Triple

T8853221
Position Surface form Disambiguated ID Type / Status
Subject Centro Sur E210687 entity
Predicate borders P224 FINISHED
Object Wele-Nzas E210688 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: Wele-Nzas | Statement: [Centro Sur, borders, Wele-Nzas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wele-Nzas
Context triple: [Centro Sur, borders, Wele-Nzas]
  • A. Wele-Nzas chosen
    Wele-Nzas is a province in mainland Equatorial Guinea known for its forests, border location near Gabon and Cameroon, and the city of Mongomo.
  • B. Eyamba
    Eyamba is a prominent clan of the Efik people of southeastern Nigeria, historically associated with leadership and influence in the Old Calabar region.
  • C. Ndowe
    Ndowe is a Bantu language spoken by the Ndowe people along the coastal region of Equatorial Guinea.
  • D. Ewondo
    Ewondo is a Bantu language spoken primarily by the Ewondo people in central Cameroon, including in and around the capital city, Yaoundé.
  • E. Benina
    Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
  • 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_69ca838a424c8190b1ecac115c2927e7 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc60c55e348190957b3bbb7397e380 completed April 1, 2026, 12:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfab8911c8819083f5caa318071720 completed April 3, 2026, 11:59 a.m.
Created at: March 30, 2026, 6:49 p.m.