Our no guess blog 081

People often treat search and resolution as if they were the same operation. In practice, they solve different problems, and confusing them usually creates messy downstream data. That distinction matters a lot in the Wikidata + Google Knowledge Graph MCP server, where kg_search and kg_resolve sit close together conceptually but behave very differently. If you work with MCP for Wikidata, or more specifically with MCP for google knowledge graph and wikidata, the easiest mi

Entity resolution looks deceptively simple until you have to do it at scale, under time pressure, and with records that were never designed to line up cleanly. A person name arrives with one alternate spelling. An organization record carries a city but no founding date. A title is shared by three films, two books, and a song. At that point, the problem stops being “find me the right ID” and becomes “show me why this is the right ID, and tell me when you are not sure.” Th

The value of Wikidata has never been limited to the data itself. The real challenge has always been access. Anyone who has worked with entity resolution, catalog enrichment, or knowledge retrieval knows the pattern: the information exists, the identifiers are there, the statements are structured, yet the path from a plain-language question to a reliable answer is often awkward. One tool uses a search endpoint, another expects SPARQL, a third returns a flood of candidates wi

Entity resolution looks deceptively simple until you have to do it at scale, under time pressure, and with records that were never designed to line up cleanly. A person name arrives with one alternate spelling. An organization record carries a city but no founding date. A title is shared by three films, two books, and a song. At that point, the problem stops being “find me the right ID” and becomes “show me why this is the right ID, and tell me when you are not sure.” Th

Candidate search limits sound like a small implementation detail until you have to trust the output. Then they become one of the most important design choices in the whole system. That is especially true in entity resolution work, where the difference between "probably right" and "provably inspectable" decides whether a match can be used downstream at all. The open source project often described as Wikidata + Google Knowledge Graph MCP takes a very specific stance here.

Entity resolution looks simple until you have to trust it. Anyone who has tried to map a local catalog, CRM, newsroom archive, research database, or product knowledge base to Wikidata knows where the pain starts. Names collide. Labels are incomplete. Dates are missing. Places change names. Organizations merge, split, or rebrand. A human can often sense the difference between a plausible match and a dangerous one. Software needs firmer rules. That is what makes the res

Teams that work with entity data usually hit the same wall sooner or later. Search is easy enough. Reliable retrieval is not. You can find ten plausible entities for a name like "Mercury" in seconds, but narrowing that result to the right record, extracting only the facts you need, and preserving enough evidence for later review is where systems often become messy. That is the problem space where the open source project commonly described as Wikidata + Google Knowledge