Triple

T19104926
Position Surface form Disambiguated ID Type / Status
Subject Paul Rusesabagina E467627 entity
Predicate placeOfArrest P6464 FINISHED
Object Kigali, Rwanda 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, Rwanda | Statement: [Paul Rusesabagina, placeOfArrest, Kigali, Rwanda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kigali, Rwanda
Context triple: [Paul Rusesabagina, placeOfArrest, Kigali, Rwanda]
  • A. Kigali chosen
    Kigali is the capital and largest city of Rwanda, known as a major political and economic hub in East Africa.
  • B. Butaro, Rwanda
    Butaro, Rwanda is a rural town in northern Rwanda known for its innovative, community-focused health facilities and scenic volcanic landscapes.
  • 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. Bujumbura
    Bujumbura is the largest city and former capital of Burundi, located on the northeastern shore of Lake Tanganyika.
  • 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_69d8dd05ac4c8190b1967d8f97f3fb2f completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5e3704c688190b84ef82da45d0862 completed April 20, 2026, 8:27 a.m.
Created at: April 10, 2026, 12:04 p.m.