QIAN-ME/bird_schema_data huggingface dataset:
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 craftbeer
【Schema】
# Table: beers
[
(id:INTEGER, Primary Key, Examples: [2687, 2688, 2689]),
(brewery_id:INTEGER, Examples: [0, 1, 2]),
(abv:REAL, Examples: [0.065, 0.087, 0.08]),
(ibu:REAL, Examples: [65.0, 85.0, 35.0]),
(name:TEXT, Examples: [Dale's Pale Ale, Gordon Ale (2009), Old Chub]),
(style:TEXT, Examples: [American Pale Ale (APA)]),
(ounces:REAL, Examples: [12.0, 16.0, 24.0])
]
# Table: breweries
[
(id:INTEGER, Primary Key, Examples: [0, 1, 2]),
(name:TEXT, Examples: [NorthGate Brewing ]),
(city:TEXT, Examples: [Minneapolis, Louisville, Framingham]),
(state:TEXT, Examples: [MN, KY, MA])
]
【Foreign keys】
beers.brewery_id=breweries.id

    【Question】
    Of all the beer styles produced by Stevens Point Brewery, how many percent do they allot for American Adjunct Lager? Percent allotted = count(American Adjunct Lager beer styles) / count(styles) * 100%

    【Evidence】
    Percent allotted = count(American Adjunct Lager beer styles) / count(styles) * 100%

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CAST(SUM(IIF(T1.style = 'American Adjunct Lager', 1, 0)) AS REAL) * 100 / COUNT(T1.brewery_id) FROM beers AS T1 INNER JOIN breweries AS T2 ON T1.brewery_id = T2.id WHERE T2.name = 'Stevens Point Brewery'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 craftbeer
【Schema】
# Table: beers
[
(id:INTEGER, Primary Key, Examples: [2687, 2688, 2689]),
(brewery_id:INTEGER, Examples: [0, 1, 2]),
(abv:REAL, Examples: [0.065, 0.087, 0.08]),
(ibu:REAL, Examples: [65.0, 85.0, 35.0]),
(name:TEXT, Examples: [Dale's Pale Ale, Gordon Ale (2009), Old Chub]),
(style:TEXT, Examples: [American Pale Ale (APA)]),
(ounces:REAL, Examples: [12.0, 16.0, 24.0])
]
# Table: breweries
[
(id:INTEGER, Primary Key, Examples: [0, 1, 2]),
(name:TEXT, Examples: [NorthGate Brewing ]),
(city:TEXT, Examples: [Minneapolis, Louisville, Framingham]),
(state:TEXT, Examples: [MN, KY, MA])
]
【Foreign keys】
beers.brewery_id=breweries.id

    【Question】
    Which city and state produces the most and least bitter beer, and what is the difference in bitterness between the two? List also the names of the beer. The more IBU, the more bitter the beer is, most bitter means highest IBU; The less IBU, the less bitter the beer is, least bitter means lowest IBU

    【Evidence】
    The more IBU, the more bitter the beer is, most bitter means highest IBU; The less IBU, the less bitter the beer is, least bitter means lowest IBU

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T1.state, T1.city, T2.name, T2.ibu FROM breweries AS T1 INNER JOIN beers AS T2 ON T1.id = T2.brewery_id GROUP BY T1.state, T1.city, T2.name, T2.ibu HAVING MAX(ibu) AND MIN(ibu) LIMIT 2
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 craftbeer
【Schema】
# Table: beers
[
(id:INTEGER, Primary Key, Examples: [2687, 2688, 2689]),
(brewery_id:INTEGER, Examples: [0, 1, 2]),
(abv:REAL, Examples: [0.065, 0.087, 0.08]),
(ibu:REAL, Examples: [65.0, 85.0, 35.0]),
(name:TEXT, Examples: [Dale's Pale Ale, Gordon Ale (2009), Old Chub]),
(style:TEXT, Examples: [American Pale Ale (APA)]),
(ounces:REAL, Examples: [12.0, 16.0, 24.0])
]
# Table: breweries
[
(id:INTEGER, Primary Key, Examples: [0, 1, 2]),
(name:TEXT, Examples: [NorthGate Brewing ]),
(city:TEXT, Examples: [Minneapolis, Louisville, Framingham]),
(state:TEXT, Examples: [MN, KY, MA])
]
【Foreign keys】
beers.brewery_id=breweries.id

