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A result count tells you that the API returned something. It does not tell you whether it found the entity you meant. In one daily visibility check, a Knowledge Graph Search query for a person returned four results, and the count looked healthy. Two of the results were the same person under different machine IDs, and one was an Andean condor. The lesson is to log the identity and ranking of the top match, not just how many items came back.
What the response actually contained
The case comes from a single article by Marin T. Kael describing one response from Google’s Knowledge Graph Search API. The author’s daily check asked whether a named person was recognized as an entity. The response held four items:
- Two entries labeled “Marin T. Kael” with different machine IDs. One carried the description “Author” and the other had no description at all.
- An Andean condor. Condors belong to the New World vulture family, Cathartidae, so the title’s word is accurate even though the result was clearly unrelated to the query.
- A fourth result that fell below the author’s logging cutoff and was not stored.
The scores tell the more important part of the story. The first two entries scored 43, the condor scored 0.0374, and the article describes a drop of about three orders of magnitude between the second and third displayed entries. A simple count of four treats a strong identity match and a near-zero noise hit as equal. They are not equal.
Why a count hides the problem
Storing only the number of results discards three things you need to audit a match:
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- Which ID ranked first. If the top ID changes between days, the entity Google resolves to has changed, even when the count stays at four.
- How strong the top match was. A score of 43 and a score of 0.04 can both produce “4 results”.
- Whether duplicates exist. Two IDs for one person look like two people if you only count rows.
The article’s core point is that a number you cannot check against the raw response is a number you are choosing to trust. A count is easy to graph and easy to misread, which is why it is a poor sole record of a recognition check.
What to store instead
A workable record for each run keeps the following fields. The article suggests this set, and you should adapt it to your own query:
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- Timestamp and query string, exactly as sent, so runs can be compared.
- Top machine ID and its name and description, if any.
- Top score, stored as a raw number with no rounding.
- Count of results at or above your floor, with the floor value written next to it.
- First-seen date for the top ID, so you can tell a new entity from a long-standing one.
- The top three raw results, including IDs, names, descriptions and scores, so a surprising answer can be reviewed later.
Keeping raw results costs storage, but it is the only way to explain a result like the condor after the fact.
Choosing a floor, and what the score does not mean
A floor is the minimum score you treat as a plausible match. The article’s floor is a choice the author made, not a threshold documented by Google. If you adopt a floor, label it as your own rule in your logs, and expect to revisit it when you see real data. Because the floor is arbitrary, a count above it is only as meaningful as the floor itself.
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The article also cautions against reading the score as a probability or a percentage, or comparing it across different queries as if it were one shared scale. Within a single response, you can reasonably say that one result scored far higher than another. Across queries, treat scores as relative within each run unless you have documentation that defines them. The article did not confirm Google’s official definition of the score, so do not build confidence figures on top of it without checking the current API documentation yourself.
Person queries and work queries behave differently
The article also tested work-level queries. A book title and a series name, each combined with the author’s name, returned zero results even though the person query resolved. The author reads this as evidence that recognizing a person is different from associating that person with their works. For a monitoring job, a zero from a title query does not mean the author is absent from the graph, and a resolved person does not guarantee that their works are indexed under the query you wrote. Run and log these query types separately.
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How far this evidence goes
This is one response, from one query, described by its author. The article’s exact-title coverage was available, but the author’s own page could not be opened for full verification. The official score semantics, the request parameters and the current response schema were not independently checked here. Treat the specific numbers, including the 43 and 0.0374 scores, as the author’s reported observations rather than documented API behavior. The logging approach stands on its own: a count without identity and score cannot be audited, whatever the API’s exact scoring rules turn out to be.
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