Fix Entity Resolution: Why AI Confuses Your Company
AI systems can combine facts from several different versions of your company: the brand, the legal entity, an old name, a product, a founder, a parent or subsidiary. When those versions conflict across sources, AI resolves the wrong business or none at all. This is a process for finding where the split begins and correcting it at the sources that carry the most weight.
By Maria Dykstra · AI Visibility Architect · Last updated: July 2026
Entity resolution is the process of determining which real-world organization a name, domain, profile, or claim refers to. Companies get harder to resolve when names, URLs, categories, ownership, and facts conflict across sources. Organization schema and legitimate third-party identity records give AI disambiguation clues. No single database or markup field guarantees AI visibility.
A machine-readable identity record can help systems connect consistent facts about your company. It does not guarantee retrieval, recommendation, or citation. The work here reduces confusion. It does not buy placement.
Most B2B companies with an entity problem don't have it because their content is weak. They have it because the facts about who they are disagree across the places AI looks. The brand name and the legal name look like two companies. Crunchbase says one category, the homepage says another. An old name still resolves ahead of the current one. AI reconciles the conflict on its own, and the result is inconsistent.
If the two platforms describe your company differently, that gap is a useful symptom. It suggests ambiguous or conflicting public information. It does not by itself tell you the cause. The two systems use different indexes, different retrieval, and different source dates. Disagreement points you toward a resolution problem. Confirming the cause takes the diagnostic below.
Entity resolution runs throughout retrieval and generation, not as a single gate before your content is read. Ambiguous identity signals make it harder for a system to connect the evidence it retrieves to the correct company. That is the failure this guide addresses: not that you're absent from a registry, but that your facts don't agree with each other.
The companies AI describes consistently made every version of themselves point back to the same business. Same name logic, same category, same founding facts, same official URL, everywhere it mattered.
In Wave 1 of the Algorithmic Authority Index study (20 B2B companies, 5 industries, 3 platforms, Q4 2025), the companies AI described consistently shared one trait the inconsistent ones lacked: their facts agreed across their own surfaces and across the major third-party profiles. Across the 20, platforms agreed on basic identity 69% of the time. The disagreement clustered on companies whose own name, category, and founding facts conflicted from one source to the next.
One illustrative composite from audit work: a B2B workforce-analytics company with strong content and a clean site. ChatGPT called it an "HR technology provider." Perplexity called it a "business intelligence company." Neither matched its own category. Crunchbase listed a third. The company existed on the web. Its facts didn't agree, so AI reconciled them differently each time. This is the gap between a company existing and its facts being consistent enough to resolve. Content quality does not close that gap.
Do you have an entity resolution problem? Run this test.
Fifteen minutes. It tells you whether your facts agree across sources or whether AI is reconciling conflicting versions of your company.
Run it clean. Use fresh sessions with no chat history. Enable search or browsing on each platform. Use the identical prompt. Run it three times per platform. Note the date. Review the sources each answer cites. Compare every fact against your own records and independent coverage. One answer from each platform is too noisy to draw conclusions from.
A worked attribute-conflict matrix
| Fact | Your site | Crunchbase | Conflict | |
|---|---|---|---|---|
| Company name | Acme | Acme Technologies | Acme Inc. | Yes |
| Founded | 2018 | 2019 | 2017 | Yes |
| Category | Risk platform | Compliance software | IT services | Yes |
| Headquarters | Boston | Cambridge | New York | Yes |
| Official URL | acme.com | acme.com | acme.com | Clean |
This tells you far more than "does a Wikidata entry exist." Four conflicting facts across three sources is a company AI cannot resolve consistently, no matter how good the content is.
