Why AI Mistakes Your Business for Another One (And How to Fix It)

Entity misattribution is more common than most business owners realize. The Stanford AI Index 2024 documented that over 18% of LLM outputs involving brand entities contain hallucinations or entity misattributions -- the AI confidently recommending, describing, or attributing facts to the wrong business or person.

For a local business, this typically shows up one of two ways: the AI recommends a competitor with a similar name, or it surfaces information about another person who shares the business owner's name. We've seen audits where an AI platform described a consultant as a pastor and game developer -- because the name matched stronger training data signals pointing to someone else entirely. The client's actual business never appeared.

That's not a ranking problem. It's an entity problem. And fixing it requires a different approach than fixing schema or directory presence.

How AI Decides Which Business You Are

AI platforms resolve entity references through a process called Named Entity Disambiguation (NED). When someone asks "best HVAC company in Dallas," the model identifies the entity category and then resolves which specific businesses match.

The resolution is probabilistic, not alphabetical. The model picks whichever entity appears most frequently and consistently in its training data for that query context -- this is called popularity bias. A plumbing company with a name that overlaps with a hardware chain will lose the disambiguation contest to the chain every time, because the chain appears more often across the web.

Our session 9 research (2026-04-30, `knowledge/wikidata-entity-disambiguation.md`) documents a related failure called entity fragmentation. When a business is described differently across directories -- G2 says "AI audit tool," Capterra says "SEO visibility platform," Crunchbase says "marketing analytics software" -- the model may treat those as distinct entities rather than one business. Each description competes against the others, and none reaches the confidence threshold for reliable citation.

The inverse is also true: when four or more sources use consistent language to describe the same business, confidence in entity resolution increases and the business gets cited more reliably.

Why Wikidata Matters for This Specifically

In October 2025, Wikimedia Deutschland, Jina.AI, and DataStax launched the Wikidata Embedding Project. All 119 million Wikidata entries were converted into vector representations, stored in a format any RAG pipeline can query directly -- no custom engineering required. Our session 3 research (2026-04-23, `knowledge/wikidata-entity-disambiguation.md`) noted that before this project, integrating Wikidata into an AI pipeline required significant custom work. After October 2025, it became plug-and-play, with explicit Model Context Protocol (MCP) support bridging AI systems directly to Wikidata.

The practical implication: Wikidata is no longer just training data that got incorporated at some point. It's a live disambiguation signal that AI developers can pull in directly at inference time.

A May 2025 arXiv paper (2505.02737) confirmed the mechanism: using Wikidata's class-subclass hierarchy -- the structured taxonomy categorizing every entry -- significantly improves Named Entity Disambiguation accuracy in zero-shot settings. AI systems that incorporate Wikidata can prune the entity candidate space using structured taxonomy before committing to a resolution.

This is why AEO guides now recommend creating a Wikidata entry for your business.

The Risk No One Mentions

Our session 5 research (2026-04-25, `knowledge/wikidata-entity-disambiguation.md`) investigated Wikidata's notability policy in detail -- specifically because standard fix plan language said "create a Wikidata Q-item in 45 minutes" without qualification.

Wikidata's Criterion 2 -- the one relevant to most businesses -- requires that an entry be "clearly identifiable and described with reliable, public sources." Their definition of reliable, public sources: press articles, scientific publications, or references in public databases. A business's own website, LinkedIn page, or social media profile are explicitly listed as insufficient.

Entries created without qualifying sources are subject to deletion by Wikidata editors. Deletions of company entries for lack of notability appear regularly in WikiProject Companies discussions. A business that spends 45 minutes creating a Wikidata entry that disappears within a week comes away with a lower opinion of the entire AEO category -- and no improvement in entity resolution.

Most local SMBs do not qualify under Criterion 2 if they have no press coverage. The majority operate as sole proprietors without formal incorporation in a searchable public registry.

Who Actually Qualifies

Two categories of businesses can credibly create and maintain a Wikidata entry.

The first: businesses with at least one qualifying external source. BBB accreditation qualifies -- the Better Business Bureau is a public database with indexed entries. A provincial or state corporate registry listing qualifies. A single press article from an indexed publication qualifies. If any of these exist for your business, you likely have enough to create a defensible entry.

The second: businesses with documented misattribution. If Gemini is describing your business with someone else's background, or ChatGPT is consistently surfacing a competitor when your name is searched, fixing entity resolution is the right priority -- and Wikidata is the cleanest available tool if you meet the source requirement.

For home service businesses with no media coverage and no corporate registry entry, creating a Wikidata entry is not the right first step. It may not be possible at all without building the external footprint first.

What Fixes This If You Don't Qualify

The underlying entity fragmentation problem is fixable through source consistency, Wikidata entry or not.

If every directory profile -- Yelp, BBB, Foursquare, Angi, Thumbtack -- describes your business using the same name, category terms, and service description, the model's confidence in resolving your entity increases. The goal is not perfection in a single listing. It's consistency across the set.

Four sources describing you identically outperforms one perfectly detailed source in every case we've seen. "HVAC contractor serving Dallas-Fort Worth" across all listings is more entity-stable than "HVAC company," "air conditioning services," and "climate control specialists" scattered across different profiles.

NAP consistency -- name, address, phone number -- is the minimum. Category and description consistency matters too, and it's the part most fix plans skip.

How to Know If You Have an Entity Problem

Sourcepull's Signal Check runs live queries across ChatGPT, Perplexity, Gemini, and Google AI Mode. It doesn't just measure citation rate -- it captures the actual AI responses, which means misattribution shows up in the results directly. If a platform is confusing your business with another entity, or citing a competitor where your name should appear, the Signal Check output will show it.

It's free and takes about 90 seconds. If you've ever wondered whether AI knows the right version of your business -- rather than a different business that shares your name -- that's where to start.