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     |
|:------|
| pack  |
| pcs   |
| reams |
| ream  |
| box   |
| bot.  |
| 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:
| city          |
|:--------------|
| hollywood     |
| los angeles   |
| san diego     |
| san francisco |
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:
| знятий/прийнятий                  |
|:----------------------------------|
| знятий                            |
| знятий (не був на обслуговуванні) |
| знятий (гуртожиток)               |
| прийнятий 1 серпня                |
| прийнятий 1 вересня               |
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:
| label            |
|:-----------------|
| d. moj: f        |
| d. gri: f        |
| d. mel: d (base) |
| d. ere: d (base) |
| d. ere: d (ext.) |
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   |
|:----|
| i   |
| ii  |
| iii |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| region                                                 |
|:-------------------------------------------------------|
| southwest (az_ ca_ nv)                                 |
| north central (co_ ia_ ks_ mn_ mo_ ne_ nd_ ok_ sd_ wy) |
| northwest (ak_ hi_ id_ mt_ or_ ut_ wa)                 |
| south (al_ fl_ ga_ ky_ ms_ sc_ tn)                     |
| great lakes (il_ in_ mi_ oh_ wi)                       |
| southeast (wdc_ md_ nc_ va_ wv)                        |
| east (de_ nj_ pa)                                      |
| north east (ct_ me_ ma_ nh_ ny_ ri_ vt)                |
| south central (ar_ la_ nm_ tx)                         |
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:
| 2019 status                                                          |
|:---------------------------------------------------------------------|
| on-track                                                             |
| revised/delayed                                                      |
| see fisheries bill updates                                           |
| complete                                                             |
| delayed/revised                                                      |
| action on-track, timeline revised                                    |
|                                                                      |
| new action                                                           |
| no update                                                            |
| ongoing                                                              |
| off-track                                                            |
| action revised                                                       |
| no update from core cities, other than last bullet - revised/delayed |
| in progress                                                          |
| in progress, although no update by core cities                       |
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:
| 15.4     |
|:---------|
| мсп      |
| ивд      |
| ивд_ мсп |
| зер_ ивд |
| мсп_ ивд |
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:
| grade   |
|:--------|
| d       |
| b       |
| a       |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| start date   |
|:-------------|
| 08/17/2015   |
| 08/28/2015   |
| 09/01/2016   |
| 09/07/2016   |
| 05/31/2017   |
| 07/01/2016   |
| 01/01/2017   |
| 08/14/2017   |
| 10/15/2016   |
| 10/20/2014   |
| 10/15/2014   |
| 01/11/2017   |
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:
| c13      |
|:---------|
| arth 108 |
| danc 210 |
| dfst 101 |
| educ 203 |
| emf 205  |
| engl 233 |
| engl 234 |
| engl 235 |
| engl 239 |
| fmst 310 |
| fmst 360 |
| hlth 220 |
| honr 240 |
| idhp 300 |
| lgbt 101 |
| musc 205 |
| nurs 416 |
| phil 204 |
| rlst 205 |
| rlst 206 |
| rlst 209 |
| rlst 210 |
| soci 241 |
| soci 243 |
| thea 303 |
| thea 304 |
| thea 316 |
| thea 380 |
| wmst 231 |
| wmst 232 |
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   |
|:----|
| ju  |
| ve  |
| sa  |
| ma  |
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:
| cat outside all inclusive   |
|:----------------------------|
| ob                          |
| oc                          |
| og                          |
| ok                          |
| o1                          |
| oh                          |
| oj                          |
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:
| builder                                              |
|:-----------------------------------------------------|
| lakemont wild stallion llc                           |
| montage marketing llc_ montage marketing corporation |
| toll south reno llc                                  |
| lakemont canyon pines llc                            |
| lennar reno llc                                      |
| tl highlands lp                                      |
| tmb builders llc                                     |
| meritage homes of nevada inc                         |
| kastle investments highland place llc                |
| homecrafters ltd                                     |
| toll north reno llc                                  |
| ryder management llc                                 |
| iota spring llc                                      |
| br catron llc                                        |
| corona lasenda 2 llc                                 |
| pinnacle land holdings llc                           |
| dh1 llc                                              |
| dhi llc                                              |
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:
| 8 color   |
|:----------|
| cyan      |
| yellow    |
| lime      |
| pink      |
| red       |
| magenta   |
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:
| kultuur          |
|:-----------------|
| suvioder         |
| suvinisu         |