    【Question】
    When compared to the total number of breweries in the US producing American Blonde Ale, how many in the state of Wisconsin produces American Blonde Ale? Indicate your answer in percentage (%). Percentage of the state of Wisconsin produces American Blonde Ale could be computed by count(breweries in Wisconsin producing American Blonde Ale) / count(all breweries)

    【Evidence】
    Percentage of the state of Wisconsin produces American Blonde Ale could be computed by count(breweries in Wisconsin producing American Blonde Ale) / count(all breweries)

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CAST(SUM(IIF(T2.state = 'WI', 1, 0)) AS REAL) * 100 / COUNT(T1.id) FROM beers AS T1 INNER JOIN breweries AS T2 ON T1.brewery_id = T2.id WHERE T1.style = 'American Blonde Ale'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 cookbook
【Schema】
# Table: Quantity
[
(quantity_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(recipe_id:INTEGER, Examples: [214, 215, 216]),
(ingredient_id:INTEGER, Examples: [1613, 3334, 2222]),
(max_qty:REAL, Examples: [2.0, 0.25, 0.5]),
(min_qty:REAL, Examples: [2.0, 0.25, 0.5]),
(unit:TEXT, Examples: [cup(s), teaspoon(s), 14 oz. can(s)]),
(preparation:TEXT, Examples: [melted, or water, divided]),
(optional:TEXT, Examples: [FALSE, TRUE])
]
# Table: Nutrition
[
(recipe_id:INTEGER, Primary Key, Examples: [214, 215, 216]),
(protein:REAL, Examples: [5.47, 5.7, 4.9]),
(carbo:REAL, Examples: [41.29, 23.75, 26.88]),
(alcohol:REAL, Examples: [0.0, 1.93, 2.7]),
(total_fat:REAL, Examples: [11.53, 1.08, 1.1]),
(sat_fat:REAL, Examples: [2.21, 0.58, 0.06]),
(cholestrl:REAL, Examples: [1.39, 3.48, 3.46]),
(sodium:REAL, Examples: [260.78, 46.17, 41.79]),
(iron:REAL, Examples: [0.81, 0.57, 0.37]),
(vitamin_c:REAL, Examples: [8.89, 13.02, 6.13]),
(vitamin_a:REAL, Examples: [586.2, 2738.24, 1521.1]),
(fiber:REAL, Examples: [0.87, 0.62, 0.34]),
(pcnt_cal_carb:REAL, Examples: [56.8, 67.38, 78.45]),
(pcnt_cal_fat:REAL, Examples: [35.68, 6.89, 7.24]),
(pcnt_cal_prot:REAL, Examples: [7.53, 16.17, 14.3]),
(calories:REAL, Examples: [290.79, 141.01, 137.06])
]
# Table: Recipe
[
(recipe_id:INTEGER, Primary Key, Examples: [214, 215, 216]),
(title:TEXT, Examples: [Raspberry Chiffon Pie]),
(subtitle:TEXT, Examples: [with Banana Cream Whip]),
(servings:INTEGER, Examples: [10, 4, 8]),
(yield_unit:TEXT, Examples: [1 pie, 8 tarts, 2 quarts]),
(prep_min:INTEGER, Examples: [20, 5, 10]),
(cook_min:INTEGER, Examples: [8, 2, 13]),
(stnd_min:INTEGER, Examples: [305, 65, 0]),
(source:TEXT, Examples: [The California Tree Fruit Agreement]),
(intro:TEXT),
(directions:TEXT)
]
# Table: Ingredient
[
(ingredient_id:INTEGER, Primary Key, Examples: [1, 6, 10]),
(category:TEXT, Examples: [dairy, Mexican products, cereals]),
(name:TEXT, Examples: [1% lowfat cottage cheese]),
(plural:TEXT, Examples: [s, #NAME?, es])
]
【Foreign keys】
Quantity.recipe_id=Recipe.recipe_id
Quantity.ingredient_id=Ingredient.ingredient_id
Quantity.recipe_id=Nutrition.recipe_id
Nutrition.recipe_id=Recipe.recipe_id

