1sf/bird-sql-train-with-schema huggingface dataset:
Database: mondial_geo
Tables: borders, city, continent, country, desert, economy, encompasses, ethnicGroup, geo_desert, geo_estuary, geo_island, geo_lake, geo_mountain, geo_river, geo_sea, geo_source, island, islandIn, isMember, lake, language, located, locatedOn, mergesWith, mountain, mountainOnIsland, organization, politics, population, province, religion, river, sea, target
Columns (table index, column name): -1, *, 0, Country1, 0, Country2, 0, Length, 1, Name, 1, Country, 1, Province, 1, Population, 1, Longitude, 1, Latitude, 2, Name, 2, Area, 3, Name, 3, Code, 3, Capital, 3, Province, 3, Area, 3, Population, 4, Name, 4, Area, 4, Longitude, 4, Latitude, 5, Country, 5, GDP, 5, Agriculture, 5, Service, 5, Industry, 5, Inflation, 6, Country, 6, Continent, 6, Percentage, 7, Country, 7, Name, 7, Percentage, 8, Desert, 8, Country, 8, Province, 9, River, 9, Country, 9, Province, 10, Island, 10, Country, 10, Province, 11, Lake, 11, Country, 11, Province, 12, Mountain, 12, Country, 12, Province, 13, River, 13, Country, 13, Province, 14, Sea, 14, Country, 14, Province, 15, River, 15, Country, 15, Province, 16, Name, 16, Islands, 16, Area, 16, Height, 16, Type, 16, Longitude, 16, Latitude, 17, Island, 17, Sea, 17, Lake, 17, River, 18, Country, 18, Organization, 18, Type, 19, Name, 19, Area, 19, Depth, 19, Altitude, 19, Type, 19, River, 19, Longitude, 19, Latitude, 20, Country, 20, Name, 20, Percentage, 21, City, 21, Province, 21, Country, 21, River, 21, Lake, 21, Sea, 22, City, 22, Province, 22, Country, 22, Island, 23, Sea1, 23, Sea2, 24, Name, 24, Mountains, 24, Height, 24, Type, 24, Longitude, 24, Latitude, 25, Mountain, 25, Island, 26, Abbreviation, 26, Name, 26, City, 26, Country, 26, Province, 26, Established, 27, Country, 27, Independence, 27, Dependent, 27, Government, 28, Country, 28, Population_Growth, 28, Infant_Mortality, 29, Name, 29, Country, 29, Population, 29, Area, 29, Capital, 29, CapProv, 30, Country, 30, Name, 30, Percentage, 31, Name, 31, River, 31, Lake, 31, Sea, 31, Length, 31, SourceLongitude, 31, SourceLatitude, 31, Mountains, 31, SourceAltitude, 31, EstuaryLongitude, 31, EstuaryLatitude, 32, Name, 32, Depth, 33, Country, 33, Target
Question: Based on the data shown at Target, what percentage of countries are non-Christian?
