frutas frescas) |
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:
| land use     |
|:-------------|
| snow and ice |
| forest       |
| grassland    |
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:
| fy      |
|:--------|
| 2015/16 |
| 2016/17 |
| 2017/18 |
| 2018/19 |
| 2019/20 |
| 2020/21 |
| 2021/22 |
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:
| lodging     |
|:------------|
| only dinner |
| fb          |
| bb          |
| only lunch  |
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:
| description                                 |
|:--------------------------------------------|
| engineering specialist currently unassigned |
| sonya.mansfield                             |
| troy.danielson                              |
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:
| zaporka        |
|:---------------|
| 12488bron      |
| 03160orion     |
| 36918minolovac |
| 31415vražićak  |
| 01010bakalar   |
| 45454banana    |
| 13017brate     |
| 33333maslina   |
| 90912tkd       |
| 05121pokemon   |
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:
| gender   |
|:---------|
| f        |
| m        |
| x        |
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:
| boiler_primary_fuel   |
|:----------------------|
| natural gas           |
| none                  |
| propane               |
| fuel oil              |
| electricity           |
| off-site steam        |
| off-site hot water    |
| other                 |
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:
| course #                                                                                                              |
|:----------------------------------------------------------------------------------------------------------------------|
| remove cmac 2053 and 3001 from core requirements for ba communication studies and bs strategic communication degrees  |
| change prad 3553 course description and prerequisite                                                                  |
| thea 1011                                                                                                             |
| thea 2010                                                                                                             |
| thea 2020                                                                                                             |
| thea 4001                                                                                                             |
| mus 1503                                                                                                              |
| remove musp 1111 from bme and replace with musp 1112                                                                  |
| changing prereqs for mus 1513                                                                                         |
| remove rs 4822 and rs 436v.  add rsu 4112.  correct 4613 and 4622 to appropriate course titles on 8 sem. degree plan. |
| psy 2013 (intro to) will replace computer course requirement.                                                         |
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                             |
|:------------------------------|
| bank of maharashtra           |
| canara bank                   |
| central bank of india         |
| indian bank                   |
| indian overseas bank          |
| punjab and sindh bank         |
| punjab national bank          |
| state bank of india           |
| uco bank                      |
| union bank of india           |
| psbs - sub total              |
| axis bank                     |
| bandhan bank                  |
| catholic syrian bank          |
| city union bank               |
| development credit bank       |
| dhan lakshmi bank             |
| federal bank ltd.             |
| hdfc bank                     |
| icici bank                    |
| idbi bank                     |
| idfc first bank               |
| indusind bank limited         |
| jammu and kashmir bank        |
| karnataka bank limited        |
| karur vysya bank ltd.         |
| kotak mahindra bank           |
| lakshmi vilas bank            |
| ratnakar bank ltd. (rbl)      |
| south indian bank             |
| standard chartered bank       |
| tamilnadu mercantile bank     |
| yes bank                      |
| private bank - sub total      |
| commercial banks sub total    |
| mgb                           |
| mpgb                          |
| rrbs - sub total              |
| dccb & apex bank              |
| co-operative bank - sub total |
| au small finance bank         |
| equitas small finance bank    |
| esaf                          |
| fincare small finance bank    |
| jana small finance bank       |
| suryoday small finance bank   |
| ujjivan small finance bank    |
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:
|   spoken language - other non-english  |
|---------------------------------------:|
|                             0.00462287 |
|                             0.0413207  |
|                             0.0125196  |
|                             0.0307028  |
|                             0.0311355  |
|                             0.00705882 |
|                             0.00488478 |
|                             0.008      |
|                             0.01294    |
|                             0.0406091  |
|                             0.00694858 |
|                             0.014214   |
|                             0.0216031  |
|                             0.039666   |
|                             0.00872523 |
