Triple

T4077087
Position Surface form Disambiguated ID Type / Status
Subject Global Forest Resources Assessment E87389 entity
Predicate acronym P43 FINISHED
Object FRA
FRA is the acronym for the Global Forest Resources Assessment, a periodic FAO-led study that evaluates the state and trends of the world’s forests.
 E411512 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 | Statement: [Global Forest Resources Assessment, acronym, FRA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: FRA
Context triple: [Global Forest Resources Assessment, acronym, FRA]
  • A. FRA
    FRA is the three-letter ISO 3166-1 alpha-3 country code that uniquely identifies France in international standards and data systems.
  • B. FRA
    FRA is the United States government agency responsible for regulating and overseeing the nation’s railroad safety, infrastructure, and operations.
  • C. FRA
    FRA is the standard abbreviation used to refer to the Royal Moroccan Air Force, the aerial warfare branch of Morocco’s armed forces.
  • D. FRAS
    FRAS is a professional post-nominal title indicating fellowship in the Royal Astronomical Society, typically awarded to individuals who have made significant contributions to astronomy or geophysics.
  • E. FRD
    FRD is the station code for a railway station named after U.S. President Franklin D. Roosevelt.
  • 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
Triple: [Global Forest Resources Assessment, acronym, FRA]
Generated description
FRA is the acronym for the Global Forest Resources Assessment, a periodic FAO-led study that evaluates the state and trends of the world’s forests.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: FRA
Target entity description: FRA is the acronym for the Global Forest Resources Assessment, a periodic FAO-led study that evaluates the state and trends of the world’s forests.
  • A. FRA
    FRA is the three-letter ISO 3166-1 alpha-3 country code that uniquely identifies France in international standards and data systems.
  • B. FRA
    FRA is the United States government agency responsible for regulating and overseeing the nation’s railroad safety, infrastructure, and operations.
  • C. FRA
    FRA is the standard abbreviation used to refer to the Royal Moroccan Air Force, the aerial warfare branch of Morocco’s armed forces.
  • D. FRAS
    FRAS is a professional post-nominal title indicating fellowship in the Royal Astronomical Society, typically awarded to individuals who have made significant contributions to astronomy or geophysics.
  • E. FRD
    FRD is the station code for a railway station named after U.S. President Franklin D. Roosevelt.
  • 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.