Task: NL2SQL (source dataset: KaggleDBQA)

Natural language to SQL, also known as NL2SQL, is the problem of generating a SQL query that can be executed on a input table, for a given natural language question.

You will be given an input table called "table" in the markdown format, as well as a question. Your task is to generate an SQL query that can be executed on the input table to answer the question.

Please ensure that the generated SQL only return relevant columns being specifically asked in the question (e.g., in SQL please don't return more than one columns in SELECT if only one column is needed to answer the given question, and similarly please refrain from using SELECT * unless the entire row is needed to answer the question).
 
When generating SQL code, please use the SQLite syntax to ensure that your SQL is executable on SQLite3. Use only columns that are present in the table, always use quotes around your column and table names (some column names have punctuation or special characters), and refer to the columns using the column-names exactly as they appear in the table, do not add or remove punctuation (underscore, slash, etc.)

No explanation, return the code only with the following markdown codeblock format : ```sql<SQL CODE>```. For example, ```sql
SELECT * FROM "table"
``` is a valid format.

Database:
GreaterManchesterCrime

Tables:

Table: GreaterManchesterCrime
| CrimeID           | CrimeTS             | Location                       | LSOA            | Type                         | Outcome                                       |
|:------------------|:--------------------|:-------------------------------|:----------------|:-----------------------------|:----------------------------------------------|
| 0F:67:BA:85:59:6E | 2018-08-24 22:22:01 | On or near Holland Street      | Tameside 025A   | Violence and sexual offences | Under investigation                           |
| E1:68:4D:92:FB:10 | 2018-08-23 15:57:24 | On or near Back Burnaby Street | Bolton 021E     | Violence and sexual offences | Unable to prosecute suspect                   |
| F3:AF:79:03:F9:55 | 2018-08-29 07:47:27 | On or near Aldworth Grove      | Trafford 019D   | Burglary                     | Investigation complete; no suspect identified |
| 33:F2:BA:C7:2D:88 | 2018-08-06 19:33:11 | On or near Shrewsbury Close    | Wigan 016A      | Burglary                     | Investigation complete; no suspect identified |
| D2:70:22:5F:62:15 | 2018-08-13 16:44:27 | On or near Pym Street          | Manchester 007B | Violence and sexual offences | Investigation complete; no suspect identified |
| C0:D2:21:9B:EE:8A | 2018-08-26 22:41:09 | On or near Debenham Avenue     | Manchester 012E | Public order                 | Investigation complete; no suspect identified |
| E8:8B:3A:F9:7F:89 | 2018-08-25 19:08:15 | On or near Parking Area        | Manchester 050E | Criminal damage and arson    | Investigation complete; no suspect identified |
| 5E:F0:0E:B9:E1:FB | 2018-08-06 14:15:50 | On or near Airport/Airfield    | Manchester 053D | Shoplifting                  | Awaiting court outcome                        |
| 0E:B3:34:3A:AE:42 | 2018-08-21 20:42:30 | On or near Redmere Drive       | Bury 013C       | Public order                 | Under investigation                           |
| 75:4F:3B:02:1C:B8 | 2018-08-13 04:41:40 | On or near Derwent Street      | Tameside 012C   | Violence and sexual offences | Unable to prosecute suspect                   |


Question: What’s the most common type of crime?

db_id: GreaterManchesterCrime
tables: name: GreaterManchesterCrime
data: GreaterManchesterCrime.csv
label: SELECT Type FROM GreaterManchesterCrime GROUP BY Type ORDER BY count(*) DESC LIMIT 1
sqlite_path: $MMTU_HOME/data_sqlite/KaggleDBQA/databases/GreaterManchesterCrime/GreaterManchesterCrime.sqlite