| kaer             |
| valge ristik     |
| suviraps         |
| põldtimut        |
| talinisu         |
| suvitritikale    |
| lutsern          |
| talirukis        |
| roosa ristik     |
| punane ristik    |
| põlduba          |
| põldhernes       |
| harilik aruhein  |
| põld-sojauba     |
| päideroog        |
| talioder         |
| sale haguhein    |
| talirüps         |
| ida-kitsehernes  |
| valge mesikas    |
| ohtetu luste     |
| aasnurmikas      |
| punane aruhein   |
| karjamaa-raihein |
| taliraps         |
| lamba-aruhein    |
| itaalia raihein  |
| tatar            |
| talitritikale    |
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:
|          i |
|-----------:|
| 0.0170823  |
| 0.0140031  |
| 0.0186233  |
| 0.0209335  |
| 0.0246254  |
| 0.0308206  |
| 0.0307293  |
| 0.0312312  |
| 0.0269185  |
| 0.0260401  |
| 0.0285306  |
| 0.0258086  |
| 0.0228545  |
| 0.020957   |
| 0.0224805  |
| 0.0228779  |
| 0.02227    |
| 0.0219656  |
| 0.020016   |
| 0.017486   |
| 0.0194973  |
| 0.0184103  |
| 0.0176758  |
| 0.015582   |
| 0.0180788  |
| 0.0179366  |
| 0.014099   |
| 0.0147456  |
| 0.0177707  |
| 0.0155104  |
| 0.0175097  |
| 0.0133791  |
| 0.0126576  |
| 0.0162496  |
| 0.0167019  |
| 0.0177232  |
| 0.0158444  |
| 0.0138352  |
| 0.0161781  |
| 0.016797   |
| 0.0147935  |
| 0.0157252  |
| 0.015916   |
| 0.00987768 |
| 0.015176   |
| 0.0158206  |
| 0.0162258  |
| 0.014075   |
| 0.0155581  |
| 0.015749   |
| 0.0128261  |
| 0.0135472  |
| 0.0150565  |
| 0.015391   |
| 0.0149131  |
| 0.0140271  |
| 0.013307   |
| 0.0142428  |
| 0.0136432  |
| 0.0145542  |
| 0.0130186  |
| 0.0137632  |
| 0.0131388  |
| 0.0119588  |
| 0.012007   |
| 0.0103386  |
| 0.0123445  |
| 0.011331   |
| 0.0107746  |
| 0.0120311  |
| 0.0121035  |
| 0.0113551  |
| 0.0101931  |
| 0.0105809  |
| 0.0102658  |
| 0.00832057 |
| 0.00982913 |
| 0.0114035  |
| 0.00917301 |
| 0.0116209  |
| 0.0124891  |
| 0.0136912  |
| 0.0132109  |
| 0.0130426  |
| 0.0124168  |
| 0.0125132  |
| 0.0126336  |
| 0.0106293  |
| 0.0129464  |
| 0.0128742  |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| top word pairs in tweet in g9   |
|:--------------------------------|
| st_louis                        |
| louis_cathedral                 |
| visitneworleans_akadrjohn       |
| akadrjohn_wwoz_neworleans       |
| wgnotv_jrmason21010             |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| top words in tweet in g5   |
|:---------------------------|
| flyswiss                   |
| cs100                      |
| din                        |
| find                       |
| lufthansanews              |
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:
| kolumn2                                       |
|:----------------------------------------------|
| programhandläggning och studievägledning      |
| prov- och intervjubaserad antagning           |
| driftmedel                                    |
| internationalisering                          |
| tapil                                         |
| inst.komp pd-uppdrag_ tandhygienistprogrammet |
| inst.komp pd-uppdrag_ tandläkarprogrammet     |
| arvode pd                                     |
| arvode programnämndsordförande                |
| arvode studentrepresentanter                  |
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   |
|:----------|
| g4        |
| g5        |
| g7        |
| g10       |
| g11       |
| g15       |
| g1        |
| g2        |
| g3        |
| g9        |
| g6        |
| g8        |
| g12       |
| g13       |
| g14       |
| g16       |
| g17       |
| g18       |
| g19       |
| g20       |
| g21       |
| g22       |
| g23       |
| g24       |
| g25       |
| g26       |
| g27       |
| g28       |
| g29       |
| g30       |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| top words in tweet in g1   |
|:---------------------------|
| airportpsp                 |
| americanair                |
| thanks                     |
| taking                     |
| everything                 |
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:
| glcommodity                                          |
|:-----------------------------------------------------|
| fleet                                                |
| telecommunications                                   |
| z non-procurement                                    |
| property & facilities                                |
| employee services                                    |
| education and training                               |
| business equipment and services                      |
| professional services                                |
| contingent labour                                    |
| contractors - split by agency - 0000523013           |
| bcm contractors & specialists                        |
| professional services - split by agency - 0000523019 |
| electricity and utilities                            |
| logistics                                            |
| ict services                                         |
| travel                                               |
| food and catering services                           |
| shared service providers                             |
| marketing                                            |
| marketing/ict services                               |
| community participation and support                  |
| hardware                                             |
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       |
| 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       |
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:
| url(asetuksen viitekortti) teksti    |
|:-------------------------------------|
| https://www.edilex.fi/smur/20100128/ |
| https://www.edilex.fi/smur/19931501/ |
| https://www.edilex.fi/smur/20010288/ |
| https://www.edilex.fi/smur/20070893/ |
