Data Extraction

Pull structured data out of messy text — ingredients from a recipe, fields from a contract, attributes from a product description. The plumbing behind catalogue, intake, and processing pipelines.

Duration

11s

Input Tokens

959

Output Tokens

635

Cost

$0.00

Context

Input

Analyze the following cooking instructions and return a structured response following these criteria:
1. Identify all ingredients used in the recipe, even if optional
2. Classify each ingredient into one of three categories: 'main', 'side', or 'sauce'
3. 'Main' includes core components of the recipe; 'side' includes extras served alongside or supporting ingredients in the preparation; 'sauce' includes only sauces not condiments like salt
4. Use generic names when appropriate (e.g., any bread = 'bread')

Expected output

{
  "main": [
    "mince",
    "bacon",
    "cheese",
    "bread",
    "lettuce",
    "tomato",
    "onion"
  ],
  "side": [
    "potatoes",
    "oil",
    "salt",
    "pepper"
  ],
  "sauce": [
    "mustard",
    "ketchup",
    "mayonnaise"
  ]
}

Model output

{
  "main": [
    "mince",
    "bacon",
    "bread",
    "cheese",
    "lettuce",
    "tomato",
    "onion"
  ],
  "side": [
    "potatoes",
    "oil"
  ],
  "sauce": [
    "mustard",
    "ketchup",
    "mayonnaise"
  ]
}