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:
| e                  |
|:-------------------|
| 3g                 |
| 4g                 |
| 4g/lte             |
| 3g/4g              |
| 4g lte             |
| 3.5 g              |
| lte                |
| 3.5g               |
| 2g                 |
| 4g/h+              |
| 3g/3g+             |
| h+                 |
| 3g+/h+/4g          |
| 4g/h+/3g+          |
| 4g/dual carrier    |
| lte 4g             |
| lte+               |
| 3g/hspa            |
| 3g/hsdpa/hsupa     |
| 3g gprs/edge       |
| 3g or 4g           |
| 2g/3g/4g           |
| 3g/2g              |
| 3g+                |
| 3g hspa            |
| lte/3g             |
| 3g/gprs            |
| 3g/3.5g            |
| gprs/edge/3g hsdpa |
| lte/4g             |
| 3g/2g edge         |
| double speed 4g    |
| 4g/lte/hspa+/3g    |
| 4g/4g lte/3g       |
| 4g hspa+/3g        |
| 4g lte/3g          |
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:
| période    |
|:-----------|
| mars       |
| avril      |
| mai        |
| juin       |
| juillet    |
| août       |
| septembre  |
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   |
|:-------------|
| summer 2012  |
| mini 2013    |
| summer 2013  |
| mini 2014    |
| summer 2014  |
| mini 2015    |
| summer 2015  |
| mini 2016    |
| summer 2016  |
| mini 2017    |
| summer 2017  |
| mini 2018    |
| summer 2018  |
| mini 2019    |
| summer 2019  |
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:
| rs number    |
|:-------------|
| na           |
| rs386834139  |
| rs188259026  |
| rs104894060  |
| rs28940569   |
| rs34238807   |
| rs137852883  |
| rs386834130  |
| rs386834133  |
| rs746645358  |
| rs386834138  |
| rs149308952  |
| rs386834123  |
| rs386834134  |
| rs150047904  |
| rs386834124  |
| rs386834125  |
| rs386834126  |
| rs142269885  |
| rs386834127  |
| rs386834135  |
| rs386834136  |
| rs139003032  |
| rs765097897  |
| rs746397087  |
| rs151334741  |
| rs1554449124 |
| rs587779411  |
| rs143730802  |
| rs386834129  |


label: na
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:
| uzman                 |
|:----------------------|
| erhan meydan          |
| hakan çamoğlu         |
| halid özgür           |
| erol demirer          |
| cem karahanoğlu       |
| mustafa can           |
| müslüm güzel          |
| zühal avdan karakoç   |
| ali arslan            |
| devrim gümüş          |
| mahir türkcan         |
| ogün demir            |
| metin yılmaz          |
| cemil bayram          |
| arman kara            |
| tayfun türkalp        |
| deha etabek           |
| atıl gürcan           |
| taner özdeş           |
| fulya süeren          |
| emre yurtseven        |
| mehmet yılmaz         |
| deniz belli           |
| şükran eroğlu         |
| ayşenur yazıcı        |
| vedat erol            |
| ayşe burcu eren önen  |
| haluk otman           |
| neslihan yavuzcan     |
| tuğsan yılmaz         |
| atıf ünaldı           |
| hilal meriç           |
| eren özata            |
| mert aydıner          |
| emre üstünuçar        |
| fırat çakır           |
| mert levent kibar     |
| gökhan dumanlı        |
| oyunder               |
| hakan köz             |
| wilco van herpen      |
| seval uzun            |
| şengül pallı          |
| melike gülçin çakır   |
| murat karakaş         |
| salih keskin          |
| şahap bayram tokgöz   |
| sinan işler           |
| jeff treves           |
| ibrahim öztürk        |
| mehmet canker         |
| oğuz saygın           |
| ercan altuğ yılmaz    |
| devrim ersöz          |
| serhan ok             |
| melike çarpatan       |
| eda atay              |
| didem alpaylı erdoğan |
| ahmet murat fiş       |
| bora özkent           |
| tuğçe ağca kızıl      |
| güray karacar         |
| sibel yıldızbaş       |
| ilker taner uzun      |
| hüseyin adanalı       |
| alper yılmaz          |
| işıl taysever         |
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:
| evaluación para         |
|:------------------------|
| forraje                 |
| forraje-doble propósito |
| doble propósito         |
| invierno                |
| grano                   |
| silo                    |
| grano y silo            |
| doble prop.             |
| industria               |
| grano/silo              |
| silo/azúcar             |
| forrajero               |
| dulce                   |
| vino blanco             |
| mesa tinta              |
| vino tinto              |
