Triple

T9259247
Position Surface form Disambiguated ID Type / Status
Subject Mooré E222528 entity
Predicate dialectRegion P1762 FINISHED
Object Ouagadougou area E69602 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: Ouagadougou area | Statement: [Mooré, dialectRegion, Ouagadougou area]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ouagadougou area
Context triple: [Mooré, dialectRegion, Ouagadougou area]
  • A. Ouagadougou chosen
    Ouagadougou is the capital and largest city of Burkina Faso, serving as its political, economic, and cultural center in the Sahel region.
  • B. Bobo-Dioulasso
    Bobo-Dioulasso is the second-largest city of Burkina Faso, known as a major economic and cultural center in the country’s southwest.
  • C. Bamako District
    Bamako District is the capital district of Mali, encompassing the city of Bamako as a separate administrative entity from the surrounding regions.
  • D. Koudougou
    Koudougou is a major city in central Burkina Faso known as an important commercial and transportation hub.
  • E. Sikasso
    Sikasso is a major city in southern Mali known as an important agricultural and commercial center near the borders with Burkina Faso and Côte d'Ivoire.
  • 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_69ca841e4cd481908e738c74e958eaea completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd0714317481908405f857a4f49e74 completed April 1, 2026, 11:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0e361a00881909b431dba0db978fd completed April 4, 2026, 10:09 a.m.
Created at: March 30, 2026, 7:32 p.m.