Triple

T14815620
Position Surface form Disambiguated ID Type / Status
Subject Embu people E348305 entity
Predicate primaryResidence P75 FINISHED
Object Kirinyaga County E721904 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: Kirinyaga County | Statement: [Embu people, primaryResidence, Kirinyaga County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kirinyaga County
Context triple: [Embu people, primaryResidence, Kirinyaga County]
  • A. Kirinyaga County chosen
    Kirinyaga County is an administrative region in central Kenya known for its fertile agricultural land on the slopes of Mount Kenya and its production of tea, coffee, and horticultural crops.
  • B. Nyandarua County
    Nyandarua County is an administrative region in central Kenya known for its highland agriculture and proximity to the Aberdare Range.
  • C. Laikipia County
    Laikipia County is a region in central Kenya known for its wildlife conservancies, ranches, and growing tourism and agricultural sectors.
  • D. Nyamira County
    Nyamira County is an administrative county in western Kenya known for its predominantly Kisii community, hilly highland terrain, and tea and coffee farming.
  • E. Nakuru County
    Nakuru County is a region in Kenya’s Rift Valley known for its lakes, wildlife, and agricultural activities.
  • 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_69d822eb8f588190bf53445e730a934f completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69decfe0e89c81908c0e1fe2bc3ebcfc completed April 14, 2026, 11:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe6b47389c8190ab0a46e3b4653a2a completed May 8, 2026, 11:01 p.m.
Created at: April 10, 2026, 1:49 a.m.