Triple

T4077113
Position Surface form Disambiguated ID Type / Status
Subject Global Forest Resources Assessment E87389 entity
Predicate notableEdition P3094 FINISHED
Object FRA 2010
FRA 2010 is a major global assessment report that provides comprehensive data and analysis on the world’s forest resources and their changes around 2010.
 E411517 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: FRA 2010 | Statement: [Global Forest Resources Assessment, notableEdition, FRA 2010]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: FRA 2010
Context triple: [Global Forest Resources Assessment, notableEdition, FRA 2010]
  • A. FRA
    FRA is the standard abbreviation used to refer to the Royal Moroccan Air Force, the aerial warfare branch of Morocco’s armed forces.
  • B. FRA
    FRA is the three-letter ISO 3166-1 alpha-3 country code that uniquely identifies France in international standards and data systems.
  • C. FRA
    FRA is the United States government agency responsible for regulating and overseeing the nation’s railroad safety, infrastructure, and operations.
  • D. FR3
    FR3 was a former French public television channel and network that later became part of France Télévisions.
  • E. FRIBA
    FRIBA is a professional honorific indicating fellowship in the Royal Institute of British Architects.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: FRA 2010
Triple: [Global Forest Resources Assessment, notableEdition, FRA 2010]
Generated description
FRA 2010 is a major global assessment report that provides comprehensive data and analysis on the world’s forest resources and their changes around 2010.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: FRA 2010
Target entity description: FRA 2010 is a major global assessment report that provides comprehensive data and analysis on the world’s forest resources and their changes around 2010.
  • A. FRA
    FRA is the standard abbreviation used to refer to the Royal Moroccan Air Force, the aerial warfare branch of Morocco’s armed forces.
  • B. FRA
    FRA is the three-letter ISO 3166-1 alpha-3 country code that uniquely identifies France in international standards and data systems.
  • C. FRA
    FRA is the United States government agency responsible for regulating and overseeing the nation’s railroad safety, infrastructure, and operations.
  • D. FR3
    FR3 was a former French public television channel and network that later became part of France Télévisions.
  • E. FRIBA
    FRIBA is a professional honorific indicating fellowship in the Royal Institute of British Architects.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69aed9435cf48190ad1da737c962d19d completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefc4d348c8190a94724639830aca0 completed March 9, 2026, 4:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69b562bea9b48190bcd1396c0cb19697 completed March 14, 2026, 1:29 p.m.
NEDg Description generation batch_69b563b5cc108190bb9684abafa608af completed March 14, 2026, 1:33 p.m.
NED2 Entity disambiguation (via description) batch_69b5646606f08190930451ac372154cd completed March 14, 2026, 1:36 p.m.
Created at: March 9, 2026, 3:39 p.m.