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:
| b     |
|:------|
| 3 g   |
| 56 r  |
| 28 mm |
| 18 a  |
| 30 f  |
| 31 k  |
| 7 c   |
| 73 cc |
| 54 r  |
| 9 a   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| industry of employment           |
|:---------------------------------|
| banking (investment)             |
| consulting                       |
| financial services               |
| accounting                       |
| advertising and related services |
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:
| nqf endorsement   |
|:------------------|
| endorsed          |
| not endorsed      |
| witthdrawn        |


label: witthdrawn
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      |
|:-------|
| prelim |
| nov    |
| elem   |
| med    |
| aff    |
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        |
|:---------|
| johnson  |
| petersen |
| collinge |
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:
| licence     |
|:------------|
| cc by       |
| unknown     |
| cc by-nc-nd |
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 desc 2     |
|:----------------|
| 53-70 watts     |
| 45-65 watts     |
| 35-50 watts     |
| 48-90 watts     |
| 100-175 watts   |
| 176 - 300 watts |
| 301-400 watts   |
| 40-60 watts     |
| 75-100 watts    |
| 64-93w          |
| 50-72w          |
| 68-129w         |
| 40w             |
| 25w             |
| 28w             |
| 32w             |
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                          |
|:---------------------------|
| 10-may-2021 to 14-may-2021 |
| 17-may-2021 to 21-may-2021 |
| 24-may-2021 to 28-may-2021 |
| 31-may-2021 to 04-jun-2021 |
| 07-jun-2021 to 11-jun-2021 |
| 14-jun-2021 to 18-jun-2021 |
| 21-jun-2021 to 25-jun-2021 |
| 28-jun-2021 to 02-jul-2021 |
| 05-jul-2021 to 09-jul-2021 |
| 12-jul-2021 to 16-jul-2021 |
| 19-jul-2021 to 23-jul-2021 |
| 26-jul-2021 to 30-jul-2021 |
| 02-aug-2021 to 06-aug-2021 |
| 09-aug-2021 to 13-aug-2021 |
| 16-aug-2021 to 20-aug-2021 |
| 23-aug-2021 to 27-aug-2021 |
| 30-aug-2021 to 03-sep-2021 |
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:
| client   |
|:---------|
| client b |
| client c |
| client d |
| client e |
| client f |
| client g |
| client h |
| client i |
| client j |
| client a |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| t                                                                                       |
|:----------------------------------------------------------------------------------------|
| entrances are obvious and logical in relation to likely points of arrival on site       |
| the external materials and detailing appear to be of high quality and are maintainable  |
| the external colours and textures seem appropriate and attractive for the local setting |
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:
| 2009 winner alliance   |
|:-----------------------|
| upa                    |
| others                 |
| nda                    |
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/i/web/status/813707288150163456 |
| https://twitter.com/i/web/status/810832450481950720 |
| https://twitter.com/i/web/status/812285484802211840 |
| https://twitter.com/i/web/status/812614000987033600 |
| https://twitter.com/i/web/status/813445587555192832 |
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   |
|:----|
| c   |
| d   |
| e   |
| f   |
| g   |
| h   |
| i   |
| j   |
| k   |
| l   |
| m   |
| n   |
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:
| specifični cilj/mjera   |
|:------------------------|
| 3d1                     |
| 10a2                    |
| 6c1                     |
| 9a2                     |
| 3d2                     |
| 9a1                     |
| 7i1                     |
| 9a3                     |
| 1b1                     |
| 4c2                     |
| 6ii2                    |
| 6ii1                    |
| ta1                     |
| 6c2                     |
| 3a2                     |
| 6i1                     |
| 4c1                     |
| 1a1                     |
| 9b1                     |
| 7ii2                    |
| 4b1                     |
| 7iii1                   |
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                              |
|:-------------------------------|
| 0.3_reference                  |
| 0.4_lkup                       |
| 0.5_submission_version_history |
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:
| branch    |
|:----------|
| ea ttl    |
| sea total |
| amea ttl  |
| si        |
| sothai    |
| sok       |
| sev       |
| sph       |
| somas     |
| sogulf    |
| ssa       |
| stwn      |
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:
| council name                       |
|:-----------------------------------|
| westmoreland fayette 512           |
| allegheny highlands council 382    |
| bucktail council 509               |
| chief cornplanter council_ bsa 538 |
| columbia-montour 504               |
| french creek council 532           |
| juniata valley council 497         |
| laurel highlands council 527       |
| susquehanna council 533            |
| moraine trails council 500         |
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:
| lastupdate          |
|:--------------------|
| 28/02/2014 09:53:14 |
| 28/02/2014 09:53:15 |
| 17/02/2014 13:10:37 |
| 17/02/2014 13:10:36 |
| 17/02/2014 13:10:35 |
| 17/02/2014 13:10:34 |
| 03/06/2014 10:32:47 |
| 26/03/2014 16:34:31 |
| 05/02/2014 14:17:33 |
| 17/02/2014 13:25:04 |
| 17/02/2014 13:25:03 |
| 17/02/2014 13:25:24 |
| 17/02/2014 13:25:25 |
| 05/02/2014 14:17:32 |
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:
| vínculo   |
|:----------|
| ctfpti    |
| ctfptrc   |
| ctfpti-pe |
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                       |
|:------------------------|
| other thermal power     |
| - condensing power      |
| - chp, district heating |
