Triple

T341420
Position Surface form Disambiguated ID Type / Status
Subject OPEC E6844 entity
Predicate formerMemberState P1168 FINISHED
Object Gabon E8377 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: Gabon | Statement: [OPEC, formerMemberState, Gabon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gabon
Context triple: [OPEC, formerMemberState, Gabon]
  • A. Gabon chosen
    Gabon is a Central African country on the Atlantic coast, known for its equatorial rainforests, rich biodiversity, and significant oil reserves.
  • B. Equatorial Guinea
    Equatorial Guinea is a small Central African country on the Atlantic coast, known for its significant oil reserves and unique status as the only African nation where Spanish is an official language.
  • C. Cameroon
    Cameroon is a Central African country known for its cultural and linguistic diversity, varied geography from coast to rainforest and savanna, and a mixed French-English colonial heritage.
  • D. Benin
    Benin is a West African country on the Gulf of Guinea known for its historical Kingdom of Dahomey and as a key region in the transatlantic slave trade.
  • E. Guinea
    Guinea is a West African country on the Atlantic coast known for its rich mineral resources, diverse ethnic groups, and role as a major producer of bauxite.
  • 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_69a2e7951ba08190960e90823b5078f3 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2ee043fac8190af72291c04761687 completed Feb. 28, 2026, 1:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69a58030d0e48190a6390478e62f8659 completed March 2, 2026, 12:18 p.m.
Created at: Feb. 28, 2026, 1:08 p.m.