    【Question】
    What is the title of the recipe that is most likely to gain weight? most likely to gain weight refers to MAX(total_fat)

    【Evidence】
    most likely to gain weight refers to MAX(total_fat)

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T1.title FROM Recipe AS T1 INNER JOIN Nutrition AS T2 ON T1.recipe_id = T2.recipe_id ORDER BY T2.total_fat DESC LIMIT 1
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 cookbook
【Schema】
# Table: Quantity
[
(quantity_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(recipe_id:INTEGER, Examples: [214, 215, 216]),
(ingredient_id:INTEGER, Examples: [1613, 3334, 2222]),
(max_qty:REAL, Examples: [2.0, 0.25, 0.5]),
(min_qty:REAL, Examples: [2.0, 0.25, 0.5]),
(unit:TEXT, Examples: [cup(s), teaspoon(s), 14 oz. can(s)]),
(preparation:TEXT, Examples: [melted, or water, divided]),
(optional:TEXT, Examples: [FALSE, TRUE])
]
# Table: Nutrition
[
(recipe_id:INTEGER, Primary Key, Examples: [214, 215, 216]),
(protein:REAL, Examples: [5.47, 5.7, 4.9]),
(carbo:REAL, Examples: [41.29, 23.75, 26.88]),
(alcohol:REAL, Examples: [0.0, 1.93, 2.7]),
(total_fat:REAL, Examples: [11.53, 1.08, 1.1]),
(sat_fat:REAL, Examples: [2.21, 0.58, 0.06]),
(cholestrl:REAL, Examples: [1.39, 3.48, 3.46]),
(sodium:REAL, Examples: [260.78, 46.17, 41.79]),
(iron:REAL, Examples: [0.81, 0.57, 0.37]),
(vitamin_c:REAL, Examples: [8.89, 13.02, 6.13]),
(vitamin_a:REAL, Examples: [586.2, 2738.24, 1521.1]),
(fiber:REAL, Examples: [0.87, 0.62, 0.34]),
(pcnt_cal_carb:REAL, Examples: [56.8, 67.38, 78.45]),
(pcnt_cal_fat:REAL, Examples: [35.68, 6.89, 7.24]),
(pcnt_cal_prot:REAL, Examples: [7.53, 16.17, 14.3]),
(calories:REAL, Examples: [290.79, 141.01, 137.06])
]
# Table: Recipe
[
(recipe_id:INTEGER, Primary Key, Examples: [214, 215, 216]),
(title:TEXT, Examples: [Raspberry Chiffon Pie]),
(subtitle:TEXT, Examples: [with Banana Cream Whip]),
(servings:INTEGER, Examples: [10, 4, 8]),
(yield_unit:TEXT, Examples: [1 pie, 8 tarts, 2 quarts]),
(prep_min:INTEGER, Examples: [20, 5, 10]),
(cook_min:INTEGER, Examples: [8, 2, 13]),
(stnd_min:INTEGER, Examples: [305, 65, 0]),
(source:TEXT, Examples: [The California Tree Fruit Agreement]),
(intro:TEXT),
(directions:TEXT)
]
# Table: Ingredient
[
(ingredient_id:INTEGER, Primary Key, Examples: [1, 6, 10]),
(category:TEXT, Examples: [dairy, Mexican products, cereals]),
(name:TEXT, Examples: [1% lowfat cottage cheese]),
(plural:TEXT, Examples: [s, #NAME?, es])
]
【Foreign keys】
Quantity.recipe_id=Recipe.recipe_id
Quantity.ingredient_id=Ingredient.ingredient_id
Quantity.recipe_id=Nutrition.recipe_id
Nutrition.recipe_id=Recipe.recipe_id

