Triple

T15985455
Position Surface form Disambiguated ID Type / Status
Subject Nakuru County E387680 entity
Predicate hasSettlement P1068 FINISHED
Object Nakuru E85650 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: Nakuru | Statement: [Nakuru County, hasSettlement, Nakuru]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nakuru
Context triple: [Nakuru County, hasSettlement, Nakuru]
  • A. Nakuru chosen
    Nakuru is a prominent Kenyan city in the Rift Valley region, known for its proximity to Lake Nakuru National Park and its role as an important agricultural and commercial center.
  • B. Kajiado
    Kajiado is a town in southern Kenya that serves as an administrative and commercial center for the surrounding Maasai-inhabited region.
  • C. Kisumu
    Kisumu is a major Kenyan city on the shores of Lake Victoria, serving as a key commercial and transport hub in western Kenya.
  • D. Nyamira
    Nyamira is a town in western Kenya that serves as an administrative and commercial center in the former Nyanza region.
  • E. Kapsabet
    Kapsabet is a town in western Kenya known as an administrative and commercial center in a highland farming region and as a training base for elite long-distance runners.
  • 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_69d86daa562c81908aacc179c0fe8fb5 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e157589d78819091f7b9b1081dd6ad completed April 16, 2026, 9:40 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00179fc28481909e1c46af343676ff completed May 10, 2026, 5:29 a.m.
Created at: April 10, 2026, 4:54 a.m.