MMTU-benchmark/MMTU huggingface dataset:
Task: Error-Detect (source dataset: Relational-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:
| ngram            |
|:-----------------|
| usually wear     |
| looks great      |
| fit perfectly    |
| love top         |
| size small       |
| well made        |
| fits perfectly   |
| usual size       |
| look like        |
| looks like       |
| cannot wait      |
| fit well         |
| many compliments |
| highly recommend |
| love love        |
| super cute       |
| runs large       |
| wear size        |
| feel like        |
| fabric soft      |
| size fit         |
| ordered size     |
| fit great        |
| soft comfortable |
| normally wear    |
| super soft       |
| wanted love      |
| skinny jeans     |
| absolutely love  |
| even though      |
| fit perfect      |
| really like      |
| regular size     |
| thought would    |
| going back       |
| looked like      |
| fits well        |
| ordered small    |
| really cute      |
| look great       |
| runs small       |
| material soft    |
| fits true        |
| would say        |
| normal size      |
| wait wear        |
| one size         |
| fit like         |
| good quality     |
| fits great       |
| high quality     |
| fell love        |
| made look        |
| would look       |
| full price       |
| small fit        |
| would recommend  |
| fit true         |
| like model       |
| length perfect   |
| great quality    |
| felt like        |
| size medium      |
| really wanted    |
| fits like        |
| little bit       |
| arm holes        |
| first time       |
| ordered xs       |
| think would      |
| sale price       |
| little big       |
| size fits        |
| great fit        |
| tried store      |
| love color       |
| small medium     |
| wear work        |
| ordered medium   |
| beautiful dress  |
| bought dress     |
| fabric nice      |
| dress beautiful  |
| easy wear        |
| long enough      |
| love sweater     |
| small fits       |
| run large        |
| light weight     |
| love shirt       |
| wear small       |
| perfect length   |
| really nice      |
| look good        |
| like wearing     |
| size large       |
Task: Error-Detect (source dataset: Relational-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:
| mbx_storageused(kb/mb/gb/tb)   |
|:-------------------------------|
| 42.13 gb                       |
| 351.74 gb                      |
| 312.22 gb                      |
| 39.52 gb                       |
| 360.82 gb                      |
| 320.47 gb                      |
| 40.36 gb                       |
| 353.86 gb                      |
| 314.42 gb                      |
| 39.44 gb                       |
| 337.68 gb                      |
| 299.23 gb                      |
| 38.45 gb                       |
| 329.54 gb                      |
| 292.16 gb                      |
| 37.38 gb                       |
| 345.25 gb                      |
| 308.32 gb                      |
| 36.93 gb                       |
| 343.30 gb                      |
| 307.92 gb                      |
| 35.38 gb                       |
| 338.66 gb                      |
| 304.40 gb                      |
| 34.26 gb                       |
| 326.92 gb                      |
| 293.79 gb                      |
| 33.13 gb                       |
| 317.38 gb                      |
| 283.59 gb                      |
| 33.79 gb                       |
| 105.63 gb                      |
Task: Error-Detect (source dataset: Relational-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:
| sub_grade   |
|:------------|
| b2          |
| b4          |
| b1          |
| b3          |
| b5          |
| d2          |
| c2          |
| d3          |
| a5          |
| e5          |
| c1          |
| d5          |
| e2          |
| d4          |
| e1          |
| a4          |
| f4          |
| f2          |
| e3          |
| e4          |
| f1          |
| a1          |
| a2          |
| a3          |
| d1          |
| c4          |
| g4          |
| f5          |
| g2          |
| f3          |
| c3          |
| g3          |
| c5          |
| g1          |
| g5          |
Task: Error-Detect (source dataset: Relational-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:
| auto_model     |
|:---------------|
| escape         |
| tl             |
| a3             |
| malibu         |
| x6             |
| ml350          |
| 95             |
| a5             |
| civic          |
| c300           |
| silverado      |
| 92x            |
| tahoe          |
| crv            |
| 93             |
| 3 series       |
| wrangler       |
| grand cherokee |
| neon           |
| passat         |
| f150           |
| jetta          |
| mdx            |
