Claude Sonnet 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. It turned the 946 records into 460 clusters and wrote them to `clusters.json`. Every id appears in exactly one cluster, singletons included. I couldn't score it, because there's no ground truth here, so I can't confirm the 0.95 precision and recall targets. I only read through 12 random multi-record clusters, and all of them looked like correct merges.\n\nTwo records are merged when they pass both checks below, and the clusters are the connected groups of those merges:\n- **Same name:** the names are compatible, in either order, and ignoring case and accents. The check allows nicknames (Bob/Robert, Liz/Elizabeth), initials, and one or two typos (Ivna/Ivan, Inse/Ines, Emli/Emil). It also uses the full name taken from the email address, so \"P.\" can match \"Patricia\".\n- **Same contact detail:** they share an email once the `+tag` is dropped and case is ignored, or the same last 10 digits of a phone number. Failing that, they share a normalised street address (St/Street, Dr./Drive and so on) with no conflicting zip.\n\nHousehold members who share a phone and address stay separate because their first names don't match. Different people with the same common name stay separate because a name match alone never merges anyone.\n\nThe code has no hard-coded ids, so it should run on a fresh file of the same kind. One weak spot: a record with just an initial (such as \"P. Moreau\") and a shared address could be wrongly merged into a household member with the same initial (say, a \"Paul Moreau\"). I didn't test for that.",
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