Triple

T12390368
Position Surface form Disambiguated ID Type / Status
Subject Palo Mayombe E295976 entity
Predicate hasKeyFigure P810 FINISHED
Object tata nganga E295980 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: tata nganga | Statement: [Palo Mayombe, hasKeyFigure, tata nganga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: tata nganga
Context triple: [Palo Mayombe, hasKeyFigure, tata nganga]
  • A. tata nganga chosen
    Tata nganga is a high-ranking priest and ritual specialist in the Afro-Cuban Palo Monte religion, responsible for leading ceremonies, divination, and work with spiritual forces.
  • B. Bangangté
    Bangangté is a prominent city in western Cameroon known as an important administrative and commercial center of the West Region.
  • C. Bobangi
    Bobangi is a Bantu language historically spoken along the Congo River that served as a major regional trade lingua franca in Central Africa.
  • D. Tengatangi
    Tengatangi is a small village settlement located on the island of Atiu in the Cook Islands.
  • E. Tigak
    Tigak is an Austronesian language of the Meso-Melanesian subgroup spoken primarily in parts of Papua New Guinea.
  • 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_69d6ad9e653c8190b1473c860ee53dae completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d93fd0bcc48190bb1a59a3aaa6bfdf completed April 10, 2026, 6:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69f63479df38819085c5ca791c460d5e completed May 2, 2026, 5:29 p.m.
Created at: April 8, 2026, 9:54 p.m.