Task: Schema-Matching (source dataset: HXD)

Task: 
Please identify the matching columns between Table A and Table B. For each column in Table A, specify the corresponding column in Table B. If a column in A has no corresponding column in Table B, you can map it to null. Represent each column mapping using a pair of column headers in a list, i.e., [Table A Column, Table B column or null]. Provide the mapping for each column in Table A and return all mappings in a list.

No explanation and return the matching columns in a structured JSON format: {"column_mappings": <a list of column pairs>}.

TableA:
| Country      | Sector      |   People in need | Total population   |
|:-------------|:------------|-----------------:|:-------------------|
| Chad         | MS          |           388000 |                    |
| Mauritania   | FS          |           493000 | 370000             |
| Burkina Faso | Prepareness |            57000 |                    |
| Cameroon     | Health      |         14402010 | 1438614            |
| Mauritania   | Nutrition   |           165000 | 147000             |
| Mali         | WASH        |          1200000 | 859000             |
| Cameroon     | Education   |           373084 | 280462             |
| Niger        | Health      |           766000 |                    |
| Mauritania   | Health      |           203000 | 203000             |
| Mauritania   | WASH        |           615000 | 250000             |

TableB:
| country   | country_pcode   | region    | region_pcode   | cercle    | cercle_pcode   |   population_2021 | sector                 |   people_in_need |
|:----------|:----------------|:----------|:---------------|:----------|:---------------|------------------:|:-----------------------|-----------------:|
| Mali      | ML              | Kidal     | ML08           | Tessalit  | ML0804         |           22318.3 | Sécurité alimentaire   |          2626.56 |
| Mali      | ML              | Koulikoro | ML02           | Koulikoro | ML0201         |          292848   | Nutrition              |          1494.51 |
| Mali      | ML              | Sikasso   | ML03           | Bougouni  | ML0306         |          628825   | Nutrition              |          3255.05 |
| Mali      | ML              | Segou     | ML04           | San       | ML0404         |          463479   | Nutrition              |         49820.2  |
| Mali      | ML              | Koulikoro | ML02           | Kolokani  | ML0206         |          323807   | Education              |             0    |
| Mali      | ML              | Segou     | ML04           | San       | ML0404         |          463479   | Abris d'urgence et NFI |          6333.96 |
| Mali      | ML              | Kayes     | ML01           | Nioro     | ML0106         |          318793   | Protection             |         27462    |
| Mali      | ML              | Mopti     | ML05           | Djenne    | ML0505         |          285830   | Sécurité alimentaire   |         52530.1  |
| Mali      | ML              | Gao       | ML07           | Ansongo   | ML0702         |          186522   | Protection             |        168334    |
| Mali      | ML              | Koulikoro | ML02           | Nara      | ML0207         |          337108   | Nutrition              |         31058.7  |



source_headers: Country
Sector 
People in need
target_headers: country
sector
people_in_need
column_mappings: Country
country
Sector 
sector
People in need
people_in_need
output: {"column_mappings": [["Country", "country"], ["Sector ", "sector"], ["People in need", "people_in_need"], ["Total population", null]]}