| - chp, industry         |
| - gas turbines etc.     |
| hydro power             |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| group 2   |
|:----------|
| g3        |
| g5        |
| g9        |
| g14       |
| g15       |
| g26       |
| g2        |
| g4        |
| g8        |
| g6        |
| g7        |
| g10       |
| g11       |
| g12       |
| g13       |
| g16       |
| g17       |
| g18       |
| g19       |
| g20       |
| g21       |
| g22       |
| g23       |
| g24       |
| g25       |
| g27       |
| g28       |
| g29       |
| g30       |
| g31       |
| g32       |
| g33       |
| g34       |
| g35       |
| g36       |
| g37       |
| g38       |
| g39       |
| g40       |
| g41       |
| g42       |
| g43       |
| g44       |
| g45       |
| g46       |
| g47       |
| g48       |
| g49       |
| g50       |
| g51       |
| g52       |
| g53       |
| g54       |
| g55       |
| g56       |
| g57       |
| g58       |
| g59       |
| g60       |
| g61       |
| g62       |
| 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       |
| g88       |
| g89       |
| g90       |
| g91       |
| g92       |
| g93       |
| g94       |
| g95       |
| g96       |
| g97       |
| g98       |
| g99       |
| g100      |
| g101      |
| g102      |
| g103      |
| g104      |
| g105      |
| g106      |
| g107      |
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                        |
|:-------------------------|
| b (new model)            |
| a (new tool)             |
| d (carry over)           |
| c (color up/material up) |
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       |
| 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       |
| g88       |
| g89       |
| g90       |
| g91       |
| g92       |
| g93       |
| g94       |
| g95       |
| g96       |
| g97       |
| g98       |
| g99       |
| g100      |
| g101      |
| g102      |
| g103      |
| g104      |
| g105      |
| g106      |
| g107      |
| g108      |
| g109      |
| g110      |
| g111      |
| g112      |
| g113      |
| g114      |
| g115      |
| g116      |
| g117      |
| g118      |
| g119      |
| g120      |
| g121      |
| g122      |
| g123      |
| g124      |
| g125      |
| g126      |
| g127      |
| g128      |
| g129      |
| g130      |
| g131      |
| g132      |
| g133      |
| g134      |
| g135      |
| g136      |
| g137      |
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:
| teoria 1 - dia   |
|:-----------------|
| segundas         |
| terças           |
| sextas           |
| quintas          |
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     |
|:------|
| pmut  |
| ytet  |
| mprt  |
| mxmt  |
| bgbit |
| isst  |
| mtjt  |
| megt  |
| dxpt  |
| mmlt  |
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:
| associated city   |
|:------------------|
| auburn            |
| bedford           |
| bloomington       |
| brazil            |
| clinton           |
| columbus          |
| connersville      |
| crawfordsville    |
| delphi            |
| elkhart           |
| evansville        |
| fort wayne        |
| frankfort         |
| french lick       |
| gary              |
| goshen            |
| greencastle       |
| greensburg        |
| griffith          |
| peru              |
| indianapolis      |
| huntingburg       |
| huntington        |
| jeffersonville    |
| kendallville      |
| kentland          |
| knox              |
| kokomo            |
| lafayette         |
| laporte           |
| lebanon           |
| logansport        |
| madison           |
| marion            |
| michigan city     |
| monticello        |
| muncie            |
| new castle        |
| north vernon      |
| paoli             |
| plymouth          |
| portland          |
| rensselaer        |
| richmond          |
| rochester         |
| salem             |
| seymour           |
| shelbyville       |
| sheridan          |
| south bend        |
| sullivan          |
| tell city         |
| terre haute       |
| valparaiso        |
| wabash            |
| warsaw            |
| washington        |
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:
| state                           |
|:--------------------------------|
| al - alabama                    |
| ar - arkansas                   |
| az - arizona                    |
| ca - california                 |
| co - colorado                   |
| ct - connecticut                |
| dc - district of columbia       |
| de - delaware                   |
| fl - florida                    |
| ga - georgia                    |
| gu - guam                       |
| hi - hawaii                     |
| ia - iowa                       |
| id - idaho                      |
| il - illinois                   |
| in - indiana                    |
| ks - kansas                     |
| ky - kentucky                   |
| la - louisiana                  |
| ma - massachusetts              |
| md - maryland                   |
| me - maine                      |
| mi - michigan                   |
| mn - minnesota                  |
| mo - missouri                   |
| ms - mississippi                |
| mt - montana                    |
| nc - north carolina             |
| nd - north dakota               |
| ne - nebraska                   |
| nh - new hampshire              |
| nj - new jersey                 |
| nm - new mexico                 |
| nv - nevada                     |
| ny - new york                   |
| oh - ohio                       |
| ok - oklahoma                   |
| or - oregon                     |
| pa - pennsylvania               |
| pr - puerto rico                |
| ri - rhode island               |
| sc - south carolina             |
| sd - south dakota               |
| tn - tennessee                  |
| tx - texas                      |
| ut - utah                       |
| va - virginia                   |
| vi - virgin islands of the u.s. |
