feat(normalizer): drop unmatched-names.csv; unresolved-names is the names report

The unmatched list was just non-family correspondents (expected noise);
their count stays in summary.txt and they remain in canonical-persons.xlsx.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

tools/import-normalizer/README.md

@@ -25,15 +25,16 @@ Outputs:
 | Review file | What to do |
 | --- | --- |
 | `unparsed-dates.csv` | For each `raw` (sorted by frequency), fill `suggested_iso` + `suggested_precision`, then paste `raw,suggested_iso,suggested_precision` into `overrides/dates.csv` (header `raw,iso,precision`). |
-| `unmatched-names.csv` | If `suggested_id` is right, copy `raw,suggested_id` into `overrides/names.csv`; else look up the correct id in `out/canonical-persons.xlsx` (the `person_id` column). |
-| `unresolved-names.csv` | Names whose value is itself problematic, grouped by `category`: `unknown` (`?`/illegible), `single_token` (first OR last name only), `relational` (`Tante …`), `collective` (`Familie …`), `prose` (a description landed in a name column), `ambiguous_pair` (two given names → likely two people, not auto-split). Review highest-impact categories first; add decisions to `overrides/names.csv`. |
+| `unresolved-names.csv` | Names whose value is itself problematic, grouped by `category`: `unknown` (`?`/illegible), `single_token` (first OR last name only), `relational` (`Tante …`), `collective` (`Familie …`), `prose` (a description landed in a name column), `ambiguous_pair` (two given names → likely two people, not auto-split). Review highest-impact categories first; add decisions to `overrides/names.csv` (look up valid ids in `out/canonical-persons.xlsx`). |
 | `index-file-mismatch.csv` | The `Datei` path disagrees with the index-derived filename — reconcile when the PDFs arrive. |
 | `duplicate-index.csv`, `blank-index-rows.csv`, `skipped-x-suffix.csv` | Inspect; fix in the source spreadsheet if needed. |
 
-> `unresolved-names.csv` is the focused "names that need a human" list — distinct from
-> `unmatched-names.csv` (which is just non-family correspondents that got provisional persons).
-> The given-name set that drives `ambiguous_pair` detection is the register's first names plus
-> `config.EXTRA_GIVEN_NAMES` — add names there if a real two-person cell isn't being flagged.
+> `unresolved-names.csv` is the focused "names that need a human" list. Non-family
+> correspondents that simply aren't in the register are NOT reported — they just become
+> provisional persons in `out/canonical-persons.xlsx` (the `unmatched_name_strings` count in
+> `summary.txt` tracks how many). The given-name set that drives `ambiguous_pair` detection is
+> the register's first names plus `config.EXTRA_GIVEN_NAMES` — add names there if a real
+> two-person cell isn't being flagged.
 
 **Valid `person_id` values** all come from the `person_id` column of `out/canonical-persons.xlsx`.
 

tools/import-normalizer/normalize.py

@@ -83,14 +83,6 @@ def run(*, document_workbook, document_sheet, person_workbook, person_sheet,
     writers.write_review_csv(review_dir / "unparsed-dates.csv",
                              ["raw", "count", "example_rows", "suggested_iso", "suggested_precision"], unparsed_rows)
 
-    unmatched_rows = []
-    for name, rows in sorted(ctx.unmatched.items()):
-        sid, score = alias_index.suggest(name)
-        unmatched_rows.append([name, len(rows), " ".join(map(str, rows[:5])),
-                               sid or "", f"{score:.2f}" if sid else ""])
-    writers.write_review_csv(review_dir / "unmatched-names.csv",
-                             ["raw", "count", "example_rows", "suggested_id", "suggested_score"], unmatched_rows)
-
     writers.write_review_csv(review_dir / "duplicate-index.csv", ["source_row", "index"], duplicates)
     writers.write_review_csv(review_dir / "blank-index-rows.csv", ["source_row", "kind", "content"], blank_index)
     writers.write_review_csv(review_dir / "skipped-x-suffix.csv", ["source_row", "index", "base_index"], skipped_x)