| corolla        |
| camry          |
| forrestor      |
| maxima         |
| ram            |
| ultima         |
| legacy         |
| pathfinder     |
| m5             |
| fusion         |
| highlander     |
| x5             |
| accord         |
| rsx            |
| e400           |
| impreza        |


label: forrestor
Task: Error-Detect (source dataset: Relational-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:
| company county           |
|:-------------------------|
| rockingham               |
| davie                    |
| durham                   |
| johnston                 |
| caldwell                 |
| sampson                  |
| wayne                    |
| scotland                 |
| stanly                   |
| haywood                  |
| person                   |
| cabarrus                 |
| mcdowell                 |
| wake                     |
| cleveland                |
| lenoir                   |
| guilford                 |
| burke                    |
| richmond                 |
| surry                    |
| mecklenburg              |
| nash                     |
| wake, durham             |
| hertford                 |
| wake, mecklenburg        |
| iredell                  |
| henderson                |
| hoke                     |
| granville                |
| dare                     |
| catawba                  |
| montgomery               |
| rowan                    |
| randolph                 |
| cabarrus, mecklenburg    |
| mecklenburg, forsyth     |
| franklin                 |
| orange                   |
| polk                     |
| rutherford               |
| anson                    |
| forsyth                  |
| lee                      |
| alamance                 |
| craven                   |
| pitt                     |
| buncombe                 |
| davidson                 |
| gaston                   |
| lincoln                  |
| beaufort                 |
| union                    |
| duplin                   |
| northampton              |
| martin                   |
| rowan, catawba, caldwell |
| pasquotank               |
| vance                    |
| edgecombe                |
| halifax                  |
| cherokee                 |
| jackson                  |
| chowan                   |
| bladen                   |
| alexander                |
| robeson                  |
| yadkin                   |
| wilkes                   |
| brunswick                |
| burke, alexander         |
| mitchell                 |
Task: Error-Detect (source dataset: Relational-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:
| city           |
|:---------------|
| pune           |
| bangalore      |
| kolkata        |
| delhi          |
| hyderabad      |
| chennai        |
| mumbai         |
| durban         |
| centurion      |
| east london    |
| kimberley      |
| johannesburg   |
| port elizabeth |
| jaipur         |
| chandigarh     |
| raipur         |
| sharjah        |
| ahmedabad      |
| rajkot         |
| visakhapatnam  |
| indore         |
| bloemfontein   |
| nagpur         |
| dharamsala     |
| kochi          |
| abu dhabi      |
| cuttack        |
| ranchi         |
| kanpur         |
| cape town      |
| mohali         |
| bengaluru      |
Task: Error-Detect (source dataset: Relational-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:
| snapshot_time           |
|:------------------------|
| 2021-04-30 12:00:00.000 |
| 2021-04-30 12:10:01.000 |
| 2021-04-30 12:20:02.000 |
| 2021-04-30 12:30:04.000 |
| 2021-04-30 12:40:01.000 |
| 2021-04-30 12:50:05.000 |
| 2021-04-30 13:00:01.000 |
| 2021-04-30 13:10:04.000 |
| 2021-04-30 13:20:04.000 |
| 2021-04-30 13:30:00.000 |
| 2021-04-30 13:40:01.000 |
| 2021-04-30 13:50:02.000 |
| 2021-04-30 14:00:03.000 |
| 2021-04-30 14:10:01.000 |
| 2021-04-30 14:20:03.000 |
| 2021-04-30 14:30:00.000 |
| 2021-04-30 14:40:02.000 |
| 2021-04-30 14:50:01.000 |
| 2021-04-30 15:00:04.000 |
| 2021-04-30 15:10:01.000 |
| 2021-04-30 15:20:00.000 |
| 2021-04-30 15:30:05.000 |
| 2021-04-30 15:40:05.000 |
| 2021-04-30 15:50:02.000 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| d                    |
|:---------------------|
| ingham/clinton/eaton |
| macomb               |
| macomb/oakland       |
| midland              |
| oakland              |
| washtenaw            |
| wayne                |
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:
| powertrain   |
|:-------------|
| electric     |
| fcev         |
| lpg          |
| cng          |
| 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:
|   % of closings |
|----------------:|
|      0.0740741  |
|      0.0555556  |
|      0.0462963  |
|      0.037037   |
|      0.0277778  |
|      0.0185185  |
