Triple

T1764495
Position Surface form Disambiguated ID Type / Status
Subject French Community E38730 entity
Predicate memberState P10 FINISHED
Object Guinea E40812 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: Guinea | Statement: [French Community, memberState, Guinea]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Guinea
Context triple: [French Community, memberState, Guinea]
  • A. Guinea chosen
    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.
  • B. Gabon
    Gabon is a Central African country on the Atlantic coast, known for its equatorial rainforests, rich biodiversity, and significant oil reserves.
  • C. The Gambia
    The Gambia is a small West African country centered around the Gambia River, known for its diverse ecosystems, colonial history, and tourism-focused economy.
  • D. Senegal
    Senegal is a West African country on the Atlantic coast known for its vibrant culture, historic role in transatlantic trade, and diverse coastal and Sahelian landscapes.
  • E. Mali
    Mali is a landlocked West African country known for its historic trading cities like Timbuktu, rich Sahelian culture, and significant role in the ancient Mali Empire.
  • 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_69a8862d562481908d7025a1c1f67c0d completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa646665088190afa31bdf48f14316 completed March 6, 2026, 5:21 a.m.
NED1 Entity disambiguation (via context triple) batch_69af8336f98c8190949c0145d2d31a8f completed March 10, 2026, 2:34 a.m.
Created at: March 4, 2026, 7:31 p.m.