Evidence: percentage of countries are non-Christian = [count(non-Christian) / count(non-Christian + Christian)] * 100%
SQL: SELECT 100 - (CAST(SUM(CASE WHEN Target = 'Christian' THEN 1 ELSE 0 END) AS REAL)) * 100 / COUNT(Country) FROM target
Database: mondial_geo
Tables: borders, city, continent, country, desert, economy, encompasses, ethnicGroup, geo_desert, geo_estuary, geo_island, geo_lake, geo_mountain, geo_river, geo_sea, geo_source, island, islandIn, isMember, lake, language, located, locatedOn, mergesWith, mountain, mountainOnIsland, organization, politics, population, province, religion, river, sea, target
Columns (table index, column name): -1, *, 0, Country1, 0, Country2, 0, Length, 1, Name, 1, Country, 1, Province, 1, Population, 1, Longitude, 1, Latitude, 2, Name, 2, Area, 3, Name, 3, Code, 3, Capital, 3, Province, 3, Area, 3, Population, 4, Name, 4, Area, 4, Longitude, 4, Latitude, 5, Country, 5, GDP, 5, Agriculture, 5, Service, 5, Industry, 5, Inflation, 6, Country, 6, Continent, 6, Percentage, 7, Country, 7, Name, 7, Percentage, 8, Desert, 8, Country, 8, Province, 9, River, 9, Country, 9, Province, 10, Island, 10, Country, 10, Province, 11, Lake, 11, Country, 11, Province, 12, Mountain, 12, Country, 12, Province, 13, River, 13, Country, 13, Province, 14, Sea, 14, Country, 14, Province, 15, River, 15, Country, 15, Province, 16, Name, 16, Islands, 16, Area, 16, Height, 16, Type, 16, Longitude, 16, Latitude, 17, Island, 17, Sea, 17, Lake, 17, River, 18, Country, 18, Organization, 18, Type, 19, Name, 19, Area, 19, Depth, 19, Altitude, 19, Type, 19, River, 19, Longitude, 19, Latitude, 20, Country, 20, Name, 20, Percentage, 21, City, 21, Province, 21, Country, 21, River, 21, Lake, 21, Sea, 22, City, 22, Province, 22, Country, 22, Island, 23, Sea1, 23, Sea2, 24, Name, 24, Mountains, 24, Height, 24, Type, 24, Longitude, 24, Latitude, 25, Mountain, 25, Island, 26, Abbreviation, 26, Name, 26, City, 26, Country, 26, Province, 26, Established, 27, Country, 27, Independence, 27, Dependent, 27, Government, 28, Country, 28, Population_Growth, 28, Infant_Mortality, 29, Name, 29, Country, 29, Population, 29, Area, 29, Capital, 29, CapProv, 30, Country, 30, Name, 30, Percentage, 31, Name, 31, River, 31, Lake, 31, Sea, 31, Length, 31, SourceLongitude, 31, SourceLatitude, 31, Mountains, 31, SourceAltitude, 31, EstuaryLongitude, 31, EstuaryLatitude, 32, Name, 32, Depth, 33, Country, 33, Target
Question: Which country with a city with a population between 50,000 and 300,000 inhabitants and which is a member of an organization established between 03/01/1991 and 04/30/1991 is also a member of the EBRD?
SQL: SELECT T2.Country FROM country AS T1 INNER JOIN isMember AS T2 ON T1.Code = T2.Country INNER JOIN organization AS T3 ON T3.Country = T2.Country INNER JOIN city AS T4 ON T4.Country = T3.Country WHERE T3.Abbreviation = 'EBRD' AND T4.Population BETWEEN 50000 AND 300000 AND T3.Established BETWEEN '1991-01-31' AND '1991-04-30'
Database: mondial_geo
Tables: borders, city, continent, country, desert, economy, encompasses, ethnicGroup, geo_desert, geo_estuary, geo_island, geo_lake, geo_mountain, geo_river, geo_sea, geo_source, island, islandIn, isMember, lake, language, located, locatedOn, mergesWith, mountain, mountainOnIsland, organization, politics, population, province, religion, river, sea, target