|      0.00925926 |
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:
| famille d'équipement             |
|:---------------------------------|
| fermeture                        |
| porte piétonne automatique       |
| fermeture à enroulement motorisé |
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:
| name_zh-hans   |
|:---------------|
| 蔡沛良牧师     |
| 蔡陈艳婷师母   |
| 陈俊业牧师     |
| 陈沈丽霞师母   |
| 张贤才牧师     |
| 获夫牧师       |
| 梁兆基牧师     |
| 梁吴小萍师母   |
| 杨琇惠传道     |
| 黎维山长老     |
| 黎吴丽贤姊妹   |
| 黄严丽慈姊妹   |
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:
| adresse                      |
|:-----------------------------|
| la mothe-achard              |
| la chapelle-achard           |
| beaulieu-sous-la-roche       |
| la chapelle-hermier          |
| le girouard                  |
| nieul-le-dolent              |
| sainte-flaive-des-loups      |
| saint-georges-de-pointindoux |
| saint-mathurin               |
| martinet                     |
| saint-julien-des-landes      |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| Unnamed: 0                     |
|:-------------------------------|
| ilighast chamnabbar or sulphon |
| 0                              |
| narthund delzhour              |
| ammathair hawkfeather’         |
| ammathair hawkfeather          |
| meritid archneie               |
| jasmal                         |
| kromnlor sernar                |
| galinda raventree amcathra     |
| vlorn keenear                  |
| irbryth authamaun              |
| mirrormul tszul                |
| melshimber                     |
| lord bly ruldegost             |
| cassalanter                    |
| thestus thongolir              |
| myrna cassalanter              |
| zeltabbar iliphar              |
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                  |
|:-------------------|
| industrial         |
| public authorities |
| irrigation         |
| other (specify)    |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| Unnamed: 0                |
|:--------------------------|
| days of therapy / 1000 rd |
| sbar used and completed   |
| met criteria to start abx |
| sbar used but incomplete  |
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:
| color      |
|:-----------|
| 7_ 125_ 0  |
| 13_ 121_ 0 |
| 20_ 118_ 0 |
| 26_ 115_ 0 |
| 39_ 108_ 0 |
| 66_ 95_ 0  |
| 53_ 102_ 0 |
| 72_ 92_ 0  |
| 105_ 76_ 0 |
| 33_ 112_ 0 |
| 85_ 85_ 0  |
| 112_ 72_ 0 |
| 46_ 105_ 0 |
| 79_ 89_ 0  |
| 98_ 79_ 0  |
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:
| ledger list                                                |
|:-----------------------------------------------------------|
| employer contributions to employee retirement plans        |
| interest expenses on mortgage obligations                  |
| interest expenses on other fixed debt obligations          |
| lease payments on real property                            |
| lease payments on other lease obligations for business use |
| gas                                                        |
| electricity                                                |
| water                                                      |
| transportation                                             |
| telephone                                                  |
| internet                                                   |
| operations expenditures                                    |
| property damage costs                                      |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| d         |
|:----------|
| melus jr  |
| nikitin   |
| mathis    |
| betterly  |
| pitsoulis |
| melus     |
| sverdin   |
| kreb      |
| lee       |
| zhang     |
| ahn       |
| snyderman |
| lim       |
| schendel  |
| hong      |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| Unnamed: 0     |
|:---------------|
| manan, sarthak |
| manan          |
| sarthak        |
| sarthak, manan |
| sarthak manan  |
| front end      |
| front end/back |
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:
| function               |
|:-----------------------|
| environmental health   |
| development management |
| human resources        |
| neighbourhood services |
| ict & business change  |
| finance                |
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:
| css regional director   |
|:------------------------|
| vacant                  |
| adam berke              |
| samantha yee            |
| cynthia weekley         |
| teal sexton             |
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   |
|:----------|
| g1        |
| g2        |
| g3        |
| g4        |
| g5        |
| g6        |
| g7        |
| g8        |
| g9        |
| g10       |
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:
