Triple

T22584613
Position Surface form Disambiguated ID Type / Status
Subject Minister of Defence of Rwanda E564754 entity
Predicate seat P75 FINISHED
Object Kigali NE NERFINISHED

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: Kigali | Statement: [Minister of Defence of Rwanda, seat, Kigali]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kigali
Context triple: [Minister of Defence of Rwanda, seat, Kigali]
  • A. Kigali chosen
    Kigali is the capital and largest city of Rwanda, known as a major political and economic hub in East Africa.
  • B. Bujumbura
    Bujumbura is the largest city and former capital of Burundi, located on the northeastern shore of Lake Tanganyika.
  • C. Gisenyi
    Gisenyi is a city in northwestern Rwanda on the shores of Lake Kivu, historically significant as one of the key sites affected during the 1994 Rwandan genocide.
  • D. Bukavu
    Bukavu is a major city in the eastern Democratic Republic of the Congo, located on the southwestern shore of Lake Kivu near the Rwandan border.
  • E. Gitega
    Gitega is the political and administrative capital city of Burundi, located in the central part of the country.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e245836014819091b91ed3074742a3 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f1615b4fa08190a8d2d66e01db429f completed April 29, 2026, 1:39 a.m.
Created at: April 17, 2026, 2:44 p.m.