Triple

T17056021
Position Surface form Disambiguated ID Type / Status
Subject Diass E413820 entity
Predicate locatedInAdministrativeTerritory P40 FINISHED
Object Mbour Department E1084446 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: Mbour Department | Statement: [Diass, locatedInAdministrativeTerritory, Mbour Department]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mbour Department
Context triple: [Diass, locatedInAdministrativeTerritory, Mbour Department]
  • A. Mbour Department chosen
    Mbour Department is an administrative division in Senegal’s Thiès Region, known for its coastal towns, fishing activities, and tourism along the Petite Côte.
  • B. Mouloundou Department
    Mouloundou Department is an administrative division located in southeastern Gabon within Ogooué-Lolo Province.
  • C. Bénoué Department
    Bénoué Department is an administrative division in northern Cameroon known for its capital Garoua and proximity to the Bénoué River and Bénoué National Park.
  • D. Lékoumou Department
    Lékoumou Department is an administrative region in the Republic of the Congo located in the southern part of the country.
  • E. Guédiawaye Department
    Guédiawaye Department is an administrative division in western Senegal that forms part of the greater Dakar metropolitan area.
  • 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_69d886cde3d481908d4d01ba88ba7eb7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3db7934e881909e9f0ae956fb0816 completed April 18, 2026, 7:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a012346bcfc819093cd922baa94e186 completed May 11, 2026, 12:31 a.m.
Created at: April 10, 2026, 5:34 a.m.