How to read your results
| Result | Score | What it means |
|---|---|---|
| Both platforms agree on accurate facts across runs; your matrix is clean; cited sources match your records | Consistent | Your facts resolve to one business. Any remaining invisibility is upstream: content, authority, or category positioning, not resolution. |
| One or two conflicting fields; platforms mostly agree but occasionally drift on category or founding year | Ambiguous | AI can usually resolve you but works harder than it should, and drifts under some prompts. Fixable by correcting the conflicting sources. |
| Platforms disagree across runs; multiple conflicting fields; old name or product bleeding into results | Conflicted | Your public facts contradict each other. AI reconciles them differently each time, which produces fabricated or inconsistent answers. This is the primary failure for companies described inaccurately despite strong content. |
- Which source caused a specific wrong fact
- Whether the answer came from training data or live retrieval
- Whether you're being confused with a similarly named entity
- Whether the failure is name, category, relationship, or evidence
- Whether the same error shows up in buyer-intent queries, where it costs you
The Snapshot tests the failure in live buyer and branded queries and traces which surfaces are feeding the conflict.
Why entity resolution fails. And why more content doesn't fix it.
Entity resolution is not a single step that runs before AI reads your content. Systems resolve entities throughout the process: while retrieving passages, while ranking sources, while generating the answer, and sometimes from parametric knowledge with no live retrieval at all. What consistently hurts you is ambiguity. When the retrieved evidence about your company disagrees with itself, connecting that evidence to the right business gets harder, and the output gets less reliable.
This is why strong content stays invisible. Publishing more posts adds more signals to a picture that already doesn't agree with itself. You're not short on evidence. You're short on evidence that points to one business.
A working model, not a claim about internal architecture: consistent structured and unstructured evidence reduces the inference a system has to do to connect facts to the right company. Less inference tends to mean fewer errors and more stable answers. I can't see inside the platforms, so I hold this as a hypothesis that the diagnostic tests, not as a description of how any specific model works.
Corroboration across three evidence types
AI rarely verifies a fact from one place. It corroborates across evidence types. Owned evidence is your own site and schema. Structured third-party records are profiles like LinkedIn and Crunchbase. Independent web evidence is press, analyst coverage, and cited content. When these agree, the fact is well corroborated. When they conflict, it's ambiguous.
Entity Triangulation is my diagnostic lens, not a score a model publishes. The labels describe how well your evidence agrees, not a confidence percentage inside any platform. The underlying idea that knowledge bases represent facts as subject-predicate-object triples is longstanding, dating to Google's 2014 Knowledge Vault paper. That paper supports the triple as the unit of a machine-verifiable fact. It does not validate any scoring model.
Why your legal name and brand name create a hidden split
Many B2B companies have a legal name that differs from the brand. "Acme Solutions Holdings, LLC" versus "Acme." To a person, these are obviously the same. To a system resolving evidence, they can look like two entities with related footprints unless something explicitly links them.
Your site uses the brand. Legal filings use the legal name. Crunchbase may use either. The fix is declaring the relationship: use the legalName and alternateName properties in your Organization schema, and keep both names consistent across profiles. Declaring the relationship gives systems an explicit clue that the names refer to one organization. It does not force any platform to merge them, but it removes the excuse to treat them as two.
How to fix entity resolution: the sequence
Total time: 3 to 5 hours for the core work on your own sources and schema. Correcting third-party profiles depends on each platform's edit process.
When complete: every version of your company points back to the same business, with consistent name, category, founding facts, and URL across the sources that carry weight.
@id (for example https://yoursite.com/#organization). Reference that same @id from other page schema rather than repeating a full Organization block on every page. Put only true identity-equivalent URLs in sameAs: your LinkedIn company page, your Crunchbase organization, your official social profiles, and a legitimate Wikidata item if one exists. Use the copy-paste blocks below. Suppress Rank Math or Yoast auto-generated Organization schema so you don't publish two competing Organization nodes.On Google Knowledge Panels: a panel can be evidence that Google recognizes a distinct entity, and it draws on consistent public information across the web. You cannot force one with a recipe, and its absence does not prove your company is unresolved. Improve the consistency of your public facts and treat a panel as a possible byproduct, not a target with a timeline.
What this looks like: before and after
Illustrative composites drawn from audit patterns, not single named clients or controlled experiments. Other changes and platform behavior can move results during the same window.
