Triple

T6435704
Position Surface form Disambiguated ID Type / Status
Subject German Kamerun E129888 entity
Predicate capital P234 FINISHED
Object Buea E445155 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: Buea | Statement: [German Kamerun, capital, Buea]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Buea
Context triple: [German Kamerun, capital, Buea]
  • A. Buea chosen
    Buea is a town in southwestern Cameroon, historically significant as an administrative center during colonial rule and today known as a regional capital near Mount Cameroon.
  • B. Bamenda
    Bamenda is a prominent city in northwestern Cameroon known as a cultural and commercial hub of the Anglophone region.
  • C. Ngaoundéré
    Ngaoundéré is a major city in northern Cameroon that serves as the regional capital of Adamawa and an important commercial and transport hub between central and northern Africa.
  • D. Bangui
    Bangui is the capital and largest city of the Central African Republic, serving as its political, economic, and cultural center.
  • E. Yaoundé
    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.
  • 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_69c0084caac48190a7bc2ad8ba44536f completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c069415c3c8190b91bd12ae79edd26 completed March 22, 2026, 10:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6637ffa648190b39e9721c9a7c092 completed March 27, 2026, 11:01 a.m.
Created at: March 22, 2026, 4:45 p.m.