Columns (table index, column name): -1, *, 0, Country1, 0, Country2, 0, Length, 1, Name, 1, Country, 1, Province, 1, Population, 1, Longitude, 1, Latitude, 2, Name, 2, Area, 3, Name, 3, Code, 3, Capital, 3, Province, 3, Area, 3, Population, 4, Name, 4, Area, 4, Longitude, 4, Latitude, 5, Country, 5, GDP, 5, Agriculture, 5, Service, 5, Industry, 5, Inflation, 6, Country, 6, Continent, 6, Percentage, 7, Country, 7, Name, 7, Percentage, 8, Desert, 8, Country, 8, Province, 9, River, 9, Country, 9, Province, 10, Island, 10, Country, 10, Province, 11, Lake, 11, Country, 11, Province, 12, Mountain, 12, Country, 12, Province, 13, River, 13, Country, 13, Province, 14, Sea, 14, Country, 14, Province, 15, River, 15, Country, 15, Province, 16, Name, 16, Islands, 16, Area, 16, Height, 16, Type, 16, Longitude, 16, Latitude, 17, Island, 17, Sea, 17, Lake, 17, River, 18, Country, 18, Organization, 18, Type, 19, Name, 19, Area, 19, Depth, 19, Altitude, 19, Type, 19, River, 19, Longitude, 19, Latitude, 20, Country, 20, Name, 20, Percentage, 21, City, 21, Province, 21, Country, 21, River, 21, Lake, 21, Sea, 22, City, 22, Province, 22, Country, 22, Island, 23, Sea1, 23, Sea2, 24, Name, 24, Mountains, 24, Height, 24, Type, 24, Longitude, 24, Latitude, 25, Mountain, 25, Island, 26, Abbreviation, 26, Name, 26, City, 26, Country, 26, Province, 26, Established, 27, Country, 27, Independence, 27, Dependent, 27, Government, 28, Country, 28, Population_Growth, 28, Infant_Mortality, 29, Name, 29, Country, 29, Population, 29, Area, 29, Capital, 29, CapProv, 30, Country, 30, Name, 30, Percentage, 31, Name, 31, River, 31, Lake, 31, Sea, 31, Length, 31, SourceLongitude, 31, SourceLatitude, 31, Mountains, 31, SourceAltitude, 31, EstuaryLongitude, 31, EstuaryLatitude, 32, Name, 32, Depth, 33, Country, 33, Target
Question: Which river with its mouth in the Donau River and a length greater than 500 km is located in Slovenia?
SQL: SELECT T2.River FROM country AS T1 INNER JOIN geo_river AS T2 ON T1.Code = T2.Country WHERE T1.Name = 'Slovenia' AND T2.River IN ( SELECT NAME FROM river WHERE Length > 500 AND River = 'Donau' )
Database: mondial_geo
Tables: borders, city, continent, country, desert, economy, encompasses, ethnicGroup, geo_desert, geo_estuary, geo_island, geo_lake, geo_mountain, geo_river, geo_sea, geo_source, island, islandIn, isMember, lake, language, located, locatedOn, mergesWith, mountain, mountainOnIsland, organization, politics, population, province, religion, river, sea, target
Columns (table index, column name): -1, *, 0, Country1, 0, Country2, 0, Length, 1, Name, 1, Country, 1, Province, 1, Population, 1, Longitude, 1, Latitude, 2, Name, 2, Area, 3, Name, 3, Code, 3, Capital, 3, Province, 3, Area, 3, Population, 4, Name, 4, Area, 4, Longitude, 4, Latitude, 5, Country, 5, GDP, 5, Agriculture, 5, Service, 5, Industry, 5, Inflation, 6, Country, 6, Continent, 6, Percentage, 7, Country, 7, Name, 7, Percentage, 8, Desert, 8, Country, 8, Province, 9, River, 9, Country, 9, Province, 10, Island, 10, Country, 10, Province, 11, Lake, 11, Country, 11, Province, 12, Mountain, 12, Country, 12, Province, 13, River, 13, Country, 13, Province, 14, Sea, 14, Country, 14, Province, 15, River, 15, Country, 15, Province, 16, Name, 16, Islands, 16, Area, 16, Height, 16, Type, 16, Longitude, 16, Latitude, 17, Island, 17, Sea, 17, Lake, 17, River, 18, Country, 18, Organization, 18, Type, 19, Name, 19, Area, 19, Depth, 19, Altitude, 19, Type, 19, River, 19, Longitude, 19, Latitude, 20, Country, 20, Name, 20, Percentage, 21, City, 21, Province, 21, Country, 21, River, 21, Lake, 21, Sea, 22, City, 22, Province, 22, Country, 22, Island, 23, Sea1, 23, Sea2, 24, Name, 24, Mountains, 24, Height, 24, Type, 24, Longitude, 24, Latitude, 25, Mountain, 25, Island, 26, Abbreviation, 26, Name, 26, City, 26, Country, 26, Province, 26, Established, 27, Country, 27, Independence, 27, Dependent, 27, Government, 28, Country, 28, Population_Growth, 28, Infant_Mortality, 29, Name, 29, Country, 29, Population, 29, Area, 29, Capital, 29, CapProv, 30, Country, 30, Name, 30, Percentage, 31, Name, 31, River, 31, Lake, 31, Sea, 31, Length, 31, SourceLongitude, 31, SourceLatitude, 31, Mountains, 31, SourceAltitude, 31, EstuaryLongitude, 31, EstuaryLatitude, 32, Name, 32, Depth, 33, Country, 33, Target
Question: In which city is the sea whose depth is 4232 meters less than that of the Bay of Bengal?
