Triple

T16964694
Position Surface form Disambiguated ID Type / Status
Subject Global Forest Resources Assessment E411512 entity
Predicate abbreviation P43 FINISHED
Object FRA E411512 NE FINISHED

How this triple was built (2 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, abbreviation, FRA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: FRA
Context triple: [Global Forest Resources Assessment, abbreviation, 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. FRA chosen
    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.
  • E. FRA
    FRA is the three-letter IATA airport code for Frankfurt Airport, one of Europe’s busiest international aviation hubs located in Frankfurt, Germany.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d886c9c9d481909afe222093641cae completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d0a2cda88190bd574a869f0e43e9 completed April 18, 2026, 6:42 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00d46cb56481908c2bc6648a12fbcf completed May 10, 2026, 6:54 p.m.
Created at: April 10, 2026, 5:31 a.m.