    【Question】
    What is the unsaturated fat content in the recipe "Raspberry Chiffon Pie"? Raspberry Chiffon Pie refers to title; unsaturated fat refers to SUBTRACT(total_fat, sat_fat)

    【Evidence】
    Raspberry Chiffon Pie refers to title; unsaturated fat refers to SUBTRACT(total_fat, sat_fat)

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T2.total_fat - T2.sat_fat FROM Recipe AS T1 INNER JOIN Nutrition AS T2 ON T1.recipe_id = T2.recipe_id WHERE T1.title = 'Raspberry Chiffon Pie'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 cookbook
【Schema】
# Table: Quantity
[
(quantity_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(recipe_id:INTEGER, Examples: [214, 215, 216]),
(ingredient_id:INTEGER, Examples: [1613, 3334, 2222]),
(max_qty:REAL, Examples: [2.0, 0.25, 0.5]),
(min_qty:REAL, Examples: [2.0, 0.25, 0.5]),
(unit:TEXT, Examples: [cup(s), teaspoon(s), 14 oz. can(s)]),
(preparation:TEXT, Examples: [melted, or water, divided]),
(optional:TEXT, Examples: [FALSE, TRUE])
]
# Table: Nutrition
[
(recipe_id:INTEGER, Primary Key, Examples: [214, 215, 216]),
(protein:REAL, Examples: [5.47, 5.7, 4.9]),
(carbo:REAL, Examples: [41.29, 23.75, 26.88]),
(alcohol:REAL, Examples: [0.0, 1.93, 2.7]),
(total_fat:REAL, Examples: [11.53, 1.08, 1.1]),
(sat_fat:REAL, Examples: [2.21, 0.58, 0.06]),
(cholestrl:REAL, Examples: [1.39, 3.48, 3.46]),
(sodium:REAL, Examples: [260.78, 46.17, 41.79]),
(iron:REAL, Examples: [0.81, 0.57, 0.37]),
(vitamin_c:REAL, Examples: [8.89, 13.02, 6.13]),
(vitamin_a:REAL, Examples: [586.2, 2738.24, 1521.1]),
(fiber:REAL, Examples: [0.87, 0.62, 0.34]),
(pcnt_cal_carb:REAL, Examples: [56.8, 67.38, 78.45]),
(pcnt_cal_fat:REAL, Examples: [35.68, 6.89, 7.24]),
(pcnt_cal_prot:REAL, Examples: [7.53, 16.17, 14.3]),
(calories:REAL, Examples: [290.79, 141.01, 137.06])
]
# Table: Recipe
[
(recipe_id:INTEGER, Primary Key, Examples: [214, 215, 216]),
(title:TEXT, Examples: [Raspberry Chiffon Pie]),
(subtitle:TEXT, Examples: [with Banana Cream Whip]),
(servings:INTEGER, Examples: [10, 4, 8]),
(yield_unit:TEXT, Examples: [1 pie, 8 tarts, 2 quarts]),
(prep_min:INTEGER, Examples: [20, 5, 10]),
(cook_min:INTEGER, Examples: [8, 2, 13]),
(stnd_min:INTEGER, Examples: [305, 65, 0]),
(source:TEXT, Examples: [The California Tree Fruit Agreement]),
(intro:TEXT),
(directions:TEXT)
]
# Table: Ingredient
[
(ingredient_id:INTEGER, Primary Key, Examples: [1, 6, 10]),
(category:TEXT, Examples: [dairy, Mexican products, cereals]),
(name:TEXT, Examples: [1% lowfat cottage cheese]),
(plural:TEXT, Examples: [s, #NAME?, es])
]
【Foreign keys】
Quantity.recipe_id=Recipe.recipe_id
Quantity.ingredient_id=Ingredient.ingredient_id
Quantity.recipe_id=Nutrition.recipe_id
Nutrition.recipe_id=Recipe.recipe_id

    【Question】
    Please list the titles of all the recipes that are salt/sodium-free. salt/sodium-free refers to sodium < 5