SQL: SELECT T2.City FROM sea AS T1 INNER JOIN located AS T2 ON T1.Name = T2.Sea INNER JOIN city AS T3 ON T3.Name = T2.City WHERE ( SELECT Depth FROM sea WHERE Name LIKE '%Bengal%' ) - T1.Depth = 4235
Database: mondial_geo
Tables: borders, city, continent, country, desert, economy, encompasses, ethnicGroup, geo_desert, geo_estuary, geo_island, geo_lake, geo_mountain, geo_river, geo_sea, geo_source, island, islandIn, isMember, lake, language, located, locatedOn, mergesWith, mountain, mountainOnIsland, organization, politics, population, province, religion, river, sea, target
Columns (table index, column name): -1, *, 0, Country1, 0, Country2, 0, Length, 1, Name, 1, Country, 1, Province, 1, Population, 1, Longitude, 1, Latitude, 2, Name, 2, Area, 3, Name, 3, Code, 3, Capital, 3, Province, 3, Area, 3, Population, 4, Name, 4, Area, 4, Longitude, 4, Latitude, 5, Country, 5, GDP, 5, Agriculture, 5, Service, 5, Industry, 5, Inflation, 6, Country, 6, Continent, 6, Percentage, 7, Country, 7, Name, 7, Percentage, 8, Desert, 8, Country, 8, Province, 9, River, 9, Country, 9, Province, 10, Island, 10, Country, 10, Province, 11, Lake, 11, Country, 11, Province, 12, Mountain, 12, Country, 12, Province, 13, River, 13, Country, 13, Province, 14, Sea, 14, Country, 14, Province, 15, River, 15, Country, 15, Province, 16, Name, 16, Islands, 16, Area, 16, Height, 16, Type, 16, Longitude, 16, Latitude, 17, Island, 17, Sea, 17, Lake, 17, River, 18, Country, 18, Organization, 18, Type, 19, Name, 19, Area, 19, Depth, 19, Altitude, 19, Type, 19, River, 19, Longitude, 19, Latitude, 20, Country, 20, Name, 20, Percentage, 21, City, 21, Province, 21, Country, 21, River, 21, Lake, 21, Sea, 22, City, 22, Province, 22, Country, 22, Island, 23, Sea1, 23, Sea2, 24, Name, 24, Mountains, 24, Height, 24, Type, 24, Longitude, 24, Latitude, 25, Mountain, 25, Island, 26, Abbreviation, 26, Name, 26, City, 26, Country, 26, Province, 26, Established, 27, Country, 27, Independence, 27, Dependent, 27, Government, 28, Country, 28, Population_Growth, 28, Infant_Mortality, 29, Name, 29, Country, 29, Population, 29, Area, 29, Capital, 29, CapProv, 30, Country, 30, Name, 30, Percentage, 31, Name, 31, River, 31, Lake, 31, Sea, 31, Length, 31, SourceLongitude, 31, SourceLatitude, 31, Mountains, 31, SourceAltitude, 31, EstuaryLongitude, 31, EstuaryLatitude, 32, Name, 32, Depth, 33, Country, 33, Target
Question: In which city is the lake located at coordinates longitude -85.35 and latitude 11.6?
