Entity Resolution Deciding which references point to the same thing.
Entity resolution is the process of determining whether different names, records, identifiers or references represent the same underlying entity. It is the identity layer that prevents a graph from fragmenting one thing into many duplicates—or incorrectly collapsing different things into one node.
Reliable resolution usually combines multiple signals: names and aliases, stable identifiers, attributes, context, relationships and provenance. No single string comparison is sufficient for every case.
Resolve referenceswithout erasing real differences.
Select a case. The center panel shows the evidence used to compare references, while the right panel produces an illustrative decision: merge, keep separate or review.
Resolution cases
The names differ slightly, but a stable identifier, domain evidence and compatible context support treating the references as one organization entity.
OUTCOME / MERGENames are useful candidate-generation signals, but reliable resolution should use stronger evidence when available.
IDENTITY PRINCIPLEEntity resolution is a sequence,not one fuzzy-match function.
Production systems vary, but a useful conceptual pipeline separates candidate generation, evidence comparison, decision logic and canonicalization.
Standardize case, punctuation, spacing and known formatting differences without destroying meaningful distinctions.
PREPAREFind plausible matches using names, identifiers, addresses, domains or other blocking signals.
SHORTLISTMeasure agreement and disagreement across multiple attributes and relationships.
EVALUATELook for incompatible identifiers, dates, locations or entity types that argue against a merge.
DISAMBIGUATEMerge, keep separate or route the case for human or higher-confidence review.
RESOLVESelect a stable canonical entity while preserving useful aliases and source references.
UNIFYRetain where each claim came from so the resolved entity remains auditable.
TRACEStrong identity decisionscombine different kinds of evidence.
The best signal depends on the entity type and available data. Stable identifiers are often stronger than labels, but even identifiers require source and scope awareness.
Registry IDs, knowledge-base IDs, internal IDs or other persistent identifiers can strongly support identity when their scope is trustworthy.
HIGH VALUECanonical names, abbreviations, transliterations and historical names are useful but can be ambiguous or reused.
CANDIDATE SIGNALDates, addresses, organization type, product category or biographical details can support or contradict a candidate match.
CONTEXTShared owners, employers, locations, products or connected people can provide graph-context evidence beyond string similarity.
GRAPH SIGNALKnowing which source produced a claim helps determine whether two records are independent, duplicated or conflicting.
TRUST LAYERDifferent names can mean one entity.Same names can mean two entities.
Good resolution handles both aliasing and ambiguity. Solving only one side creates serious graph errors.
Different labels, same entity
A company, person or product can appear under abbreviations, historical names, punctuation variants or source-specific labels.
Same name, different entity
Two people, places, products or organizations can share the same visible name. Context must keep them separate.
A match is strongest whenindependent signals agree.
This matrix is conceptual rather than a universal scoring formula. Different domains require different evidence thresholds.
Northstar Labs / Laboratories
normalized variant
same org ID
same domain and address
evidence converges
Jordan Lee / Jordan Lee
same visible string
separate IDs
different profession/location
name is misleading
similar organization labels
string similarity only
no stable ID
some shared attributes
insufficient evidence
Identity errors propagatethrough the entire graph.
A wrong merge or missed merge affects every relationship, traversal path and inference connected to the entity.
Two different entities are incorrectly collapsed because their names or attributes look similar.
OVER-MERGEOne real entity remains fragmented across multiple nodes because aliases or IDs were not connected.
UNDER-MERGEAn identifier is treated as globally unique even though it is only unique within a limited source or namespace.
SCOPE ERRORString similarity is allowed to dominate despite conflicting contextual evidence.
AMBIGUITY ERROROld names, reorganizations or ownership changes are flattened without preserving temporal context.
TIME ERRORAliases are merged, but the system forgets which source supplied each claim.
AUDIT ERRORA person, brand, organization or product is matched across incompatible entity classes.
CLASS ERRORA numeric score looks precise even though the underlying evidence model is weak or undocumented.
SCORE ≠ TRUTHFor semantic SEO, resolution meansconsistent identity across documents.
The practical lesson is not that websites receive a public entity-resolution score. It is that consistent naming, structured identifiers, contextual relationships and clear references reduce ambiguity for users and machines.
Use a stable primary name while preserving relevant aliases where they help explain identity.
IDENTITYKeep important facts such as organization type, authorship and locations internally consistent across pages.
CONSISTENCYConnect people, organizations, products and topics through clear contextual statements and internal links.
RELATIONSHIPSWhere appropriate, use structured data and external identifiers to make entity references more explicit.
MACHINE READABILITYContinue throughthe graph architecture.
Identity resolution connects directly to traversal, inference, semantic SEO, retrieval and AI because every downstream path depends on knowing which entity a reference actually means.
Ground identity linksin explicit public data models.
These references support the concepts of identifiers, equivalence, structured claims and externally linked entity references used on this page.
Shows how items, properties, identifiers, statements and references are represented in a structured knowledge base.
OPEN SOURCE →Defines ontology constructs including identity and equivalence semantics used in linked knowledge systems.
OPEN SOURCE →Provides a web vocabulary property for linking an entity to another URL that unambiguously indicates the same identity.
OPEN SOURCE →A graph can survive missing edges. It cannot survive confused identity.
Entity resolution protects the graph from fragmentation and false consolidation. When aliases, identifiers, attributes, relationships and provenance are evaluated together, references can converge on stable canonical entities without erasing meaningful distinctions.