    【Evidence】
    salt/sodium-free refers to sodium < 5

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T1.title FROM Recipe AS T1 INNER JOIN Nutrition AS T2 ON T1.recipe_id = T2.recipe_id WHERE T2.sodium < 5
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 cookbook
【Schema】
# Table: Quantity
[
(quantity_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(recipe_id:INTEGER, Examples: [214, 215, 216]),
(ingredient_id:INTEGER, Examples: [1613, 3334, 2222]),
(max_qty:REAL, Examples: [2.0, 0.25, 0.5]),
(min_qty:REAL, Examples: [2.0, 0.25, 0.5]),
(unit:TEXT, Examples: [cup(s), teaspoon(s), 14 oz. can(s)]),
(preparation:TEXT, Examples: [melted, or water, divided]),
(optional:TEXT, Examples: [FALSE, TRUE])
]
# Table: Nutrition
[
(recipe_id:INTEGER, Primary Key, Examples: [214, 215, 216]),
(protein:REAL, Examples: [5.47, 5.7, 4.9]),
(carbo:REAL, Examples: [41.29, 23.75, 26.88]),
(alcohol:REAL, Examples: [0.0, 1.93, 2.7]),
(total_fat:REAL, Examples: [11.53, 1.08, 1.1]),
(sat_fat:REAL, Examples: [2.21, 0.58, 0.06]),
(cholestrl:REAL, Examples: [1.39, 3.48, 3.46]),
(sodium:REAL, Examples: [260.78, 46.17, 41.79]),
(iron:REAL, Examples: [0.81, 0.57, 0.37]),
(vitamin_c:REAL, Examples: [8.89, 13.02, 6.13]),
(vitamin_a:REAL, Examples: [586.2, 2738.24, 1521.1]),
(fiber:REAL, Examples: [0.87, 0.62, 0.34]),
(pcnt_cal_carb:REAL, Examples: [56.8, 67.38, 78.45]),
(pcnt_cal_fat:REAL, Examples: [35.68, 6.89, 7.24]),
(pcnt_cal_prot:REAL, Examples: [7.53, 16.17, 14.3]),
(calories:REAL, Examples: [290.79, 141.01, 137.06])
]
# Table: Recipe
[
(recipe_id:INTEGER, Primary Key, Examples: [214, 215, 216]),
(title:TEXT, Examples: [Raspberry Chiffon Pie]),
(subtitle:TEXT, Examples: [with Banana Cream Whip]),
(servings:INTEGER, Examples: [10, 4, 8]),
(yield_unit:TEXT, Examples: [1 pie, 8 tarts, 2 quarts]),
(prep_min:INTEGER, Examples: [20, 5, 10]),
(cook_min:INTEGER, Examples: [8, 2, 13]),
(stnd_min:INTEGER, Examples: [305, 65, 0]),
(source:TEXT, Examples: [The California Tree Fruit Agreement]),
(intro:TEXT),
(directions:TEXT)
]
# Table: Ingredient
[
(ingredient_id:INTEGER, Primary Key, Examples: [1, 6, 10]),
(category:TEXT, Examples: [dairy, Mexican products, cereals]),
(name:TEXT, Examples: [1% lowfat cottage cheese]),
(plural:TEXT, Examples: [s, #NAME?, es])
]
【Foreign keys】
Quantity.recipe_id=Recipe.recipe_id
Quantity.ingredient_id=Ingredient.ingredient_id
Quantity.recipe_id=Nutrition.recipe_id
Nutrition.recipe_id=Recipe.recipe_id

    【Question】
    Please list the titles of all the recipes that may lead to constipation, feeling sick or stomach pain. may lead to constipation, feeling sick or stomach pain refers to iron > 20

    【Evidence】
    may lead to constipation, feeling sick or stomach pain refers to iron > 20

