Triple

T22348692
Position Surface form Disambiguated ID Type / Status
Subject FIBA Africa Zone 3 E552467 entity
Predicate memberCountry P1138 FINISHED
Object Guinea NE NERFINISHED

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: [FIBA Africa Zone 3, memberCountry, Guinea]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Guinea
Context triple: [FIBA Africa Zone 3, memberCountry, 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. La Guinea
    La Guinea is a small settlement located on Isla del Rey in Spain’s Balearic Islands.
  • C. Gaboni
    Gaboni is a small locality in southern Poland situated near the Beskid Sądecki mountain range, serving as a starting point for hikes to nearby peaks such as Przehyba.
  • D. Gabon
    Gabon is a Central African country on the Atlantic coast, known for its equatorial rainforests, rich biodiversity, and significant oil reserves.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e4a0ad08190a385b4d343cf6524 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f1579a1c308190ae2174f99ae317ab completed April 29, 2026, 12:58 a.m.
Created at: April 16, 2026, 8:43 p.m.