Triple

T14586373
Position Surface form Disambiguated ID Type / Status
Subject Yopougon E342325 entity
Predicate languageUsed P238 FINISHED
Object Dioula E162977 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: Dioula | Statement: [Yopougon, languageUsed, Dioula]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dioula
Context triple: [Yopougon, languageUsed, Dioula]
  • A. Dioula chosen
    Dioula is a Mande language of West Africa, widely used as a trade and lingua franca language in countries like Burkina Faso, Côte d’Ivoire, and Mali.
  • B. Bambara
    Bambara is a major Mande language widely spoken in Mali and neighboring West African countries, serving as a key lingua franca in the region.
  • C. Mandingo
    Mandingo is a controversial 1975 American film set on a Southern slave plantation, known for its graphic depiction of slavery, racism, and sexual exploitation.
  • D. Mandingo
    Mandingo is a Mande language spoken primarily by the Mandinka people across several West African countries, including Mali, Senegal, Gambia, and Guinea.
  • E. Dosso Zarma
    Dosso Zarma is a major regional variety of the Zarma language spoken primarily around the Dosso area of Niger.
  • 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_69d822ddc0f081909cd8163c7de298cd completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb421bb308190a457425429ef6aa5 completed April 14, 2026, 9:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd94bef27481908c108110dbf21780 completed May 8, 2026, 7:46 a.m.
Created at: April 10, 2026, 1:24 a.m.