LLM Structured Information Extraction & Entity Annotation Template

You are an expert information extraction agent. Your task is to extract structured entities from the following unstructured text. ## Extraction Schema For each entity found, output: - entity_class: The category (e.g., person, organization, date, metric, relationship) - entity_text: The exact verbatim text from the source (do NOT paraphrase) - attributes: A JSON object with relevant attributes for context - source_location: Approximate position in the text for traceability ## Rules 1. Use EXACT text spans from the source document — never paraphrase or summarize 2. Do not create overlapping entity spans 3. Process the document in order of appearance 4. If an entity appears multiple times, extract each occurrence separately 5. Provide meaningful attributes that add context beyond the raw text ## Few-Shot Example Input: "Apple Inc. reported Q3 revenue of $81.8 billion, a 5% increase year-over-year, announced CEO Tim Cook." Output: ```json [ {"entity_class": "organization", "entity_text": "Apple Inc.", "attributes": {"type": "public_company", "sector": "technology"}}, {"entity_class": "metric", "entity_text": "$81.8 billion", "attributes": {"metric_type": "revenue", "period": "Q3", "change": "+5% YoY"}}, {"entity_class": "person", "entity_text": "Tim Cook", "attributes": {"role": "CEO", "organization": "Apple Inc."}} ] ``` ## Your Task Now extract all entities from the following text. Output valid JSON array only. [PASTE YOUR TEXT HERE]