Triple

T10021236
Position Surface form Disambiguated ID Type / Status
Subject Kitengela dispersal area E200612 entity
Predicate adjacentTo P224 FINISHED
Object Kitengela town E773373 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: Kitengela town | Statement: [Kitengela dispersal area, adjacentTo, Kitengela town]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kitengela town
Context triple: [Kitengela dispersal area, adjacentTo, Kitengela town]
  • A. Kitengela chosen
    Kitengela is a rapidly growing commuter town in Kenya known for its residential estates, industrial development, and proximity to Nairobi.
  • B. Chimanimani town
    Chimanimani town is a small mountainous settlement in southeastern Zimbabwe known as a gateway to the scenic Chimanimani Mountains and surrounding national park.
  • C. Itezhi-Tezhi town
    Itezhi-Tezhi town is a settlement in Zambia that developed near the Itezhi-Tezhi Dam and serves as a local center for services and access to the surrounding rural and wildlife areas.
  • D. Limuru
    Limuru is a highland town in central Kenya known for its cool climate, tea plantations, and proximity to Nairobi.
  • E. Kalangoya
    Kalangoya is an alternative name for the Kalanguya language, an Austronesian language spoken by indigenous communities in the northern Philippines.
  • 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_69ca831c45f08190ac1505cc15076608 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cdcd79485881909df562bfff36ccf4 completed April 2, 2026, 1:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69d26ab241848190a97dea745f6d2324 completed April 5, 2026, 1:59 p.m.
Created at: March 30, 2026, 8:53 p.m.