Entity resolution is the process of determining when separate records, often from different sources and in different formats, refer to the same real-world entity, such as one company or one property. It is what turns a pile of scattered records into a connected graph where each real business or parcel appears once, with all of its records attached.
Two records naming Acme LLC might be the same company or two unrelated businesses in different states. Names are reused, abbreviated, and misspelled, so matching on name alone produces false merges that quietly corrupt everything downstream. Reliable resolution leans on strong shared identifiers (registration numbers, parcel keys, federal IDs) and treats a name as a weak hint, never proof.
A resolver can err two ways: miss a true match (low recall) or wrongly merge two entities (low precision). For business and property data, a wrong merge is the more dangerous error, because it attaches one company's liens or lawsuits to another. The trustworthy default is to keep records separate unless the evidence genuinely connects them.
Related on Amalgament: Business records API · Property records API
They are related. Deduplication usually means collapsing duplicate rows within one dataset; entity resolution is the broader problem of linking records across many different sources to one real-world entity.
Because names are not unique or stable. Distinct companies share names, and the same company is written many ways. Merging on name alone creates false entities, so strong identifiers must carry the join.
More terms: KYB (Know Your Business) · UCC filing (UCC-1) · APN (Assessor's Parcel Number) · MCP server · Business registry (Secretary of State filing) · UEI and SAM.gov · Non-FCRA (business-purpose) data · Context layer (for AI)