Namesakes · separating names

When a machine decides who you are — and gets it wrong.

Two people share a name. The search engine treats them as one. One inherits the other's story — a court case, a scandal, a fraud charge that was never his. This page is about why it happens and how to separate the two people.

A name is not a unique identifier. When a search engine or a language model reads the web, it sees not ‘Evgeny Tsyplakov’ but pages where the same string of characters appears, and it tries to work out who stands behind them: different people, companies, places. Separating people with the same name is the work after which the system begins to tell two people apart instead of taking them as one.

This is not a rare edge case but the ordinary weakness of the internet. The consequences are concrete: a visa officer reads someone else's story under your name; an investor's check runs into another person; AI answers ‘who is this?’ with someone else's biography.

Signal What the engine reads Why this helps separate
Structured data ORCID, Wikidata, schema.org markup on a personal site Gives a name its own sign a machine can tell you by
Agreement of sources Independent sources matching on profession, city and connections When sources agree, it is clear at once who is meant
Links to others Links between you and verified people and organisations A connected person is harder to confuse with someone else
How unlike the namesakes are How many people bear the name and how different they are The more differences, the more surely the engine splits them

How the engine sees a name

How does a search engine decide which person a name refers to?
Through indirect signals. The engine gathers everything published under a name and tries to see who is behind it: it leans on structured data (ORCID, Wikidata, a knowledge-graph entry), on sources that agree with each other about profession, city and affiliations, and on how many different people share the name. When those signals are few or contradict each other, the engine may merge two different people into one.
Why do search engines confuse me with someone who shares my name?
A name is not a unique identifier. When two people share a name and the differences are few, the engine treats them as one person, and one ends up with the other's links. This confusion is not a rare glitch but the usual result of too little information — which is why it happens to ordinary people, not only to celebrities.
What is a knowledge graph, and why does it matter for a name?
A knowledge graph is what a search engine knows about the world: people, companies, places and the links between them. Everyone it has recognised has properties and connections. If you are not in this system, or little is known about you, or you are mixed up with someone else, the engine cannot show you correctly. And the error spreads further: into results, into the knowledge panel, into AI answers.

How it is fixed

How is mistaken identity with a namesake actually fixed?
By strengthening what shows that it is you we are talking about: consistent data about you and supporting sources tied to your real profession, city and connections. As they build up, the engine separates the two people, and queries about you stop returning the other person's story. The work is about what is known about you, not the results page itself.
Which sources help separate two people who share a name?
Professional identifiers that agree with each other — ORCID, Wikidata, a personal site with structured markup — plus sources that match on profession, city and connections. The stronger and more consistent they are, the harder it is for the engine to glue you to someone else. Here what decides is consistency, not volume.
Do AI systems confuse people the same way search engines do?
Yes, and often worse. A language model assembles a single answer from everything it has read about you, so thin or contradictory data produces a confident but wrong biography — and there is no results list here that you could correct. The same thing helps: consistent, well-structured and verified sources that the model reads when composing its answer.

What it is not

Can you remove a namesake from search results?
No — and it is not the goal. The other person has a right to their own story. The task is to separate the two, not to erase one: to make the engine see two people and, for each query, return the right one. Anything that promises to ‘delete’ a namesake is either a lie or unlawful.
What is the difference between correcting your data and suppressing information?
Suppression hides material; correcting data explains to the engine who is who. Separating two people works with what is known about you — data, consistency of sources, connections — so that the right person comes up for the right query. Nothing is hidden: two people simply stop being one. This is work for accuracy, not for concealment.

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A confidential audit of how a name currently reads in search and AI — with a realistic target state and a path to it.