Task: semantic-join (source dataset: SEMA-join)

You are given two input tables below. Your task is to identify the most likely semantic relationships between values of these two columns, and then output pairs of values from Table 1 and Table 2, that can be joined/linked based on the inferred semantic relationships. 

No explanation is needed, only return your answer in JSON format: {"output": <answer>}, where the <answer> is a list of lists. Each inner list should contain exactly one value from Table 1, and its semantically matching value from Table 2. If a value from Table 1 does not have corresponding value from Table 2, you can omit it in the answer. But be careful, not to miss any values from Table 1 that do have corresponding matching values in Table 2.

Input Table 1:
| Col_0                                      |
|:-------------------------------------------|
| BAIC                                       |
| Chrysler                                   |
| Suzuki                                     |
| China National Heavy Duty Truck            |
| AvtoVAZ                                    |
| Shannxi                                    |
| Hyundai                                    |
| Tata                                       |
| PSA                                        |
| Chery                                      |
| Mazda                                      |
| Navistar                                   |
| Paccar                                     |
| Sichuan Nanjun                             |
| Daimler AG                                 |
| South East (Fujian)                        |
| Guangzhou Auto Industry                    |
| Chongqing Lifan Motor Co                   |
| Toyota                                     |
| Tangjun Ou Ling                            |
| Brilliance                                 |
| GAZ                                        |
| Volkswagen                                 |
| Volvo                                      |
| Hunan Jiangnan Automobile Manufacturing Co |
| Hebei Zhongxing                            |
| BYD                                        |
| Honda                                      |
| SAIC                                       |
| Fuji                                       |
| UAZ                                        |
| Dongfeng Motor                             |
| Xiamen King Long                           |
| Proton                                     |
| Porsche                                    |
| Isuzu                                      |
| Ashok Leyland                              |
| Changan                                    |
| Renault                                    |
| Geely                                      |
| GM                                         |
| Mitsubishi                                 |
| BMW                                        |
| JAC                                        |
| Mahindra                                   |
| Great Wall                                 |
| Fiat                                       |
| Ford                                       |
| FAW                                        |
| Nissan                                     |

Input Table 2:
| Col_1         |
|:--------------|
| South Korea   |
| Sweden        |
| United States |
| Japan         |
| China         |
| Italy         |
| Malaysia      |
| France        |
| Germany       |
| India         |
| Russia        |

Joined Table:



label: Toyota
Japan
GM
United States
Volkswagen
Germany
Hyundai
South Korea
Ford
United States
Nissan
Japan
Honda
Japan
PSA
France
Suzuki
Japan
Renault
France
Chrysler
United States
Daimler AG
Germany
Fiat
Italy
BMW
Germany
SAIC
China
Tata
India
Mazda
Japan
Dongfeng Motor
China
Mitsubishi
Japan
Changan
China
Geely
China
Fuji
Japan
BAIC
China
FAW
China
Great Wall
China
Mahindra
India
Isuzu
Japan
Chery
China
AvtoVAZ
Russia
Brilliance
China
JAC
China
BYD
China
Chongqing Lifan Motor Co
China
Volvo
Sweden
Proton
Malaysia
China National Heavy Duty Truck
China
Paccar
United States
GAZ
Russia
Ashok Leyland
India
Hunan Jiangnan Automobile Manufacturing Co
China
Guangzhou Auto Industry
China
Shannxi
China
Porsche
Germany
South East (Fujian)
China
Navistar
United States
Xiamen King Long
China
UAZ
Russia
Tangjun Ou Ling
China
Hebei Zhongxing
China
Sichuan Nanjun
China