2.3.2 Separating Claims, Observations, and Interpretations

Return to 2.3 The Role of SBRM

A central contribution of the Standard Business Report Model (SBRM) is the explicit separation of different kinds of facts that arise in reporting systems. Implementations frequently conflate these distinctions, but they remain architecturally critical [R11].

Reported facts

Assertions explicitly made by a reporting party in a filing or submission. Reported facts represent claims rather than conclusions and remain anchored to source artifacts, reporting obligations, and the reporting context in which the party made them.

Observed facts

Information produced when a system processes a reported artifact through parsing, tagging, optical character recognition, or another extraction technique. Observed facts represent what the system detects or extracts from the source artifact and may include uncertainty measures, confidence measures, extraction methods, and source-location information.

Interpreted or derived facts

Information produced by applying semantic definitions, rules, calculations, classifications, or contextual logic to reported or observed facts. Interpreted facts represent how a system understands information within a declared semantic and interpretive context, including applicable regulatory definitions, business rules, parameters, and version conditions.

These categories describe different relationships to authority and evidence:

• A reported fact records what a reporting party asserted
• An observed fact records what a processing component detected or extracted
• An interpreted fact records the result of applying governed meaning or logic

By making these distinctions explicit and traceable, SBRM enables systems to answer questions that would otherwise remain difficult or impossible to answer reliably:

• What did the reporting party actually report?
• What did the system observe or extract from the source artifact?
• What did the system infer, calculate, classify, or interpret?
• Which definitions, rules, parameters, or assumptions governed the interpretation?
• Which semantic version and interpretive context applied?
• How would the outcome differ under another semantic version or interpretive context?

This separation is essential for auditability, comparability, traceability, reinterpretation, and governance, particularly in environments where reporting requirements evolve and where artificial intelligence-assisted extraction and analysis increasingly contribute to information-processing pipelines [R10].

The Federated Data Interpretation Systems Reference Architecture (FDIS-RA) preserves these distinctions across architectural components and interfaces so that reported claims, extracted observations, and interpretive results do not collapse into a single opaque representation.