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T1.title FROM Recipe AS T1 INNER JOIN Nutrition AS T2 ON T1.recipe_id = T2.recipe_id WHERE T2.iron > 20
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 cookbook
【Schema】
# Table: Quantity
[
(quantity_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(recipe_id:INTEGER, Examples: [214, 215, 216]),
(ingredient_id:INTEGER, Examples: [1613, 3334, 2222]),
(max_qty:REAL, Examples: [2.0, 0.25, 0.5]),
(min_qty:REAL, Examples: [2.0, 0.25, 0.5]),
(unit:TEXT, Examples: [cup(s), teaspoon(s), 14 oz. can(s)]),
(preparation:TEXT, Examples: [melted, or water, divided]),
(optional:TEXT, Examples: [FALSE, TRUE])
]
# Table: Nutrition
[
(recipe_id:INTEGER, Primary Key, Examples: [214, 215, 216]),
(protein:REAL, Examples: [5.47, 5.7, 4.9]),
(carbo:REAL, Examples: [41.29, 23.75, 26.88]),
(alcohol:REAL, Examples: [0.0, 1.93, 2.7]),
(total_fat:REAL, Examples: [11.53, 1.08, 1.1]),
(sat_fat:REAL, Examples: [2.21, 0.58, 0.06]),
(cholestrl:REAL, Examples: [1.39, 3.48, 3.46]),
(sodium:REAL, Examples: [260.78, 46.17, 41.79]),
(iron:REAL, Examples: [0.81, 0.57, 0.37]),
(vitamin_c:REAL, Examples: [8.89, 13.02, 6.13]),
(vitamin_a:REAL, Examples: [586.2, 2738.24, 1521.1]),
(fiber:REAL, Examples: [0.87, 0.62, 0.34]),
(pcnt_cal_carb:REAL, Examples: [56.8, 67.38, 78.45]),
(pcnt_cal_fat:REAL, Examples: [35.68, 6.89, 7.24]),
(pcnt_cal_prot:REAL, Examples: [7.53, 16.17, 14.3]),
(calories:REAL, Examples: [290.79, 141.01, 137.06])
]
# Table: Recipe
[
(recipe_id:INTEGER, Primary Key, Examples: [214, 215, 216]),
(title:TEXT, Examples: [Raspberry Chiffon Pie]),
(subtitle:TEXT, Examples: [with Banana Cream Whip]),
(servings:INTEGER, Examples: [10, 4, 8]),
(yield_unit:TEXT, Examples: [1 pie, 8 tarts, 2 quarts]),
(prep_min:INTEGER, Examples: [20, 5, 10]),
(cook_min:INTEGER, Examples: [8, 2, 13]),
(stnd_min:INTEGER, Examples: [305, 65, 0]),
(source:TEXT, Examples: [The California Tree Fruit Agreement]),
(intro:TEXT),
(directions:TEXT)
]
# Table: Ingredient
[
(ingredient_id:INTEGER, Primary Key, Examples: [1, 6, 10]),
(category:TEXT, Examples: [dairy, Mexican products, cereals]),
(name:TEXT, Examples: [1% lowfat cottage cheese]),
(plural:TEXT, Examples: [s, #NAME?, es])
]
【Foreign keys】
Quantity.recipe_id=Recipe.recipe_id
Quantity.ingredient_id=Ingredient.ingredient_id
Quantity.recipe_id=Nutrition.recipe_id
Nutrition.recipe_id=Recipe.recipe_id

    【Question】
    Which recipe is more beneficial in wound healing, "Raspberry Chiffon Pie" or "Fresh Apricot Bavarian"? Raspberry Chiffon Pie and Fresh Apricot Bavarian are title; vitamin_c is higher refers to MAX(vitamin_c)

    【Evidence】
    Raspberry Chiffon Pie and Fresh Apricot Bavarian are title; vitamin_c is higher refers to MAX(vitamin_c)