SQL: SELECT T2.City FROM lake AS T1 INNER JOIN located AS T2 ON T1.Name = T2.Lake INNER JOIN province AS T3 ON T3.Name = T2.Province INNER JOIN city AS T4 ON T4.Province = T3.Name WHERE T1.Longitude = -85.35 AND T1.Latitude = 11.6
Database: mondial_geo
Tables: borders, city, continent, country, desert, economy, encompasses, ethnicGroup, geo_desert, geo_estuary, geo_island, geo_lake, geo_mountain, geo_river, geo_sea, geo_source, island, islandIn, isMember, lake, language, located, locatedOn, mergesWith, mountain, mountainOnIsland, organization, politics, population, province, religion, river, sea, target
Columns (table index, column name): -1, *, 0, Country1, 0, Country2, 0, Length, 1, Name, 1, Country, 1, Province, 1, Population, 1, Longitude, 1, Latitude, 2, Name, 2, Area, 3, Name, 3, Code, 3, Capital, 3, Province, 3, Area, 3, Population, 4, Name, 4, Area, 4, Longitude, 4, Latitude, 5, Country, 5, GDP, 5, Agriculture, 5, Service, 5, Industry, 5, Inflation, 6, Country, 6, Continent, 6, Percentage, 7, Country, 7, Name, 7, Percentage, 8, Desert, 8, Country, 8, Province, 9, River, 9, Country, 9, Province, 10, Island, 10, Country, 10, Province, 11, Lake, 11, Country, 11, Province, 12, Mountain, 12, Country, 12, Province, 13, River, 13, Country, 13, Province, 14, Sea, 14, Country, 14, Province, 15, River, 15, Country, 15, Province, 16, Name, 16, Islands, 16, Area, 16, Height, 16, Type, 16, Longitude, 16, Latitude, 17, Island, 17, Sea, 17, Lake, 17, River, 18, Country, 18, Organization, 18, Type, 19, Name, 19, Area, 19, Depth, 19, Altitude, 19, Type, 19, River, 19, Longitude, 19, Latitude, 20, Country, 20, Name, 20, Percentage, 21, City, 21, Province, 21, Country, 21, River, 21, Lake, 21, Sea, 22, City, 22, Province, 22, Country, 22, Island, 23, Sea1, 23, Sea2, 24, Name, 24, Mountains, 24, Height, 24, Type, 24, Longitude, 24, Latitude, 25, Mountain, 25, Island, 26, Abbreviation, 26, Name, 26, City, 26, Country, 26, Province, 26, Established, 27, Country, 27, Independence, 27, Dependent, 27, Government, 28, Country, 28, Population_Growth, 28, Infant_Mortality, 29, Name, 29, Country, 29, Population, 29, Area, 29, Capital, 29, CapProv, 30, Country, 30, Name, 30, Percentage, 31, Name, 31, River, 31, Lake, 31, Sea, 31, Length, 31, SourceLongitude, 31, SourceLatitude, 31, Mountains, 31, SourceAltitude, 31, EstuaryLongitude, 31, EstuaryLatitude, 32, Name, 32, Depth, 33, Country, 33, Target
Question: On which continent is the country with the most erosion of real income?
Evidence: highest inflation rate results in the most erosion of real income
SQL: SELECT T1.Name FROM continent AS T1 INNER JOIN encompasses AS T2 ON T1.Name = T2.Continent INNER JOIN country AS T3 ON T3.Code = T2.Country INNER JOIN economy AS T4 ON T4.Country = T3.Code ORDER BY T4.Inflation DESC LIMIT 1
Database: mondial_geo
Tables: borders, city, continent, country, desert, economy, encompasses, ethnicGroup, geo_desert, geo_estuary, geo_island, geo_lake, geo_mountain, geo_river, geo_sea, geo_source, island, islandIn, isMember, lake, language, located, locatedOn, mergesWith, mountain, mountainOnIsland, organization, politics, population, province, religion, river, sea, target
Columns (table index, column name): -1, *, 0, Country1, 0, Country2, 0, Length, 1, Name, 1, Country, 1, Province, 1, Population, 1, Longitude, 1, Latitude, 2, Name, 2, Area, 3, Name, 3, Code, 3, Capital, 3, Province, 3, Area, 3, Population, 4, Name, 4, Area, 4, Longitude, 4, Latitude, 5, Country, 5, GDP, 5, Agriculture, 5, Service, 5, Industry, 5, Inflation, 6, Country, 6, Continent, 6, Percentage, 7, Country, 7, Name, 7, Percentage, 8, Desert, 8, Country, 8, Province, 9, River, 9, Country, 9, Province, 10, Island, 10, Country, 10, Province, 11, Lake, 11, Country, 11, Province, 12, Mountain, 