| mesa blanca             |
| vino/mesa tinta         |
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:
| schiedsrichter           |
|:-------------------------|
| hsg weddingstedt/h./d.   |
| tsv büsum                |
| tsv schülp               |
| sg dithmarschen süd      |
| tura meldorf             |
| mtv heide                |
| sg pahlhude/tellingstedt |
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:
| 時間        |
|:------------|
| 17:15-18:15 |
| 15:30-16:30 |
| 13:45-15:45 |
| 12:00-13:00 |
| 14:45-15:45 |
| 11:15-12:15 |
| 14:00-15:00 |
| 13:30-14:30 |
| 12:30-13:30 |
| 11:45-12:45 |
| 15:15-16:15 |
| 10:00-11:00 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| metodologia           |
|:----------------------|
| educación tradicional |
| post primaria         |
| escuela nueva         |
| media rural           |
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:
| provide status only   |
|:----------------------|
| completed             |
| planned               |
| ongoing               |
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:
| ganadería                      |
|:-------------------------------|
| novofincas_ s.l.               |
| antonio toribio martin         |
| explotaciones el cubillo_ s.l. |
| ramon perez-carrion            |
| agropecuaria valdesequera      |
| barinitas_ s.l.                |
| manuel jurado lobato           |
| inmobico 2002_ s.l.            |
| francisco romero iglesias      |
| golonestre ganaderia_ s.l.     |
| juan luis muñoz carrasco       |
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           |
|:------------|
| high school |
| elementary  |
| unit        |
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:
| contractor   |
|:-------------|
| azco         |
| samasu       |
| ogi          |
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:
| h                                                                           |
|:----------------------------------------------------------------------------|
| 9pdm10004: developing algebraic thinking for ms/hs math teachers            |
| 9pdm10005: understanding rational numbers                                   |
| 9pdm10006: the coaching cycle                                               |
| 9pdm10008: academic youth development                                       |
| 9pdm10009: differentiated writing to close the achievement gap              |
| 9pdm10010: foundations of literacy k-3                                      |
| 9pdm10011: language essentials for teachers of reading & spelling k-3       |
| 9pdm10012: reading and writing informational text k-6                       |
| 9pdm10013: reading in the content areas 6-8                                 |
| 9pdm10014: teaching expository & persuasive writing 6-12                    |
| 9pdm10015: oral language and vocabulary k-5                                 |
| 9pdm10016: writing in response to reading 2-5                               |
| 1ta10026: guided self-assessment on the conditions for school effectiveness |
| 1ta10027: leadership networks                                               |
| 1ta10028: instructional leadership coaching                                 |
| 3ta10029: data reporting and analysis                                       |
| 7ta10030: orientation to plc guidance                                       |
| 7ta10031: common planning time self-assessment                              |
| 7ta10032: mathematics learning community                                    |
| 9ta10033: the learning walk toolkit                                         |
| 9ta10034: literacy network                                                  |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| sit_acad_ano_ant             |
|:-----------------------------|
| no estudió vigencia anterior |
| aprobó                       |
| reprobó                      |
| no culminó estudios          |
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   |
|:----------|
| 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      |
| g138      |
| g139      |
| g140      |
| g141      |
| g142      |
| g143      |
| g144      |
| g145      |
| g146      |
| g147      |
| g148      |
| g149      |
| g150      |
| g151      |
| g152      |
| g153      |
| g154      |
| g155      |
| g156      |
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                                                    |