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT DISTINCT CASE WHEN CASE WHEN T2.title = 'Raspberry Chiffon Pie' THEN T1.vitamin_c END > CASE WHEN T2.title = 'Fresh Apricot Bavarian' THEN T1.vitamin_c END THEN 'Raspberry Chiffon Pie' ELSE 'Fresh Apricot Bavarian' END AS "vitamin_c is higher" FROM Nutrition T1 INNER JOIN Recipe T2 ON T2.recipe_id = T1.recipe_id
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 cookbook
【Schema】
# Table: Quantity
[
(quantity_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(recipe_id:INTEGER, Examples: [214, 215, 216]),
(ingredient_id:INTEGER, Examples: [1613, 3334, 2222]),
(max_qty:REAL, Examples: [2.0, 0.25, 0.5]),
(min_qty:REAL, Examples: [2.0, 0.25, 0.5]),
(unit:TEXT, Examples: [cup(s), teaspoon(s), 14 oz. can(s)]),
(preparation:TEXT, Examples: [melted, or water, divided]),
(optional:TEXT, Examples: [FALSE, TRUE])
]
# Table: Nutrition
[
(recipe_id:INTEGER, Primary Key, Examples: [214, 215, 216]),
(protein:REAL, Examples: [5.47, 5.7, 4.9]),
(carbo:REAL, Examples: [41.29, 23.75, 26.88]),
(alcohol:REAL, Examples: [0.0, 1.93, 2.7]),
(total_fat:REAL, Examples: [11.53, 1.08, 1.1]),
(sat_fat:REAL, Examples: [2.21, 0.58, 0.06]),
(cholestrl:REAL, Examples: [1.39, 3.48, 3.46]),
(sodium:REAL, Examples: [260.78, 46.17, 41.79]),
(iron:REAL, Examples: [0.81, 0.57, 0.37]),
(vitamin_c:REAL, Examples: [8.89, 13.02, 6.13]),
(vitamin_a:REAL, Examples: [586.2, 2738.24, 1521.1]),
(fiber:REAL, Examples: [0.87, 0.62, 0.34]),
(pcnt_cal_carb:REAL, Examples: [56.8, 67.38, 78.45]),
(pcnt_cal_fat:REAL, Examples: [35.68, 6.89, 7.24]),
(pcnt_cal_prot:REAL, Examples: [7.53, 16.17, 14.3]),
(calories:REAL, Examples: [290.79, 141.01, 137.06])
]
# Table: Recipe
[
(recipe_id:INTEGER, Primary Key, Examples: [214, 215, 216]),
(title:TEXT, Examples: [Raspberry Chiffon Pie]),
(subtitle:TEXT, Examples: [with Banana Cream Whip]),
(servings:INTEGER, Examples: [10, 4, 8]),
(yield_unit:TEXT, Examples: [1 pie, 8 tarts, 2 quarts]),
(prep_min:INTEGER, Examples: [20, 5, 10]),
(cook_min:INTEGER, Examples: [8, 2, 13]),
(stnd_min:INTEGER, Examples: [305, 65, 0]),
(source:TEXT, Examples: [The California Tree Fruit Agreement]),
(intro:TEXT),
(directions:TEXT)
]
# Table: Ingredient
[
(ingredient_id:INTEGER, Primary Key, Examples: [1, 6, 10]),
(category:TEXT, Examples: [dairy, Mexican products, cereals]),
(name:TEXT, Examples: [1% lowfat cottage cheese]),
(plural:TEXT, Examples: [s, #NAME?, es])
]
【Foreign keys】
Quantity.recipe_id=Recipe.recipe_id
Quantity.ingredient_id=Ingredient.ingredient_id
Quantity.recipe_id=Nutrition.recipe_id
Nutrition.recipe_id=Recipe.recipe_id

    【Question】
    Among the recipes that take more than 10 minutes to prepare, what is the title of the one with the most calories? more than 10 minutes to prepare refers to prep_min > 10; the most calories refers to MAX(calories)

    【Evidence】
    more than 10 minutes to prepare refers to prep_min > 10; the most calories refers to MAX(calories)

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T1.title FROM Recipe AS T1 INNER JOIN Nutrition AS T2 ON T1.recipe_id = T2.recipe_id WHERE T1.prep_min > 10 ORDER BY T2.calories DESC LIMIT 1