Example 1: The workforce-analytics company AI couldn't place
B2B workforce-analytics company. Strong product, active blog, consistent signals on its own site. But Crunchbase listed it under "business intelligence" while the homepage claimed "workforce analytics," and LinkedIn used a third category.
ChatGPT described it as "an HR technology provider." Perplexity described it as "a data analytics company." Two descriptions, neither matching the canonical category. Both were reconciliation of conflicting sources.
The team set canonical values for name, category, founding year, and URL. They aligned the homepage, LinkedIn, and Crunchbase to that set, and added a single canonical Organization schema with a stable @id and an accurate sameAs array.
Over the following weeks, ChatGPT and Perplexity moved toward matching descriptions using the canonical category, and the founding year stabilized. The content hadn't changed. The facts stopped disagreeing. Cause of any single answer stayed unconfirmed; the trend tracked the cleanup.
Example 2: The competitive contrast
Mid-market B2B compliance software. Same name, category, founding year, and URL across its site, LinkedIn, Crunchbase, and G2. One canonical Organization schema. ChatGPT and Perplexity returned matching descriptions across runs and placed it in the right category consistently.
Larger company, same category, more content, higher domain authority. But its Crunchbase category differed from its homepage, an old brand name still resolved, and its schema sameAs was empty. ChatGPT and Perplexity returned different descriptions and sometimes surfaced the old name. It appeared inconsistently in category answers.
Company B had more content and more authority. Company A's facts agreed with each other. In head-to-head category prompts, Company A resolved cleanly and Company B did not. The content gap didn't decide it. The consistency gap did.
Organization and Product schema: copy-paste implementation
Two clean, valid JSON-LD blocks. The Organization node is your canonical entity, referenced everywhere by its stable @id. The Product node links back to it with provider, so a system never confuses your product profile with your company.
The notes are outside the code on purpose. JSON does not allow comments. Pasting a block with comments inside the JSON-LD script breaks it. Copy the code as-is and read the notes here.
{
"@context": "https://schema.org",
"@type": "Organization",
"@id": "https://yoursite.com/#organization",
"name": "Your Brand Name",
"legalName": "Your Legal Entity Name, Inc.",
"alternateName": "Any prior or common name",
"description": "Your canonical identity statement.",
"url": "https://yoursite.com",
"logo": "https://yoursite.com/logo.png",
"foundingDate": "2018",
"address": {
"@type": "PostalAddress",
"addressLocality": "Boston",
"addressRegion": "MA",
"addressCountry": "US"
},
"sameAs": [
"https://www.linkedin.com/company/yourcompany/",
"https://www.crunchbase.com/organization/yourcompany",
"https://www.wikidata.org/wiki/QXXXXXXX"
]
}{
"@context": "https://schema.org",
"@type": "SoftwareApplication",
"@id": "https://yoursite.com/product/#software",
"name": "Your Product Name",
"applicationCategory": "BusinessApplication",
"operatingSystem": "Web",
"provider": { "@id": "https://yoursite.com/#organization" },
"sameAs": [
"https://www.g2.com/products/your-product/reviews"
]
}Notes, in plain English:
- sameAs should contain only pages that unambiguously represent the same entity. Company profiles go on the Organization node. Product review pages like G2 go on the Product node.
- The legalName and alternateName fields bridge your name variants so a resolver has an explicit link between them.
- The Wikidata URL belongs in sameAs only if a legitimate item exists. Do not add a placeholder or a self-created promotional item.
- Include a Wikidata
identifierif you have a real item, but understand it is an explicit identifier for humans and parsers. It does not register your company with any AI platform.
WordPress: paste each block inside its own <script type="application/ld+json"> tag in a Custom HTML block on the relevant page. Validate syntax at the Schema.org Validator. Google's Rich Results Test checks eligibility for Google rich results, not whether an entity is resolved, so use it for Google-specific checks and the Schema.org Validator for vocabulary and syntax.
How to know it's working
Re-run the same clean diagnostic. It's working when both platforms return the same founding year, headquarters, and category across runs, without fabricated facts, and your descriptions match your canonical statement. Track the trend, not a single answer.