|:--------------------------------------------------------------|
| core municipal services in a declared public health emergency |
| other request                                                 |
| expanded public health mission                                |
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:
| customer          |
|:------------------|
| yuonne burkhart   |
| young story       |
| yoshiko sturm     |
| yaeko brennan     |
| yadira beckett    |
| wonda putnam      |
| winona cabral     |
| willow nunes      |
| willia mathias    |
| wendi solomon     |
| warner braxton    |
| waltraud haas     |
| wai witherspoon   |
| viva peak         |
| vince root        |
| vickey diamond    |
| vesta baumann     |
| vern randle       |
| vern culver       |
| velda humphries   |
| vanda sutherland  |
| valentin haugen   |
| vada ferry        |
| uwimana jasiri    |
| ula cody          |
| tyrone smith      |
| trudie somers     |
| travis foss       |
| tonita aguirre    |
| tiffaney kinard   |
| thao correa       |
| thaddeus kendrick |
| tena mackay       |
| taunya sams       |
| tatiana rodrigue  |
| tamisha russo     |
| tamar huntley     |
| tabatha brumfield |
| syreeta damon     |
| sylvie herrington |
| sydney concepcion |
| sunny belton      |
| suk hibbard       |
| suk barone        |
| stephany bartels  |
| stasia jamison    |
| star larry        |
| stacie medley     |
| sofia huynh       |
| so porterfield    |
| shona parrish     |
| shomari njete     |
| sherril wills     |
| shenika aldridge  |
| shellie storey    |
| shea ahrens       |
| shazidi mukondi   |
| shasta ennis      |
| sharita ojeda     |
| sharee cave       |
| shanita thorton   |
| shaniqua whitmire |
| selina garnett    |
| savanna madrid    |
| saundra borden    |
| sarita bueno      |
| santos prewitt    |
| sanford schmitz   |
| ruthanne rainey   |
| rudy hogg         |
| rozanne english   |
| rossana conti     |
| roselee judge     |
| roseanna tomlin   |
| rosana acevedo    |
| romeo glynn       |
| romeo cornell     |
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:
| royce abbey trophy   |
|:---------------------|
| rob heaney           |
| peter hannah         |
| don mcrae            |
| david hannah         |
| greg wheeler         |
| maurice keenan       |
| merv cross           |
| wayne denmeade       |
| greg armstrong       |
| kevin beaumont       |
| robert greig         |
| bevin pinner         |
| tony blackmore       |
| peter riddett        |
| robert motteram      |
| brian turner         |
| gavan gratston       |
| tony smith           |
| john lette           |
| jeff stephenson      |
| david clough         |
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:
| language   |
|:-----------|
| en         |
| nl         |
| und        |
| in         |
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                    |
|:---------------------|
| jeroen slooten       |
| riaan esterhuysen    |
| andries van der walt |
| elise havenga        |
| michelle taylor      |
| satish harrichand    |
| gustav fourie        |
| herman swanepoel     |
| rudi prinsloo        |
| richard francis      |
| francois king        |
| andre de beer        |
| ian mcmillan         |
| jannie badenhorst    |
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:
| place full name                        |
|:---------------------------------------|
| thatcham_ england                      |
| canberra_ australian capital territory |
| whitehorse_ yukon                      |
| bristol_ england                       |
| hyderabad_ pakistan                    |
| coimbatore_ india                      |
| punjab_ pakistan                       |
| south carolina_ usa                    |
| oak hills place_ la                    |
| oregon_ usa                            |
| ann arbor_ mi                          |
| rome_ lazio                            |
| hauppauge_ ny                          |
| calgary_ alberta                       |
| quellón_ chile                         |
| willoughby_ oh                         |
| nanaimo_ british columbia              |
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:
| technology need explanation   |
|:------------------------------|
| utilizing qualtrics           |
| e-convenings via zoom         |
| compton college website       |
| banner for scanning           |
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:
