Task: NL2SQL (source dataset: Spider)

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:
planet_1

Tables:

Table: Employee
|   EmployeeID | Name                     | Position     |   Salary | Remarks                                                                 |
|-------------:|:-------------------------|:-------------|---------:|:------------------------------------------------------------------------|
|            1 | Phillip J. Fry           | Delivery boy |     7500 | Not to be confused with the Philip J. Fry from Hovering Squid World 97a |
|            2 | Turanga Leela            | Captain      |    10000 |                                                                         |
|            3 | Bender Bending Rodriguez | Robot        |     7500 |                                                                         |
|            4 | Hubert J. Farnsworth     | CEO          |    20000 |                                                                         |
|            5 | John A. Zoidberg         | Physician    |       25 |                                                                         |
|            6 | Amy Wong                 | Intern       |     5000 |                                                                         |
|            7 | Hermes Conrad            | Bureaucrat   |    10000 |                                                                         |
|            8 | Scruffy Scruffington     | Janitor      |     5000 |                                                                         |


Table: Planet
|   PlanetID | Name             |   Coordinates |
|-----------:|:-----------------|--------------:|
|          1 | Omicron Persei 8 |   8.94753e+07 |
|          2 | Decapod X        |   6.54985e+10 |
|          3 | Mars             |   3.2435e+07  |
|          4 | Omega III        |   9.84321e+07 |
|          5 | Tarantulon VI    |   8.49842e+08 |
|          6 | Cannibalon       |   6.54322e+08 |
|          7 | DogDoo VII       |   6.54987e+10 |
|          8 | Nintenduu 64     |   6.54322e+09 |
|          9 | Amazonia         |   6.54321e+10 |


Table: Shipment
|   ShipmentID | Date       |   Manager |   Planet |
|-------------:|:-----------|----------:|---------:|
|            1 | 3004/05/11 |         1 |        1 |
|            2 | 3004/05/11 |         1 |        2 |
|            3 |            |         2 |        3 |
|            4 |            |         2 |        4 |
|            5 |            |         7 |        5 |


Table: Has_Clearance
|   Employee |   Planet |   Level |
|-----------:|---------:|--------:|
|          1 |        1 |       2 |
|          1 |        2 |       3 |
|          2 |        3 |       2 |
|          2 |        4 |       4 |
|          3 |        5 |       2 |
|          3 |        6 |       4 |
|          4 |        7 |       1 |


Table: Client
|   AccountNumber | Name              |
|----------------:|:------------------|
|               1 | Zapp Brannigan    |
|               2 | Al Gore's Head    |
|               3 | Barbados Slim     |
|               4 | Ogden Wernstrom   |
|               5 | Leo Wong          |
|               6 | Lrrr              |
|               7 | John Zoidberg     |
|               8 | John Zoidfarb     |
|               9 | Morbo             |
|              10 | Judge John Whitey |


Table: Package
|   Shipment |   PackageNumber | Contents          |   Weight |   Sender |   Recipient |
|-----------:|----------------:|:------------------|---------:|---------:|------------:|
|          1 |               1 | Undeclared        |      1.5 |        1 |           2 |
|          2 |               1 | Undeclared        |     10   |        2 |           3 |
|          2 |               2 | A bucket of krill |      2   |        8 |           7 |
|          3 |               1 | Undeclared        |     15   |        3 |           4 |
|          3 |               2 | Undeclared        |      3   |        5 |           1 |
|          3 |               3 | Undeclared        |      7   |        2 |           3 |
|          4 |               1 | Undeclared        |      5   |        4 |           5 |
|          4 |               2 | Undeclared        |     27   |        1 |           2 |
|          5 |               1 | Undeclared        |    100   |        5 |           1 |


Question: What level is Physician?

db_id: planet_1
tables: name: Employee
data: Employee.csv
name: Planet
data: Planet.csv
name: Shipment
data: Shipment.csv
name: Has_Clearance
data: Has_Clearance.csv
name: Client
data: Client.csv
name: Package
data: Package.csv
label: SELECT T1.Level FROM Has_Clearance AS T1 JOIN Employee AS T2 ON T1.Employee = T2.EmployeeID WHERE T2.position  =  "Physician";
sqlite_path: $MMTU_HOME/data_sqlite/spider/spider_data/test_database/planet_1/planet_1.sqlite