Task: equi-join-detect (source dataset: Auto-BI)

Your are given a database with related tables used for business intelligence analysis. Your task is to identify all key/foreign-key join relationships between the tables for this database. Recall that a join is a relationship between two tables, that allows rows from one table to be combined with rows from another, based on a set of common join columns.
 
 
No explanation needed. Please return all join relationships in the following JSON format:
{
  "joins": [
    {
      "from_table": "table-1",
      "from_column": "column-1",
      "to_table": "table-2",
      "to_column": "column-3"
    }, 
	...
  ]
}

Tables:

Table: Athlete
|   P_Athlete_ID | Athlete_FullName          | Athlete_Gender   |
|---------------:|:--------------------------|:-----------------|
|              4 | William Gladstone Harwell | Men              |
|              6 | Gert Jan Schlatmann       | Men              |
|              7 | Victor Dolipschi          | Men              |
|              9 | Guy Colquhoun Richardson  | Men              |
|             11 | Sven Thofelt              | Men              |
|             12 | Taco Van Den Honert       | Men              |
|             13 | Marek Kolbowicz           | Men              |
|             14 | Jan Jozef Alfons Krekels  | Men              |
|             15 | David Lee Wharton         | Men              |
|             16 | Aleksandar Djordjevic     | Men              |


Table: Athlete_Country
|   P_AthCou_ID |   F_Athlete_ID |   F_Country_ID |
|--------------:|---------------:|---------------:|
|             9 |           5598 |              8 |
|            16 |          19608 |              8 |
|            25 |          14767 |              8 |
|            28 |          10468 |              8 |
|            54 |          19645 |              8 |
|            55 |          11871 |              8 |
|            70 |           1639 |              8 |
|            92 |          22006 |              8 |
|           111 |          24147 |              8 |
|           115 |          18859 |              8 |


Table: City
|   P_City_ID | City_Name              |
|------------:|:-----------------------|
|           1 | Garmisch Partenkirchen |
|           2 | Tokyo                  |
|           3 | Grenoble               |
|           4 | Sapporo                |
|           5 | Paris                  |
|           6 | Athens                 |
|           7 | Lillehammer            |
|           8 | Barcelona              |
|           9 | Rome                   |
|          10 | Munich                 |


Table: Country
|   P_Country_ID | Country_Name   | Country_NOCCode   |   Population |   GDP | FirstLetter_Country   |
|---------------:|:---------------|:------------------|-------------:|------:|:----------------------|
|              1 | Uzbekistan     | UZB               |     31299500 |  2132 | U                     |
|              2 | Greece         | GRE               |     10823732 | 18002 | G                     |
|              3 | Saudi Arabia   | KSA               |     31540372 | 20482 | S                     |
|              4 | Denmark        | DEN               |      5676002 | 51989 | D                     |
|              5 | Mongolia       | MGL               |      2959134 |  3968 | M                     |
|              6 | Zimbabwe       | ZIM               |     15602751 |   924 | Z                     |
|              7 | Unified Team   | EUN               |            0 |     0 | U                     |
|              8 | United States  | USA               |    321418820 | 56116 | U                     |
|              9 | Germany        | GER               |     81413145 | 41313 | G                     |
|             10 | Botswana       | BOT               |      2262485 |  6360 | B                     |


Table: Discipline
|   P_Discipline_ID | Discipline_Name   |   F_Sport_ID |
|------------------:|:------------------|-------------:|
|                 1 | Jumping           |           22 |
|                 2 | Roque             |           24 |
|                 3 | Golf              |            3 |
|                 4 | Artistic G.       |           43 |
|                 5 | BMX               |           13 |
|                 6 | Badminton         |            5 |
|                 7 | Rowing            |           39 |
|                 8 | Taekwondo         |           42 |
|                 9 | Rhythmic G.       |           43 |
|                10 | Tennis            |           19 |


Table: Event_City
|   P_EveCit_ID |   F_Event_ID |   F_City_ID |
|--------------:|-------------:|------------:|
|             4 |           20 |          40 |
|            24 |          465 |          40 |
|            49 |          680 |          40 |
|            58 |          558 |          40 |
|            69 |          284 |          40 |
|            80 |          117 |          40 |
|           118 |           44 |          40 |
|           121 |          223 |          40 |
|           122 |          312 |          40 |
|           126 |          595 |          40 |


