Entity Extraction

Entity Extraction is the broader discipline of pulling structured information out of unstructured text — encompassing NER, attribute extraction, key-value extraction from forms, table extraction from documents, and increasingly LLM-driven structured-data extraction. Where NER focuses on identifying mentions of entity types, entity extraction often extends to extracting full records: pulling a complete contact (name, email, phone, company, title) from a signature block, extracting line items from an invoice, or identifying patient demographics, diagnoses, medications, and dosages from a clinical note. Modern approaches use LLMs with carefully crafted prompts and JSON schemas to extract entire structured records in one pass. Tools include Azure Form Recognizer, AWS Textract, Google Document AI, and the structured-output features in OpenAI, Anthropic, and Gemini APIs. AI governance, AI compliance, and AI risk management programs use entity extraction to automate document processing, regulatory reporting, and operational workflows — supporting responsible AI in high-volume content pipelines across enterprise AI.

Centralpoint Extracts Structured Records Without Sending Data to the Cloud: Oxcyon's Centralpoint AI Governance Platform applies entity extraction using OpenAI, Gemini, Llama, or embedded models — your choice — while keeping prompts and skills on-prem. Centralpoint meters consumption and embeds extraction-powered chatbots into your portals via a single JavaScript line.


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