| vt - vermont                    |
| wa - washington                 |
| wi - wisconsin                  |
| wv - west virginia              |
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:
| re_code gen                                               |
|:----------------------------------------------------------|
| au-ema-04 / au-ema-05 / au-ema-06                         |
| au-ema-07 / au-ema-08                                     |
| au-ema-12 / au-ema-13 / au-v4c-11                         |
| co-4mi-02 / co-4mi-01 / co-4mi-25                         |
| co-4mi-15 / co-4mi-26                                     |
| co-4mi-19 / co-4mi-24                                     |
| co-4mi-20 / co-mi-21                                      |
| co-4mi-10 / co-4mi-39                                     |
| co-4mi-41 /co-4mi-42                                      |
| ep-org-01 / ep-sou-01                                     |
| ep-tran-03 / ep-tran-04                                   |
| pi-jon-02 / pi-jon-06 / pi-jon-07                         |
| pi-jon-16 /pi-jon-17                                      |
| sd-cvi-01 / sd-cvi-03 / sd-cvi-08                         |
| sd-fmo-01 / sd-fmo-06 / sd-fmo-07 / sd-fmo-05             |
| sd-fsc-02 / sd-fsc-03                                     |
| sd-cvi-04 / sd-cvi-05 / sd-fmo-04                         |
| sd-cvi-17 / sd-fmo-03 / sd-fsc-01                         |
| sd-cvi-17 / sd-fmo-03 / sd-fsc-01/                        |
| sd-cvi-18 / sd-fmo-08 / sd-fsc-05                         |
| sd-cvi-25 / sd-cvi-26 / sd-fmo-18 / sd-fsc-13 / sd-fsc-15 |
| sd-cvi-27 / sd-fmo-19 / sd-fsc-14 /sd-fsc-16              |
| isd-qsu-01 / vso-cor-01 / so-cor-01                       |
| so-isd-01 / so-isd-03                                     |
| so-isd-05 / vso-cor-04 /vso-cor-07                        |
| st-csl-02 / st-csl-10                                     |
| st-csl-13                                                 |
| st-csl-04 / st-csl-11 / st-pre-01                         |
| st-csl-22 /_x000d_ st-pre-09 / st-csl-12                  |
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 entire graph                                                                              |
|:---------------------------------------------------------------------------------------------------------------|
| http://trib.al/fjgkxlo                                                                                         |
| http://madailygist.com/2-months-childbirth-dabota-lawson-shows-off-hot-figure-photosvideo/                     |
| http://www.bbc.com/earth/story/20161221-the-real-reasons-why-childbirth-is-so-painful-and-dangerous?ocid=twert |
| http://uploadvr.com/doctor-childbirth-vr-pain/                                                                 |
| http://affilists.com/clickbank-pregnancy-childbirth-8-best-products-december-2016/                             |
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:
| nname.1   |
|:----------|
| martinez  |
| williams  |
| johnson   |
| miller    |
| harris    |
| taylor    |
| robinson  |
| thompson  |
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                    |
|:---------------------|
| north curl curl slsc |
| freshwater slsc      |
| newport slsc         |
| manly lsc            |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| 列50     |
|:---------|
| 16:33.96 |
| 16:15.08 |
| 17:08.10 |
| 15:25.49 |
| 16:13.24 |
| 15:35.32 |
| 16:41.55 |
| 17:04.54 |
| 16:52.36 |
| 15:59.79 |
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    |
|:-----|
| mar  |
| apr  |
| may  |
| june |
| july |
| aug  |
| sep  |
| oct  |
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                                                                                                            |
|:-----------------------------------------------------------------------------------------------------------------------|
| global development_ results based aid_ food security_ global health_ technology_ anti-corruption                       |
| rethinking us foreign assistance                                                                                       |
| global development general operating support                                                                           |
| regulatory standards for financial inclusion                                                                           |
| future of financing for development: international responses to middle income countries and rising extractive revenues |
| fiscal federalism_ intergovernmental transfers to improve health                                                       |
| multilateral reform_ financing development in middle income countries_ and employment and job creation                 |
| millions saved_ third edition                                                                                          |
| aid effectiveness & innovation                                                                                         |
| general operating support                                                                                              |
| fragile states_ international financial institutions_ environmental issues_ governance_ private sector                 |
| debt relief and responsible lending with extension                                                                     |
| trade and agricultural pull mechanisms                                                                                 |
| global development_ general operating support                                                                          |
| cgd europe_ illicit financial flows                                                                                    |
| idrc visiting fellowship program                                                                                       |