| https://www.edilex.fi/smur/19930874/ |
| https://www.edilex.fi/smur/19990895/ |
| https://www.edilex.fi/smur/19960970/ |
| https://www.edilex.fi/smur/19960960/ |
| https://www.edilex.fi/smur/19890506/ |
| https://www.edilex.fi/smur/20140416/ |
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:
| country              |
|:---------------------|
| china                |
| malaysia             |
| gibraltar            |
| united arab emirates |
| united kingdom       |
| france               |
| united states        |
| zimbabwe             |
| non us               |
| hong kong            |
| sweden               |
| australia            |
| netherlands          |
| jordan               |
| germany              |
| philippines          |
| us                   |
| canada               |
| brazil               |
| poland               |
| turkey               |
| malta                |
| trinidad & tobago    |
| belgium              |
| israel               |
| japan                |
| india                |
| taiwan               |
| mauritius            |
| croatia              |
| spain                |
| mexico               |
| argentina            |
| south korea          |
| zambia               |
| bangladesh           |
| kenya                |
| bulgaria             |
| ireland              |
| denmark              |
| jamaica              |
| sri lanka            |
| vietnam              |
| greece               |
| nigeria              |
| chile                |
| italy                |
| new zealand          |
| south africa         |
| switzerland          |
| singapore            |
| egypt                |
| portugal             |
| lithuania            |
| ghana                |
| austria              |
| macau                |
| russia               |
| cyprus               |
| pakistan             |
| norway               |
| latvia               |
| ukraine              |
| cambodia             |
| namibia              |
| finland              |
| estonia              |
| czech republic       |
| isle of man          |
| indonesia            |
| venezuela            |
| romania              |
| channel islands      |
| botswana             |
| cameroon             |
| ivory coast          |
| monaco               |
| morocco              |
| tanzania             |
| thailand             |
| panama               |
| peru                 |
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:
| relationship   |
|:---------------|
| mentions       |
| tweet          |
| replies to     |
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:
| ชื่อ สกุล             |
|:-------------------|
| นางแสงจันทร์ ดีนี      |
| นางทองคำ กุหลาบ     |
| นางอยู่ ทาจู          |
| นายใช้ สกลเกียรติกำจร |
| นายประยูร คำพันธ์     |
| นางทอง กระจง       |
| นางมิ่ง มาว่อง        |
| นางจำปี ชะลารัมย์     |
| นายทองอยู่ กาสาขาว   |
| นายสมหวัง พันธ์ศิริ     |
| นายน้อม ก่อมแก้ว      |
| นาง นาง  สมนันท์     |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
|   base model |
|-------------:|
|       0.0099 |
|       0.0015 |
|       0.0001 |
|       0.0123 |
|       0.0127 |
|       0.0019 |
|       0.0143 |
|       0.0137 |
|       0.0048 |
|       0.0114 |
|       0.0088 |
|       0.0209 |
|       0.0446 |
|       0.0098 |
|       0.013  |
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         |
|:----------|
| leeshelle |
| jamie     |
| madeline  |
| billy     |
| paige     |
| mia       |
| jessie    |
| laura     |
| zara      |
| sophie    |
| clemency  |
| angus     |
| scarlett  |
| william   |
| stephanie |
| lucy      |
| sienna    |
| riley     |
| brittney  |
| mitchell  |
| amelia    |
| oscar     |
| jessica   |
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 url                                                |
|:---------------------------------------------------------|
| https://api.twitter.com/1.1/geo/id/c0b8e8dc81930292.json |
| https://api.twitter.com/1.1/geo/id/01fbe706f872cb32.json |
| https://api.twitter.com/1.1/geo/id/339cbb54d4049e36.json |
| https://api.twitter.com/1.1/geo/id/1cfa1623a8eb155e.json |
| https://api.twitter.com/1.1/geo/id/319ee7b36c9149da.json |
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:
| agency requested   |
|:-------------------|
| dms                |
| lottery            |
| dep                |
| fdc                |
| doh                |
| dot                |
| dfs                |
| state courts       |
| ast                |
| apd                |
| doea               |
| fdle               |
| fwc                |
| ahca               |
| dea                |
| dva                |
| hsmv               |
| dor                |
| dcf                |
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:
| data element sub-category   |
|:----------------------------|
| signs & symptoms            |
| obstetrical history         |
| disease/condition           |
| encounter / patient visit   |
| vital status                |
| patient identification      |
| patient location            |
| case identification         |
| exposure location           |
| exposure                    |
| classification / criteria   |
| classification/ criteria    |
| outbreak                    |
| reporting                   |
| reporting - date            |
| reporting - location        |
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:
| ct1                     |
|:------------------------|
| balmoral                |
| freedom of the seas     |
| enchantment of the seas |
| 0                       |
| norwegian breakaway     |
| adonia                  |
| aidavita                |
| norwegian spirit        |
| anthem of the seas      |
| queen victoria          |
| event                   |
| oriana                  |
| majesty of the seas     |


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