|                             0.0498     |
|                             0.0112959  |
|                             0.0330184  |
|                             0.00757006 |
|                             0.0521203  |
|                             0.0473317  |
|                             0.00429799 |
|                             0.0391073  |
|                             0.00436047 |
|                             0.00337113 |
|                             0.021251   |
|                             0.0174601  |
|                             0.168011   |
|                             0.258065   |
|                             0.0115757  |
|                             0.0118041  |
|                             0.0529501  |
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:
| base link                                                    |
|:-------------------------------------------------------------|
| http://azure.microsoft.com/pricing/details/data-transfers/   |
| http://azure.microsoft.com/pricing/details/hdinsight/        |
| http://azure.microsoft.com/pricing/details/cloud-services/   |
| http://azure.microsoft.com/pricing/details/virtual-machines/ |
| http://azure.microsoft.com/pricing/details/biztalk-services/ |
| http://azure.microsoft.com/pricing/details/expressroute/     |
| http://azure.microsoft.com/pricing/details/virtual-network/  |
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:
| kommune         |
|:----------------|
| enebakk         |
| fet             |
| frogn           |
| nittedal        |
| oppegård        |
| rælingen        |
| ski             |
| ullensaker      |
| vestby          |
| ås              |
| evje og hornnes |
| lillesand       |
| risør           |
| drammen         |
| hol             |
| lier            |
| ringerike       |
| øvre eiker      |
| vadsø           |
| bergen          |
| fjell           |
| os (hordaland)  |
| giske           |
| haram           |
| vestnes         |
| ålesund         |
| bodø            |
| narvik          |
| rana            |
| vefsn           |
| vestvågøy       |
| værøy           |
| gjøvik          |
| gran            |
| lillehammer     |
| sel             |
| sør-aurdal      |
| vestre toten    |
| oslo            |
| karmøy          |
| klepp           |
| sandnes         |
| stavanger       |
| strand          |
| luster          |
| selje           |
| notodden        |
| porsgrunn       |
| skien           |
| tinn            |
| nordreisa       |
| tromsø          |
| stjørdal        |
| trondheim       |
| verdal          |
| kristiansand    |
| mandal          |
| tønsberg        |
| askim           |
| eidsberg        |
| fredrikstad     |
| halden          |
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:
| abstract           |
|:-------------------|
| 2/1/1987+          |
| 10/1/2001-8/1/2014 |
| 4/1/1998+          |
| 4/1/2005-8/1/2012  |
| 1/1/1997+          |
| 1/1/2006-12/1/2013 |
| 1/1/1993+          |
| 4/1/2006-1/1/2013  |
| 5/1/2003-9/1/2014  |
| 8/1/2005-12/1/2013 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| group 1   |
|:----------|
| g2        |
| g3        |
| g4        |
| g5        |
| g6        |
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:
| est. qtr   |
|:-----------|
| q1         |
| q3         |
| q4         |
| q2         |
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:
| discipline          |
|:--------------------|
| management          |
| accounting          |
| information systems |
| marketing           |
| finance             |
| finance/economics   |
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:
| cambodian             |
|:----------------------|
| <11                   |
| 3.0000000000000001e-3 |
| 1.7000000000000001e-2 |
| 2e-3                  |
| 1.2999999999999999e-2 |
| 8.0000000000000002e-3 |
| 4.0000000000000001e-3 |
| 6.0000000000000001e-3 |
| 5.6000000000000001e-2 |
| 1.0999999999999999e-2 |
| 1.2e-2                |
| 2.5000000000000001e-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:
| entry code   |
|:-------------|
| cont         |
| trre         |
| tr           |
| ftf          |
| ftg          |
| re           |
| ftgu         |
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:
| personnel number   |
|:-------------------|
| hugo               |
| markus             |
| christophe         |
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:
| meilenstein   |
|:--------------|
| meilenstein 2 |
| meilenstein 3 |
| meilenstein 4 |
| meilenstein 5 |
| meilenstein 6 |
| meilenstein 7 |
| meilenstein 8 |
| meilenstein 9 |
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:
| region                           |
|:---------------------------------|
| central and southern asia        |
| eastern and south-eastern asia   |
| europe and northern america      |
| latin america and the caribbean  |
| northern africa and western asia |
| oceania                          |
| sub-saharan africa               |
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:
| number   |
|:---------|
| 3rd      |
| 4th      |
| 5th      |
| 6th      |
| 7th      |
| 8th      |
| 9th      |
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:
| docket number   |
|:----------------|
| 2015-171-lng    |
| 2010-85-lng     |
| 2010-111-lng    |
| 2016-205-lng    |
| 2017-161-lng    |
| 2011-115-lng    |
| 2011-128-lng    |
| 2015-63-lng     |
| 2014-92-lng     |
| 2018-137-lng    |
| 2016-34-lng     |
| 2013-121-lng    |
| 2012-97-lng     |
| 2012-99-lng     |
| 2011-162-lng    |
| 2018-3-lng      |
| 2013-30-lng     |
