Triple

T14998640
Position Surface form Disambiguated ID Type / Status
Subject Kajiado County E374024 entity
Predicate borders P224 FINISHED
Object Makueni County E893063 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: Makueni County | Statement: [Kajiado County, borders, Makueni County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Makueni County
Context triple: [Kajiado County, borders, Makueni County]
  • A. Makueni County chosen
    Makueni County is a semi-arid administrative region in southeastern Kenya known for its agriculture, water-scarcity challenges, and location along key transport and river basins.
  • B. Kisii County
    Kisii County is an administrative county in southwestern Kenya known for its fertile highlands, intensive agriculture, and vibrant Kisii (Abagusii) community.
  • C. Meru County
    Meru County is an administrative region in eastern Kenya known for its fertile highlands, agricultural production, and proximity to Mount Kenya.
  • D. Kirinyaga County
    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.
  • E. Laikipia County
    Laikipia County is a region in central Kenya known for its wildlife conservancies, ranches, and growing tourism and agricultural sectors.
  • 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_69d85ccc84388190aa151e5173370c8d completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded71a5618819083ae96a79735ef98 completed April 15, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69fea5aca8488190bf00bdbd7c4fb535 completed May 9, 2026, 3:10 a.m.
Created at: April 10, 2026, 2:54 a.m.