Entity Resolution Framework

An Entity Resolution Framework is the set of rules, models, and workflows used to deduplicate, merge, and maintain identities across messy real-world data. In allocator intelligence, the hardest part isn’t collecting information—it’s ensuring that “Dawson Partners,” “Dawson Capital,” and “Dawson Partners LLC” are either correctly unified or correctly separated, and that identities stay stable when names, titles, or structures change.

From an intelligence perspective, entity resolution is not a one-time cleanup. It is an ongoing governance system: every new source introduces collisions, duplicates, and ambiguity. If identity is wrong, everything downstream is wrong—search, counts, signals, coverage claims, and relationship graphs.

How teams define entity resolution risk drivers

Teams evaluate entity resolution through:

• Canonical entity definition: what counts as an entity (firm vs fund vs vehicle)
• Identifier strategy: deterministic IDs (registrations, domains, filings) vs probabilistic matching
• Alias and synonym management: DBA names, translations, abbreviations, brand variants
• Parent-child structure logic: firm ↔ fund ↔ GP ↔ management company ↔ affiliates
• Match confidence scoring: thresholds for auto-merge vs human review
• Collision handling: same-name entities and ambiguous matches
• Survivorship rules: what fields “win” when merging conflicting data

Allocator framing:
“Can the platform prove identity accuracy—or are relationship insights built on duplicate and conflated entities?”

Where entity resolution matters most

• family office and LP datasets where naming is inconsistent
• multi-entity platforms (GP, management company, vehicles, SPVs)
• global coverage with language and jurisdiction variation
• relationship graph features (warm intro mapping, network inference)

How entity resolution changes outcomes

Strong entity resolution discipline:

• reduces duplicate outreach and brand damage
• increases confidence in counts and filters (AUM, geography, strategy)
• makes relationship graphs usable and defensible
• improves downstream verification and change monitoring

Weak entity resolution discipline:

• produces false connections and broken graphs
• inflates coverage counts and damages credibility
• creates inconsistent search results (“same firm appears three times”)
• makes audits and corrections unscalable

How teams evaluate resolution discipline

Confidence increases when systems:

• use layered matching (deterministic → probabilistic → review)
• maintain explainable match logic and confidence scoring
• preserve provenance and reversible merges
• support continuous re-resolution as new evidence arrives

What slows decision-making and adoption

• opaque “AI merge” behavior with no explainability
• inability to undo merges or view merge rationale
• high duplicate rates visible in UI
• inconsistent entity hierarchies across modules

Common misconceptions

• “Deduping is a cleanup task” → it’s a living identity system.
• “One identifier solves it” → identifiers are often missing or reused.
• “Graphs fix resolution” → graphs amplify identity errors.

Key questions during diligence

• What identifiers do you use for deterministic matching?
• How do you handle same-name entities across jurisdictions?
• What is the confidence threshold for auto-merge vs review?
• Can merges be audited and reversed?
• How do you maintain parent-child relationships over time?