Triple

T11275794
Position Surface form Disambiguated ID Type / Status
Subject MASS Design Group E266932 entity
Predicate officeLocation P40 FINISHED
Object Kigali, Rwanda E87281 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: Kigali, Rwanda | Statement: [MASS Design Group, officeLocation, Kigali, Rwanda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kigali, Rwanda
Context triple: [MASS Design Group, officeLocation, 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 (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_69d6aac8c2f48190ad0596f1f89f0470 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e966cb4c8190bc410d7e623e54db completed April 9, 2026, 6:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69e4f445792c8190962d94aa71f328d9 completed April 19, 2026, 3:27 p.m.
Created at: April 8, 2026, 9:31 p.m.