12, Country, 12, Province, 13, River, 13, Country, 13, Province, 14, Sea, 14, Country, 14, Province, 15, River, 15, Country, 15, Province, 16, Name, 16, Islands, 16, Area, 16, Height, 16, Type, 16, Longitude, 16, Latitude, 17, Island, 17, Sea, 17, Lake, 17, River, 18, Country, 18, Organization, 18, Type, 19, Name, 19, Area, 19, Depth, 19, Altitude, 19, Type, 19, River, 19, Longitude, 19, Latitude, 20, Country, 20, Name, 20, Percentage, 21, City, 21, Province, 21, Country, 21, River, 21, Lake, 21, Sea, 22, City, 22, Province, 22, Country, 22, Island, 23, Sea1, 23, Sea2, 24, Name, 24, Mountains, 24, Height, 24, Type, 24, Longitude, 24, Latitude, 25, Mountain, 25, Island, 26, Abbreviation, 26, Name, 26, City, 26, Country, 26, Province, 26, Established, 27, Country, 27, Independence, 27, Dependent, 27, Government, 28, Country, 28, Population_Growth, 28, Infant_Mortality, 29, Name, 29, Country, 29, Population, 29, Area, 29, Capital, 29, CapProv, 30, Country, 30, Name, 30, Percentage, 31, Name, 31, River, 31, Lake, 31, Sea, 31, Length, 31, SourceLongitude, 31, SourceLatitude, 31, Mountains, 31, SourceAltitude, 31, EstuaryLongitude, 31, EstuaryLatitude, 32, Name, 32, Depth, 33, Country, 33, Target
Question: Which two Asian countries share a border that is 1,782 kilometers long?
SQL: SELECT T4.Country1, T4.Country2 FROM continent AS T1 INNER JOIN encompasses AS T2 ON T1.Name = T2.Continent INNER JOIN country AS T3 ON T3.Code = T2.Country INNER JOIN borders AS T4 ON T4.Country1 = T3.Code WHERE T1.Name = 'Asia' AND T4.Length = 1782
Database: mondial_geo
Tables: borders, city, continent, country, desert, economy, encompasses, ethnicGroup, geo_desert, geo_estuary, geo_island, geo_lake, geo_mountain, geo_river, geo_sea, geo_source, island, islandIn, isMember, lake, language, located, locatedOn, mergesWith, mountain, mountainOnIsland, organization, politics, population, province, religion, river, sea, target
Columns (table index, column name): -1, *, 0, Country1, 0, Country2, 0, Length, 1, Name, 1, Country, 1, Province, 1, Population, 1, Longitude, 1, Latitude, 2, Name, 2, Area, 3, Name, 3, Code, 3, Capital, 3, Province, 3, Area, 3, Population, 4, Name, 4, Area, 4, Longitude, 4, Latitude, 5, Country, 5, GDP, 5, Agriculture, 5, Service, 5, Industry, 5, Inflation, 6, Country, 6, Continent, 6, Percentage, 7, Country, 7, Name, 7, Percentage, 8, Desert, 8, Country, 8, Province, 9, River, 9, Country, 9, Province, 10, Island, 10, Country, 10, Province, 11, Lake, 11, Country, 11, Province, 12, Mountain, 12, Country, 12, Province, 13, River, 13, Country, 13, Province, 14, Sea, 14, Country, 14, Province, 15, River, 15, Country, 15, Province, 16, Name, 16, Islands, 16, Area, 16, Height, 16, Type, 16, Longitude, 16, Latitude, 17, Island, 17, Sea, 17, Lake, 17, River, 18, Country, 18, Organization, 18, Type, 19, Name, 19, Area, 19, Depth, 19, Altitude, 19, Type, 19, River, 19, Longitude, 19, Latitude, 20, Country, 20, Name, 20, Percentage, 21, City, 21, Province, 21, Country, 21, River, 21, Lake, 21, Sea, 22, City, 22, Province, 22, Country, 22, Island, 23, Sea1, 23, Sea2, 24, Name, 24, Mountains, 24, Height, 24, Type, 24, Longitude, 24, Latitude, 25, Mountain, 25, Island, 26, Abbreviation, 26, Name, 26, City, 26, Country, 26, Province, 26, Established, 27, Country, 27, Independence, 27, Dependent, 27, Government, 28, Country, 28, Population_Growth, 28, Infant_Mortality, 29, Name, 29, Country, 29, Population, 29, Area, 29, Capital, 29, CapProv, 30, Country, 30, Name, 30, Percentage, 31, Name, 31, River, 31, Lake, 31, Sea, 31, Length, 31, SourceLongitude, 31, SourceLatitude, 31, Mountains, 31, SourceAltitude, 31, EstuaryLongitude, 31, EstuaryLatitude, 32, Name, 32, Depth, 33, Country, 33, Target
Question: Of all the lakes in Bolivia, which is the deepest?
