Triple

T3837813
Position Surface form Disambiguated ID Type / Status
Subject Ewondo language E91177 entity
Predicate hasGlottologName P6521 FINISHED
Object Ewondo E138978 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: Ewondo | Statement: [Ewondo language, hasGlottologName, Ewondo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ewondo
Context triple: [Ewondo language, hasGlottologName, Ewondo]
  • A. Ewondo chosen
    Ewondo is a Bantu language spoken primarily by the Ewondo people in central Cameroon, including in and around the capital city, Yaoundé.
  • B. Benina
    Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
  • C. Wele-Nzas
    Wele-Nzas is a province in mainland Equatorial Guinea known for its forests, border location near Gabon and Cameroon, and the city of Mongomo.
  • D. Eyamba
    Eyamba is a prominent clan of the Efik people of southeastern Nigeria, historically associated with leadership and influence in the Old Calabar region.
  • E. Akwanga
    Akwanga is a town and administrative center in central Nigeria known for its role as a commercial and educational hub in Nasarawa State.
  • 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_69aed960b538819096561c8ed448dec9 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeeb9d11f081909fc51e84657ec7f1 completed March 9, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b51228a7a48190bc42898be15b450b completed March 14, 2026, 7:45 a.m.
Created at: March 9, 2026, 3:18 p.m.