| 23          |
|:------------|
| oxford      |
| quasar      |
| henley      |
| keel        |
| maltstar    |
| sloop vic   |
| mackay      |
| hannan      |
| hindmarsh   |
| skipper     |
| macquarie   |
| latrobe     |
| maritime    |
| roe         |
| schooner    |
| tulla       |
| vlamingh    |
| lockyer     |
| navigator   |
| bmaa12hors3 |
| bmaa09murr3 |
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:
| place name   |
|:-------------|
| los angeles  |
| cambridge    |
| santa maria  |
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 |
|-------------:|
|  0.000178802 |
|  0.000390226 |
|  8.80901e-05 |
|  8.05874e-05 |
|  6.18314e-05 |
|  0.000180629 |
|  1.06148e-06 |
|  9.0804e-07  |
|  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:
| a   |
|:----|
| 3c  |
| 4c  |
| 5c  |
| 6c  |
| 7c  |
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:
| stichwort 2             |
|:------------------------|
| prävention              |
| online-befragung        |
| schulentwicklung        |
| berufsbildung           |
| kultur-/sprachaustausch |
| weiterbildung           |
| medienerziehung         |
| wettbewerb              |
| sonderschulfinanzierung |
| lehrmittel              |
| veranstaltungshinweis   |
| stelleninserat          |
| bericht                 |
| online-umfrage          |
| gesundheit              |
| casting-bewerbung       |
| netzwerk                |
| publikation             |
| hbv                     |
| medienmitteilung        |
| eth zürich              |
| hausaufgaben            |
| unterrichtshilfen       |
| projektangebot          |
| beratung                |
| workshop                |
| pisa                    |
| lohn                    |
| schulleitungen          |
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:
| column8   |
|:----------|
| france    |
| germany   |
| china     |
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:
|   čas podpolianska výzva |
|-------------------------:|
|                0.0293866 |
|                0.0339468 |
|                0.0309606 |
|                0.0359722 |
|                0.0328704 |
|                0.0353704 |
|                0.0281481 |
|                0.0316088 |
|                0.0310417 |
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:
| inköp övrigt (servetter_ snacks_ alkofri dryck_ kaffe_ sopsäckar)   |
|:--------------------------------------------------------------------|
| ketchup och senap                                                   |
| alkoholfri dryck (karta med läsk ca 24 st 33 cl)                    |
| muggar till bål - välkomstdryck 140 st. ska rymma ca 1 dl           |
| snacks 5 påsar exv chips_ ostkrokar_ chilinötter)                   |
| varmkorv 1 stor burk                                                |
| bryggkaffe 3x500g + 2 l laktosmjölk                                 |
| 60 korvbröd                                                         |
| sopsäckar                                                           |
| ca 240 servetter_ till mat och efterrätt                            |
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   |
|:--------|
| ca      |
| ia      |
| pa      |
| al      |
| nj      |
| tx      |
| fl      |
| ma      |
| ok      |
| wv      |
| ar      |
| ny      |
| mn      |
| tn      |
| nh      |
| ms      |
| ut      |
| wa      |
| az      |
| sc      |
| ky      |
| mo      |
| oh      |
| nc      |
| in      |
| ga      |
| mi      |
| ks      |
| nm      |
| md      |
| il      |
| la      |
| ne      |
| va      |
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:
| status    |
|:----------|
| avslutat  |
| behandlas |
| makulerat |
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:
| recommendation                       |
|:-------------------------------------|
| continued cases                      |
| unsafe building hearing              |
| emergency demolition                 |
| city owned                           |
| ordered demolished 12/5/13           |
| ordered demolished                   |
| ordered demolished 6/28/12           |
| ordered demolished 3/21/13           |
| ordered demolished 8/24/10           |
| ordered demolished 5/30/13           |
| ordered demolished 7/11/13           |
| ordered demolished 12/22/09          |
| ordered demolished 6/14/10           |
| continued cases/emergency demolition |
| ordered demolished 10/31/12          |