Evidence: Bolivia is the country
SQL: SELECT T1.Name FROM lake AS T1 INNER JOIN geo_lake AS T2 ON T1.Name = T2.Lake INNER JOIN province AS T3 ON T3.Name = T2.Province INNER JOIN country AS T4 ON T4.Code = T3.Country WHERE T4.Name = 'Bolivia' ORDER BY T1.Depth DESC LIMIT 1
Database: mondial_geo
Tables: borders, city, continent, country, desert, economy, encompasses, ethnicGroup, geo_desert, geo_estuary, geo_island, geo_lake, geo_mountain, geo_river, geo_sea, geo_source, island, islandIn, isMember, lake, language, located, locatedOn, mergesWith, mountain, mountainOnIsland, organization, politics, population, province, religion, river, sea, target
Columns (table index, column name): -1, *, 0, Country1, 0, Country2, 0, Length, 1, Name, 1, Country, 1, Province, 1, Population, 1, Longitude, 1, Latitude, 2, Name, 2, Area, 3, Name, 3, Code, 3, Capital, 3, Province, 3, Area, 3, Population, 4, Name, 4, Area, 4, Longitude, 4, Latitude, 5, Country, 5, GDP, 5, Agriculture, 5, Service, 5, Industry, 5, Inflation, 6, Country, 6, Continent, 6, Percentage, 7, Country, 7, Name, 7, Percentage, 8, Desert, 8, Country, 8, Province, 9, River, 9, Country, 9, Province, 10, Island, 10, Country, 10, Province, 11, Lake, 11, Country, 11, Province, 12, Mountain, 12, Country, 12, Province, 13, River, 13, Country, 13, Province, 14, Sea, 14, Country, 14, Province, 15, River, 15, Country, 15, Province, 16, Name, 16, Islands, 16, Area, 16, Height, 16, Type, 16, Longitude, 16, Latitude, 17, Island, 17, Sea, 17, Lake, 17, River, 18, Country, 18, Organization, 18, Type, 19, Name, 19, Area, 19, Depth, 19, Altitude, 19, Type, 19, River, 19, Longitude, 19, Latitude, 20, Country, 20, Name, 20, Percentage, 21, City, 21, Province, 21, Country, 21, River, 21, Lake, 21, Sea, 22, City, 22, Province, 22, Country, 22, Island, 23, Sea1, 23, Sea2, 24, Name, 24, Mountains, 24, Height, 24, Type, 24, Longitude, 24, Latitude, 25, Mountain, 25, Island, 26, Abbreviation, 26, Name, 26, City, 26, Country, 26, Province, 26, Established, 27, Country, 27, Independence, 27, Dependent, 27, Government, 28, Country, 28, Population_Growth, 28, Infant_Mortality, 29, Name, 29, Country, 29, Population, 29, Area, 29, Capital, 29, CapProv, 30, Country, 30, Name, 30, Percentage, 31, Name, 31, River, 31, Lake, 31, Sea, 31, Length, 31, SourceLongitude, 31, SourceLatitude, 31, Mountains, 31, SourceAltitude, 31, EstuaryLongitude, 31, EstuaryLatitude, 32, Name, 32, Depth, 33, Country, 33, Target
Question: In which lake flows the river that is, in turn, the mouth of the Manicouagan River?
SQL: SELECT NAME FROM lake WHERE river = ( SELECT river FROM river WHERE NAME = 'Manicouagan' )