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:
aan_1

Tables:

Table: Affiliation
|   affiliation_id | name                                 | address           |
|-----------------:|:-------------------------------------|:------------------|
|                0 | 20/20 Speech Ltd.                    | Malvern UK        |
|                1 | 3SOFT GmbH                           | Erlangen Germany  |
|                2 | A-Life Medical, Inc.                 | San Diego CA      |
|                3 | A.I. DuPont Hospital for Children    | Wilmington DE     |
|                4 | ABP Pvt. Ltd.                        | Calcutta India    |
|                5 | AE Solutions                         | Worcestershire UK |
|                6 | AETHRA                               | Ancona Italy      |
|                7 | AIA Information Technology Co., Ltd. | Beijing China     |
|                8 | AOL India R&D                        | Bangalore India   |
|                9 | AT&T Bell Labs                       | Columbus OH       |


Table: Author
|   author_id | name   | email   |
|------------:|:-------|:--------|
|           0 |        |         |
|           1 | &#269  |         |
|           2 | &#353  |         |
|           3 | &#x106 |         |
|           4 | &#x107 |         |
|           5 | &#x10D |         |
|           6 | &#x110 |         |
|           7 | &#x130 |         |
|           8 | &#x131 |         |
|           9 | &#x141 |         |


Table: Author_list
| paper_id   |   author_id | affiliation_id   |
|:-----------|------------:|:-----------------|
| D15-1001   |        1233 |                  |
| D15-1001   |        9573 |                  |
| D15-1001   |       12785 |                  |
| D15-1002   |        1194 |                  |
| D15-1002   |        1824 |                  |
| D15-1002   |        6512 |                  |
| D15-1002   |       13588 |                  |
| D15-1003   |        7131 |                  |
| D15-1003   |       18526 |                  |
| D15-1004   |       10343 |                  |


Table: Citation
| paper_id   | cited_paper_id   |
|:-----------|:-----------------|
| C08-3004   | A00-1002         |
| D09-1141   | A00-1002         |
| D12-1027   | A00-1002         |
| E06-1047   | A00-1002         |
| H05-1110   | A00-1002         |
| N01-1020   | A00-1002         |
| N13-1036   | A00-1002         |
| P13-2001   | A00-1002         |
| P13-2073   | A00-1002         |
| W11-2602   | A00-1002         |


Table: Paper
| paper_id   | title                                                                                              | venue   |   year |
|:-----------|:---------------------------------------------------------------------------------------------------|:--------|-------:|
| A00-1001   | BusTUC - A Natural Language Bus Route Oracle                                                       | NAACL   |   2000 |
| A00-1002   | Machine Translation Of Very Close Languages                                                        | NAACL   |   2000 |
| A00-1003   | Cross-Language Multimedia Information Retrieval                                                    | NAACL   |   2000 |
| A00-1004   | Automatic Construction Of Parallel English-Chinese Corpus For Cross-Language Information Retrieval | NAACL   |   2000 |
| A00-1005   | PartsID: A Dialogue-Based System For Identifying Parts For Medical Systems                         | NAACL   |   2000 |
| A00-1006   | Translation Using Information On Dialogue Participants                                             | NAACL   |   2000 |
| A00-1007   | Distilling Dialogues - A Method Using Natural Dialogue Corpora For Dialogue Systems Development    | NAACL   |   2000 |
| A00-1008   | Plan-Based Dialogue Management In A Physics Tutor                                                  | NAACL   |   2000 |
| A00-1009   | A Framework For MT And Multilingual NLG Systems Based On Uniform Lexico-Structural Processing      | NAACL   |   2000 |
| A00-1010   | Talk'n'Travel: A Conversational System For Air Travel Planning                                     | NAACL   |   2000 |


Question: How many papers does Stanford University have between 2000 and 2009?

db_id: aan_1
tables: name: Affiliation
data: Affiliation.csv
name: Author
data: Author.csv
name: Author_list
data: Author_list.csv
name: Citation
data: Citation.csv
name: Paper
data: Paper.csv
label: SELECT count(DISTINCT T1.paper_id) FROM Paper AS T1 JOIN Author_list AS T2 ON T1.paper_id  =  T2.paper_id JOIN Affiliation AS T3 ON T2.affiliation_id  =  T3.affiliation_id WHERE T1.year  >=  2000 AND T1.year  <=  2009 AND T3.name LIKE "Stanford University"
sqlite_path: $MMTU_HOME/data_sqlite/spider/spider_data/test_database/aan_1/aan_1.sqlite