Table: Event_Table
|   P_Event_ID | Event_Name                      |   F_Sport_ID |
|-------------:|:--------------------------------|-------------:|
|            6 | 100 - 130KG (Super Heavyweight) |           45 |
|            7 | 60 - 67.5KG (Lightweight)       |           45 |
|           10 | 75 - 82KG (Light-Heavyweight)   |           45 |
|           16 | 57 - 62KG (Featherweight)       |           45 |
|           25 | 78 - 87KG (Middleweight)        |           45 |
|           30 | Wf 66 KG                        |           45 |
|           40 | 54 - 58KG                       |           45 |
|           42 | 66 - 74KG                       |           45 |
|           58 | 61 - 66KG (Lightweight)         |           45 |
|           86 | 58 - 62KG (Featherweight)       |           45 |


Table: Medal
|   P_Medal_ID | Medal_Type   |
|-------------:|:-------------|
|            1 | Gold         |
|            2 | Bronze       |
|            3 | Silver       |


Table: Result
|   P_Result_ID |   Year |   F_AthCou_ID |   F_EveCit_ID |   F_Medal_ID | Century   |
|--------------:|-------:|--------------:|--------------:|-------------:|:----------|
|            46 |   2004 |          6037 |          1469 |            3 | 20's      |
|            81 |   2004 |         21355 |          2861 |            3 | 20's      |
|           234 |   2004 |          1608 |          3692 |            3 | 20's      |
|           243 |   2004 |         14451 |          2057 |            3 | 20's      |
|           251 |   2004 |          8612 |          3153 |            3 | 20's      |
|           281 |   2004 |          2013 |          3612 |            3 | 20's      |
|           486 |   2004 |          4988 |          1508 |            3 | 20's      |
|           499 |   2004 |          3327 |           823 |            3 | 20's      |
|           539 |   2004 |         19998 |          3223 |            3 | 20's      |
|           561 |   2004 |         13981 |          2858 |            3 | 20's      |


Table: Sport
|   P_Sport_ID | Sport_Name    | Season   |
|-------------:|:--------------|:---------|
|            1 | Rackets       | Summer   |
|            2 | Basque Pelota | Summer   |
|            3 | Golf          | Summer   |
|            4 | Biathlon      | Winter   |
|            5 | Badminton     | Summer   |
|            6 | Judo          | Summer   |
|            7 | Sailing       | Summer   |
|            8 | Softball      | Summer   |
|            9 | Athletics     | Summer   |
|           10 | Ice Hockey    | Summer   |

 


tables: name: Athlete
data: Athlete.csv
name: Athlete_Country
data: Athlete_Country.csv
name: City
data: City.csv
name: Country
data: Country.csv
name: Discipline
data: Discipline.csv
name: Event_City
data: Event_City.csv
name: Event_Table
data: Event_Table.csv
name: Medal
data: Medal.csv
name: Result
data: Result.csv
name: Sport
data: Sport.csv
label: Athlete_Country
F_Athlete_ID
Athlete
P_Athlete_ID
Athlete_Country
F_Country_ID
Country
P_Country_ID
Result
F_AthCou_ID
Athlete_Country
P_AthCou_ID
Event_City
F_City_ID
City
P_City_ID
Discipline
F_Sport_ID
Sport
P_Sport_ID
Event_City
F_Event_ID
Event_Table
P_Event_ID
Result
F_EveCit_ID
Event_City
P_EveCit_ID
Event_Table
F_Sport_ID
Sport
P_Sport_ID
Result
F_Medal_ID
Medal
P_Medal_ID
output: {
    "joins": [
        {
            "from_table": "Athlete_Country",
            "from_column": "F_Athlete_ID",
            "to_table": "Athlete",
            "to_column": "P_Athlete_ID"
        },
        {
            "from_table": "Athlete_Country",
            "from_column": "F_Country_ID",
            "to_table": "Country",
            "to_column": "P_Country_ID"
        },
        {
            "from_table": "Result",
            "from_column": "F_AthCou_ID",
            "to_table": "Athlete_Country",
            "to_column": "P_AthCou_ID"
        },
        {
            "from_table": "Event_City",
            "from_column": "F_City_ID",
            "to_table": "City",
            "to_column": "P_City_ID"
        },
        {
            "from_table": "Discipline",
            "from_column": "F_Sport_ID",
            "to_table": "Sport",
            "to_column": "P_Sport_ID"
        },
        {
            "from_table": "Event_City",
            "from_column": "F_Event_ID",
            "to_table": "Event_Table",
            "to_column": "P_Event_ID"
        },
        {
            "from_table": "Result",
            "from_column": "F_EveCit_ID",
            "to_table": "Event_City",
            "to_column": "P_EveCit_ID"
        },
        {
            "from_table": "Event_Table",
            "from_column": "F_Sport_ID",
            "to_table": "Sport",
            "to_column": "P_Sport_ID"
        },
        {
            "from_table": "Result",
            "from_column": "F_Medal_ID",
            "to_table": "Medal",
            "to_column": "P_Medal_ID"
        }
    ]
}