Separate four things every time you test, because collapsing them is how people fool themselves: whether the answer is correct, which source it cited, the likely cause, and whether that cause is confirmed. A correct answer with an unconfirmed cause is still progress worth recording. Claiming your schema caused it, when the platform cited a different page, is not.
If consistency hasn't improved after a reasonable window, check the usual causes in order. A source you missed still carries an old fact, often an old directory profile or a legacy page. Your schema sameAs points at the wrong URL format. Or a structured category field on a major profile still disagrees with your canonical term while its description reads correctly.
The re-test: Ask both platforms "Tell me about [your company name]" three times each, on the same day, with search enabled. If the founding year, headquarters, and category come back consistent and accurate, public ambiguity has probably decreased. That does not tell you which single change caused it, and it does not need to. Consistency is the goal.
What this reveals about your other AI visibility failures
This guide covered entity resolution: making every version of your company point back to one business. Fixing it removes a source of inconsistency. It doesn't guarantee you appear in every query where you should. It removes the ambiguity that was corrupting the answers you did appear in.
Clean resolution without identity clarity means AI can tell it's you but still can't classify what you do. Your schema and profiles agree on the facts, but your category positioning is vague. That's a Layer 1 problem. See Fix Identity Fragmentation.
Clean resolution without independent corroboration means AI can verify your facts but has little outside evidence for your claims. That's a citation authority problem. See Fix Citation Authority.
The AI Visibility Snapshot screens all 7 layers across ChatGPT, Perplexity, and Gemini in 48 hours and names the first one failing. If your diagnostic came back Conflicted, the Snapshot shows which surfaces are feeding the conflict in live buyer and branded queries, and what's compounding on top of it.
Frequently Asked Questions
Should I create a Wikidata item to improve AI visibility?
No, not as a visibility play. Wikidata has notability and sourcing requirements, and items created primarily for promotion get challenged and deleted. A self-created item cited mostly to your own website is not independent validation. If a legitimate item already exists, keep its facts accurate. If one doesn't, earn independent press coverage first, then revisit. Your consistent schema and corrected profiles do more, sooner, with none of the deletion risk.
My company is in Crunchbase and LinkedIn. Why isn't that enough?
Because presence isn't the issue. Consistency is. If Crunchbase, LinkedIn, and your site disagree on your category or founding year, those profiles corroborate a conflict instead of a fact. The fix is aligning them to one canonical set of values, not adding more profiles.
ChatGPT keeps getting my founding year wrong. How do I fix it?
Find where the wrong year lives. Usually an old profile, a press mention, or a domain record disagrees with your site. Correct the conflicting sources to one canonical year and add foundingDate to your Organization schema. Then re-test over time. Propagation is platform-dependent, so watch the trend rather than expecting an immediate change, and don't assume a correct answer confirms which source the platform used.
How is entity resolution different from identity fragmentation?
Identity fragmentation is about your positioning: your own surfaces describe what you do in different ways. Entity resolution is about your facts: name, category, founding details, and ownership conflict across sources so a system can't reliably tell which business the evidence belongs to. You can fix one and still have the other. The full picture needs both: consistent positioning (Fix Identity Fragmentation) and consistent facts (this guide).
My product and my company get confused in AI answers. What causes that?
Usually schema that treats them as one entity, plus profiles that blur the line. Model them as separate nodes: an Organization with a stable @id, and a Product node that links back with provider. Put company profiles in the organization's sameAs and product profiles like G2 in the product's sameAs. Keeping the two entities distinct in your structured data is more important than any single identifier.
Will fixing this help my Google SEO rankings?
A legitimate Wikidata item may help disambiguate a notable organization, and Organization schema can help Google understand your identity and administrative details. Neither is a guaranteed ranking or citation benefit, and neither substitutes for useful content, independent evidence, relevance, and the rest of search fundamentals. The clearest payoff from this work is fewer factual errors and more consistent descriptions of your company across AI answers.