| 14          |
|:------------|
| grimmett    |
| gairdner    |
| fleet       |
| fitzroy     |
| flagship    |
| finniss     |
| oxford      |
| roe         |
| hindmarsh   |
| flinders    |
| henley      |
| latrobe     |
| granger     |
| vlamingh    |
| mackay      |
| hannan      |
| bmaa10mayr2 |
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:
| code qpv   |
|:-----------|
| qp093028   |
| qp093030   |
| qp093031   |
| qp093034   |
| qp093036   |
| qp093039   |
| qp093040   |
| qp093044   |
| qp093046   |
| qp093048   |
| qp093049   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| j       |
|:--------|
| f.hes7g |
| f.hes7j |
| f.hes7k |
| f.hes7m |
| f.hes7n |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| Unnamed: 0   |
|:-------------|
| orn          |
| wpn          |
| vhn          |
| asn          |
| emr          |
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       |
|:--------|
| 78434ea |
| 78431ds |
| 78435hh |
| 78432am |
| 7677l2  |
| 77988m1 |
| 77989m2 |
| 77990m3 |
| 77991m4 |
| 77992m5 |
| 7677l3  |
| 77993m6 |
| 77994m7 |
| 77995m8 |
| 77996m9 |
| 77997mx |
| 7677l4  |
| 78003pc |
| 78004fs |
| 78005ge |
| 78018wf |
| 78019sf |
| 7677l5  |
| 78458by |
| 78459bs |
| 78460me |
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:
| k                     |
|:----------------------|
| -                     |
| 4.6618858462308975e-2 |
| 5.0757830353542151e-2 |
| 4.9488429257985141e-2 |
| 4.9399633404864092e-2 |
| 4.9212231192314049e-2 |
| 4.908312083984126e-2  |
| 4.5952118480392906e-2 |
| 4.5250385580296697e-2 |
| 4.5972942600223964e-2 |
| 4.7018888849068387e-2 |
| 4.5193414585784054e-2 |
| 4.6493368404348694e-2 |
| 4.70572462220595e-2   |
| 4.731456750553973e-2  |
| 4.6861491500480155e-2 |
| 4.7055592734481559e-2 |
| 4.7576459875764206e-2 |
| 4.6419715940763683e-2 |
| 4.6549451530993081e-2 |
| 4.9061216329966323e-2 |
| 4.9330656193980561e-2 |
| 5.0738118803913997e-2 |
| 5.1503699864674207e-2 |
| 4.8739223850321617e-2 |
| 4.9473684210526316e-2 |
| 5.1303537296916647e-2 |
| 5.2095317682421233e-2 |
| 5.2723646559873279e-2 |
| 4.9134078212290495e-2 |
| 4.9841319014181477e-2 |
| 5.2255409660830886e-2 |
| 5.0548862867741906e-2 |
| 4.9437728843718991e-2 |
| 4.7372925634786948e-2 |
| 4.7830799425842931e-2 |
| 4.8008697642481113e-2 |
| 5.0114392409651036e-2 |
| 5.1602287581699352e-2 |
| 5.3028721051262044e-2 |
| 5.1176260360927504e-2 |
| 5.3994256300182848e-2 |
| 5.560414269275029e-2  |
| 5.0673019050609724e-2 |


label: -
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:
| br    |
|:------|
| [89]  |
| [99]  |
| [100] |
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:
| r.u.p.             |
|:-------------------|
| renna anna         |
| piomboni patrizia  |
| sorbello rosetta   |
| calvani paola      |
| verdecchia marco   |
| guidetti gabriella |
| colantoni patrizia |
| marcari massimo    |
| follega livia      |
| conti andrea       |
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                |
|:-----------------|
| first name*      |
| last name*       |
| title*           |
| primary phone*   |
| secondary phone  |
| email*           |
| mailing address* |
| city*            |
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:
| en             |
|:---------------|
| met            |
| not applicable |
| not met        |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| notes or comments                                                                                          |
|:-----------------------------------------------------------------------------------------------------------|
| revisit planned week commencing 20/03 to talk about  share solar purification technique (sodis)            |
| children come from the three villages  we covered wash in last week . we plan to re- visit school on 22/03 |
| revisit planned week commencing 20/03 share solar purification technique (sodis)                           |
| revisit planned due to funeral                                                                             |
| report received from tu tangi at mfat 23 march 2016 by email                                               |
| save the children 3ws submission: 2016/03/02                                                               |
| neoc sitrep #39:                                                                                           |
| report received from waf on 27 march 2016 by email                                                         |
| neoc sitrep #39: this was caused due to the power shut down from 1500hours – 1700hours on 28.02.16.        |
| includes kindergarden                                                                                      |


label: includes kindergarden