Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Entity resolution on ~950 merged customer records (nicknames, swaps, aliases, phone formats) without merging households

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  "result": "I wrote `dedupe.py` and ran it on `customers.csv`. It produced `clusters.json` with 435 clusters covering all 946 records, each id appearing exactly once. I could not measure the 0.95 precision and recall targets, because no ground-truth file is provided. Those numbers are unverified.\n\n**What the script does**\n- **Blocking:** records can only be the same person if they share a normalized address (house number, street, city). Street suffixes are normalized (Drive/Dr, Street/St, and so on).\n- **Name matching:** first and last names are compared in either order. Matches allow nicknames (`Maggie`\u2192Margaret, `Liam`\u2192William), typos, and initials. Transposition typos like `matoe`/`mtaeo` count as the same name. Short names (under 4 letters) get no typo tolerance, which stops `Jim`/`Kim` from matching.\n- **Contradictions block a merge:** two different emails, or two different phones, keep records apart. This separates household members and common-name collisions.\n- **Strength of evidence:**\n  - An exact email match is the strongest signal.\n  - A full name match at the same address is a merge.\n  - An initial only merges when no other record in the block could take it. Household members share phones, so an initial plus a shared phone is weak evidence.\n- **Clustering:** two clusters merge only if every cross-pair of records is compatible. This prevents chaining through a household member.\n\n**Checks I ran**\n- Every multi-record cluster I sampled looked like one person.\n- Every singleton that shares an email or phone with a record at another address turned out to be a household member, so no address-typo duplicates were missed.\n- Every fuzzy name collision in the data was reviewed. I fixed the false positives I found (`dick`/`mick`, `jim`/`kim`).\n- On a row-shuffled copy of the file, the clusters came out identical, so the result does not depend on row order.\n\n**Choices to review**\n- The rule that an address must match exactly (after normalization) is an assumption. The task lists no address changes between duplicates, and in the email groups the addresses agreed.\n- Two different people with the same full name at the same address and no contact details would be merged. I found no such case in this file.\n- Some true duplicates may stay split when both records lack an email and their address differs by more than a typo. Those are the most likely recall losses.",
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