| 2013-42-lng     |
| 2018-15-lng     |
| 2010-160-lng    |
| 2016-108-lng    |
| 2011-145-lng    |
| 2019-133-lng    |
| 2011-161-lng    |
| 2020-14-lng     |
| 2012-54-lng     |
| 2012-100-lng    |
| 2019-156-lng    |
| 2010-161-lng    |
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                          |
|:---------------------------|
| rutting\transverse profile |
| faulting                   |
| cracking                   |
| geometrics                 |
| videolog images            |
| videolog viewer            |
| image server storage       |
| pavement type extraction   |
| shoulder presence\width    |
| lane width                 |
| pavement width             |
| sign collection            |
| special pavement markings  |
| sidewalks presence\width   |
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   |
|:----------|
| g10       |
| g15       |
| g1        |
| g2        |
| g4        |
| g5        |
| g8        |
| g11       |
| g13       |
| g14       |
| g20       |
| g3        |
| g6        |
| g7        |
| g9        |
| g12       |
| g16       |
| g17       |
| g18       |
| g19       |
| 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       |
| g75       |
| g76       |
| g77       |
| g78       |
| g79       |
| g80       |
| g81       |
| g82       |
| g83       |
| g84       |
| g85       |
| g86       |
| g87       |
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:
| dział      |
|:-----------|
| inż.-tech. |
| marketing  |
| księgowość |
| admin.     |
| reklama    |
| magazyn    |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| sector                                |
|:--------------------------------------|
| processed foods                       |
| distribution services                 |
| housewares                            |
| e-commerce                            |
| tourism and travel related services   |
| fashion accessories                   |
| game and animation                    |
| it services/software development      |
| packaging products                    |
| furnitures and fixtures               |
| metal manufactures                    |
| construction and engineering services |
| forest-based products                 |
| marine and aquaculture products       |
| transport                             |
| minerals                              |
| audio visual                          |
| business process outsourcing          |
| machinery and parts                   |
| chemicals                             |
| wellness services                     |
| coconut products                      |
| fresh foods                           |
| education                             |
| garments and textiles                 |
| cosmetics and personal care           |
| games and sports                      |
| flowers and plants                    |
| footwear                              |
| beverages                             |
| pharmaceutical products               |
| electronics                           |
| entertainment products                |
| artworks and collectibles             |
| design services                       |
| live animals                          |
| pulps/fibers                          |
| health supplements                    |
| publishing and printing               |
| electrical products                   |
| toys                                  |
| homecare                              |
| photography                           |
| paper products                        |
| construction materials                |
| rubber                                |
| tobacco and tobacco products          |
| printed and published materials       |
| twines/cordages/nets                  |
| carageenan                            |
| pet food/animal feed                  |
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:
| original company               |
|:-------------------------------|
| medline complete               |
| omnifile                       |
| periodicals archive online     |
| vocational studies premier     |
| hospitality & tourism complete |
| wiley online library           |
| rsc                            |
| science direct online          |
| acs                            |
| cambridge university press     |
| springerlink(soja)             |
| oxford university press        |
| springer                       |
| biomedcentral                  |
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:
| vendedor   |
|:-----------|
| beto       |
| carlos     |
| eduardo    |
| evelin     |
| marisa     |
| saulo      |
| tania      |
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:
| grau de instrução    |
|:---------------------|
| ensino fundamental 1 |
| ensino médio         |
| superior             |
| ensino fundamental 2 |
| analfabeto           |
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:
| m           |
|:------------|
| $1000-$1500 |
| $1500-$2000 |
| $2000-$3000 |
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:
| event_classification   |
|:-----------------------|
| continental champs     |
| platinum               |
| challenge              |
| world_tour             |
| other_event            |
| wtgf                   |
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   |
|:----------|
| g2        |
| g3        |
| g4        |
| g5        |
| g6        |
| g7        |
| g8        |
| g9        |
| g10       |
| g11       |
| g12       |
| g13       |
| g14       |
| g15       |
| g16       |
| g17       |
| g18       |
| g19       |
| g20       |