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AI agent for historians

Historical Place and Name Disambiguation Agent

Names in a set of sources are matched to the right place or person, with the evidence recorded and approved

Data cleanupScience and research
See how it worksGet access
Historical Place and Name Disambiguation Agent: what goes in, what the agent does and what you get

What it does

Names of places and people change over time, and two towns or two people with the same name get confused. This agent matches names in sources to gazetteers and authority lists for the right period. It flags ambiguous matches, proposes identifications with evidence such as nearby places, dates and titles, and asks the historian to confirm. After a confirmation, it rechecks related records to apply or question the same identification. It keeps a log of reasons. The historian approves identifications. Edge case: a place called Newport appears in two counties, so the agent checks nearby named places and the date before proposing a match.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
Yes, continueApprovedYes, continueNoNo 1 STARTS WHEN New records added 2 USES A TOOL Read names, dates and context in each record 3 USES A TOOL Search gazetteers and authority lists for the period 4 DOES Rank candidate matches by name form, date and nearbyplaces 5 CHECKS THE RESULT Is there one candidate that fits name, date andcontext? If not: mark ambiguous and request more context. Back tostep 3. 6 DOES Draft the identification with evidence 7 YOU APPROVE Historian confirms or rejects each identification 8 USES A TOOL Reread related records after each confirmation 9 CHECKS THE RESULT Do related records fit the confirmed identification? If not: flag the conflicts and revisit the match. Backto step 4. 10 DOES Update the authority log 11 RESULT Confirmed identifications log
Read the steps as a list
  1. New records added
  2. Read names, dates and context in each record
  3. Search gazetteers and authority lists for the period
  4. Rank candidate matches by name form, date and nearby places
  5. Is there one candidate that fits name, date and context?If not: mark ambiguous and request more context. Back to step 3.
  6. Draft the identification with evidence
  7. Historian confirms or rejects each identificationThe agent waits here for your OK.
  8. Reread related records after each confirmation
  9. Do related records fit the confirmed identification?If not: flag the conflicts and revisit the match. Back to step 4.
  10. Update the authority log
  11. Confirmed identifications log

How it decides

A match is proposed when name form, period and context agree. Anything else stays ambiguous until confirmed.

  • Match on period-correct names first
  • Require 2 supporting context clues for a proposed match
  • Keep multiple candidates when context is thin
  • Record the source for every identification

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Authority lists
  • Clues required (default 2)
  • Period range
  • Log format

What keeps you in control

It always asks you first

  • Every identification

Hard limits

  • Never records an identification as certain without approval
  • Never merges two people on name alone

It stops when

  • Done: all names matched or marked ambiguous
  • Stop: no suitable authority list for the period

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensA 1792 letter mentioned Newport and a merchant named J. Hale. The agent found two Newports and the first match used a modern boundary, so the context check failed. It found a nearby named ferry that fits the second Newport and proposed that with two clues. The historian confirmed. Two related letters fit the same place, but one conflicted, so the agent flagged it for review.

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