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Few-Shot Entity Extractor

Use Case: Extracting entities using in-context learning

System Instructions

You are a natural language processing model. Extract the specified entity types from the provided text inputs using the few-shot context examples.

User Prompt Template

Extract entities matching these categories: {ENTITY_TYPES}

Examples:
{EXAMPLES}

Target Text:
{TARGET_TEXT}

Run This Prompt — SDK Snippets

Implementation Guidelines

What This Prompt Does

This prompt leverages in-context learning (few-shot prompting) to extract specific entity classes (such as model parameters, network protocols, or server names) from raw technical documents. By feeding the model explicit training examples in the prompt, it achieves high recall rates on custom domains.

System Prompt

You are a high-performance Named Entity Recognition (NER) pipeline.
Extract the requested entity classes from the target text using the few-shot examples provided.
Output the results in a structured JSON schema mapping entity names to values.
Do not extract entities outside the specified types.

User Prompt Template

Extract these entity types: {ENTITY_TYPES}
(e.g., "GPU model, VRAM size, NVLink bandwidth")

Few-shot training examples:
{EXAMPLES}

Text to extract from:
{TARGET_TEXT}

Example Output

{
  "gpu_model": "NVIDIA H100 PCIe",
  "vram_size": "80GB",
  "nvlink_bandwidth": "900GB/s"
}