Triple

T4132702
Position Surface form Disambiguated ID Type / Status
Subject French Ubangi-Shari E85076 entity
Predicate colonialAdministrationCenter P1474 FINISHED
Object Bangui E148403 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: Bangui | Statement: [French Ubangi-Shari, colonialAdministrationCenter, Bangui]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bangui
Context triple: [French Ubangi-Shari, colonialAdministrationCenter, Bangui]
  • A. Bangui chosen
    Bangui is the capital and largest city of the Central African Republic, serving as its political, economic, and cultural center.
  • B. Bamenda
    Bamenda is a prominent city in northwestern Cameroon known as a cultural and commercial hub of the Anglophone region.
  • C. Ouaga
    Ouaga is the commonly used short name for Ouagadougou, the capital and largest city of Burkina Faso.
  • D. N'Djamena
    N'Djamena is the largest city and political, economic, and cultural center of Chad, located in the southwestern part of the country near the border with Cameroon.
  • E. Bunia
    Bunia is a city in northeastern Democratic Republic of the Congo that has been a focal point of regional conflict and international peacekeeping efforts.
  • 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_69aed935ccd881909dc61f81bcdb7a78 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af022f55fc81909f2a1a04d0ea59e6 completed March 9, 2026, 5:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69b6509a86a081908435094d0e80e9f4 completed March 15, 2026, 6:24 a.m.
Created at: March 9, 2026, 3:42 p.m.