Short answer: an entity resolution benchmark should not invent an "authority" score. You can reproducibly measure whether a company's name, URLs, attributes, and relationships are consistent between first-party sources and relevant external surfaces. Google documents that Organization structured data can help understand and disambiguate the organization. The proposed benchmark, however, measures the quality of the information you control and the observed conflicts, not a Google ranking factor.

The population of the benchmark

Choose the critical entities: the organization, the main products, possibly the sub-brands and the relevant authors. It does not include every internal tag or campaign name.

For each entity define the material attributes:

  • canonical name;
  • canonical URL;
  • product-organization relationship;
  • category;
  • logo;
  • relevant external profiles;
  • integrations or public attributes with decision-making impact;
  • historical names, if they still appear.

Sources included

It divides the sources into three groups:

  1. first-party canonical;
  2. first-party secondary;
  3. relevant third-party.

Does not include any web mentions. The benchmark must be repeatable. If the third-party list changes chaotically, the result is not comparable.

The baseline

At time zero, it exports the values for each attribute and source. Brand:

  • `consistent';
  • ``conflict'';
  • ``missing'';
  • `stables';
  • ``not applicable''.

Save the date and reviewer. Thus, a re-audit can reproduce the decision.

Metric 1: first-party consistency rate

Numerator: conflict-free first-party attributes. Denominator: eligible first-party attributes.

This metric is controllable by the organization and should take precedence over external scores.

Metric 2: external profile consistency

Measure selected third-party profiles where the name, URL and category match the registry. It does not automatically penalize sources that the company cannot control; classify ownership separately.

Metric 3: material conflict count

Count the conflicts that can change a decision: wrong plan, old integration, product assigned to another entity, invalid URL, incomplete rebrand.

Do not dilute the result with cosmetic differences.

Metric 4: author identity coverage

For eligible articles, check byline, profile and author.url. Google recommends author identification for Article structured data and allows URLs that clarify identity.

This metric does not apply to product pages or other non-article pages.

Metric 5: resolution time

It measures the time between finding and closing the material conflict. A system with a good score, but conflicts that remain for months, is not workable.

How do you build the benchmark between products

If you have multiple products, compare the same list of attributes and sources. Do not change the criteria for the product that looks weaker.

For large portfolios, sample representative products and declare the population. Do not extrapolate a five-product benchmark to the entire catalog without justification.

Repeat measurement

Run the benchmark after material events: rebrand, acquisition, domain change, product launch, CMS migration. You can also keep a quarterly drift cadence.

Compare the same population and the same definitions. If the methodology changes, version the benchmark.

Integration with Search and AI monitoring

You can separately add comments about how the brand is described in Search or AI answers: name, category, source citations, factual accuracy. Don't mix them directly into the first-party score.

These surfaces are external outputs and can vary independently of the registry.

False attribution risks

An external representation improvement can coincide with PR, reindexing, updating a marketplace or a platform change. Do not automatically assign the Organization markup effect.

Structured data can help with disambiguation according to Google's documentation, but the benchmark does not prove causality over an AI response.

Acceptance criteria

A benchmark is reproducible if it has fixed population, versioned registry, defined sources, classification rubric, reviewer, date, raw observations and `not applicable' rules.

If two raters frequently grade the same situation differently, clarify the rubric before reporting the percentage.

Example of a conflict column

A `P0'' conflict can be a wrong first-party claim about the product that affects contracting, security, or availability.P1' may be a major marketplace with the wrong category. `P2' can be a secondary external profile that uses the old name but points to the correct domain.

This rubric makes the benchmark actionable. Two companies can have the same number of conflicts and completely different levels of risk. It reports the distribution by severity, not just the total.

Reliability of evaluators

For a benchmark used in leadership, take a sample and ask two raters to rank it independently. If the labels differ frequently, the definitions are too vague. Fix the rubric before extending the audit.

Keep examples for each category: what stale' means, whatconflict' means, and when a lack is `not applicable'. This documentation reduces drift between quarters and between teams.

What you don't compare between companies

Do not compare raw counts if portfolio size differs greatly. Normalize only when the denominator makes sense and explain it. A 50-product SaaS and a single-product SaaS should not be measured by the same raw number of profiles or relationships.

Publication of the internal benchmark

If the result ends up in an executive report, keep the method next to the percentage. A number without population, date, and definitions will be reused in contexts for which it was not constructed. Includes link to registry and rubric version.

Claim ledger

  • FACT/EVIDENCE: Google documents Organization structured data for understanding and disambiguation.
  • FACT/EVIDENCE: Google recommends author identification for Article markup.
  • PRACTITIONER GUIDANCE: the benchmark must measure conflict and consistency on a stable population.
  • NOT PROVEN: a universal entity-resolution score used by AI engines.

Conclusion

The useful benchmark is boring in a good way: same entities, same attributes, same rubric and same sources. It is this repeatability that makes it more valuable than an authority score that you cannot explain.

Sources reviewed

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