Named entity extractor
Turning a wall of LLM prose into structured data starts with pulling out the named entities — who, where, when, and how much. This tool does browser-side named entity recognition with no model and no upload, finding persons, organizations, locations, dates, money amounts, emails, and URLs and letting you copy them as clean JSON.
How it works
The extractor runs two complementary passes. First, regular expressions capture the well-structured types: ISO and natural-language dates, money amounts with currency symbols, emails, and URLs. Second, a capitalization heuristic groups runs of consecutive capitalized words into candidate proper nouns, then classifies each using keyword cues — suffixes like “Inc”, “Ltd”, or “University” mark organizations, words like “City”, “River”, or known place words mark locations, and the remainder default to persons. You can filter which types are shown and export the grouped results as JSON.
Tips and notes
- Structured types are the reliable ones. Dates, money, emails, and URLs are matched by pattern and rarely wrong; treat person/org/location as best-effort.
- Use it as a first pass. Heuristic NER is ideal for quickly structuring output or pre-filtering before a heavier model — not as a final ground truth.
- Sentence case matters. The name heuristic relies on capitalization, so all-lowercase or all-caps text yields fewer person/org/location hits.
- Everything stays local. No text leaves your browser, so extracting entities from private documents is safe.