Triple

T17200175
Position Surface form Disambiguated ID Type / Status
Subject Government of Cameroon E417453 entity
Predicate seat P75 FINISHED
Object Yaoundé E129885 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: Yaoundé | Statement: [Government of Cameroon, seat, Yaoundé]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yaoundé
Context triple: [Government of Cameroon, seat, Yaoundé]
  • A. Yaoundé chosen
    Yaoundé is the political and administrative center of Cameroon, known for its hilly terrain and role as a major cultural and economic hub in Central Africa.
  • B. Douala
    Douala is the economic capital and main port city of Cameroon, located on the Wouri River along the Atlantic coast.
  • C. Ouaga
    Ouaga is the commonly used short name for Ouagadougou, the capital and largest city of Burkina Faso.
  • D. Libreville
    Libreville is the largest city and main economic and cultural center of Gabon, located on the country’s Atlantic coast.
  • E. Kumbo
    Kumbo is a major town in Cameroon's Anglophone Grassfields, known as an important cultural, religious, and commercial center of the Northwest Region.
  • 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_69d886d6ba8c819093215917b3d01689 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e42daddcd08190a82f36c940bf3f7b completed April 19, 2026, 1:19 a.m.
NED1 Entity disambiguation (via context triple) batch_6a018c38b1ec819092a551e2683a4b93 completed May 11, 2026, 7:58 a.m.
Created at: April 10, 2026, 5:38 a.m.