dido:01-fdis-ra:02-background-and-motivation:02-4-existing-functional-capabilities-in-reporting-systems:02-4-2-information-extraction-and-observation:start

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dido:01-fdis-ra:02-background-and-motivation:02-4-existing-functional-capabilities-in-reporting-systems:02-4-2-information-extraction-and-observation:start [2026/07/12 11:43] – created nick_didodido:01-fdis-ra:02-background-and-motivation:02-4-existing-functional-capabilities-in-reporting-systems:02-4-2-information-extraction-and-observation:start [2026/07/18 12:33] (current) – external edit 127.0.0.1
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 [[dido:01-fdis-ra:02-background-and-motivation:02-4-existing-functional-capabilities-in-reporting-systems:start|Return to 2.4 Existing Functional Capabilities in Reporting Systems]] [[dido:01-fdis-ra:02-background-and-motivation:02-4-existing-functional-capabilities-in-reporting-systems:start|Return to 2.4 Existing Functional Capabilities in Reporting Systems]]
  
-Reporting systems increasingly include a functional capability that associates extracted values or observations with provenance information. This provenance links each observation to specific source artefacts, document locations, fields, or extraction contexts and records how, where, and under which conditions the system derived the observed fact.+Reporting systems increasingly include a functional capability that associates extracted values or observations with provenance information. This provenance links each observation to specific source artifacts, document locations, fields, or extraction contexts and records how, where, and under which conditions the system derived the observed fact.
  
-At the architectural level, provenance is more than ancillary metadata. It provides the mechanism that connects observed facts to their evidentiary origins and enables traceability from interpreted results, through extraction processes, back to the original reported artefacts.+At the architectural level, provenance is more than ancillary metadata. It provides the mechanism that connects observed facts to their evidentiary origins and enables traceability from interpreted results, through extraction processes, back to the original reported artifacts.
  
 Provenance information may include: Provenance information may include:
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 * Distinguish extraction uncertainty from interpretive uncertainty * Distinguish extraction uncertainty from interpretive uncertainty
  
-From an architectural perspective, this functional component ensures that every observed fact remains explicitly traceable to its source and extraction context throughout the reporting lifecycle, including when observations cross system, organisational, or jurisdictional boundaries.+From an architectural perspective, this functional component ensures that every observed fact remains explicitly traceable to its source and extraction context throughout the reporting lifecycle, including when observations cross system, organizational, or jurisdictional boundaries.
  
 This responsibility includes: This responsibility includes:
  
   * Maintaining stable identifiers   * Maintaining stable identifiers
-  * Preserving links to source artefacts and source locations+  * Preserving links to source artifacts and source locations
   * Retaining extraction context and processing history   * Retaining extraction context and processing history
   * Preserving linkage across semantic versions   * Preserving linkage across semantic versions
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 By treating observations with provenance as a distinct functional capability, the [[dido:99_annexes:annex-b-terms-and-definitions:r:reference_architecture|Reference Architecture]] establishes traceability as a governed architectural property rather than as an afterthought or tool-specific feature. By treating observations with provenance as a distinct functional capability, the [[dido:99_annexes:annex-b-terms-and-definitions:r:reference_architecture|Reference Architecture]] establishes traceability as a governed architectural property rather than as an afterthought or tool-specific feature.
  
-This separation supports auditability, reproducibility, controlled semantic evolution, and responsible use of artificial intelligence-assisted extraction techniques, particularly in environments where interpretations may change while the underlying reported artefacts remain fixed [[dido:99_annexes:annex-c-references:r10-reference-architecture-description|[R10]]], [[dido:99_annexes:annex-c-references:r11-standard-business-report-model-specification|[R11]]].+This separation supports auditability, reproducibility, controlled semantic evolution, and responsible use of artificial intelligence-assisted extraction techniques, particularly in environments where interpretations may change while the underlying reported artifacts remain fixed [[dido:99_annexes:annex-c-references:r10-reference-architecture-description|[R10]]], [[dido:99_annexes:annex-c-references:r11-standard-business-report-model-specification|[R11]]].
  
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  • Last modified: 2026/07/12 11:43
  • by nick_dido