Triple

T21319846
Position Surface form Disambiguated ID Type / Status
Subject Segeju people E525581 entity
Predicate ethnicMinorityIn P4710 FINISHED
Object Kenya NE NERFINISHED

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: Kenya | Statement: [Segeju people, ethnicMinorityIn, Kenya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kenya
Context triple: [Segeju people, ethnicMinorityIn, Kenya]
  • A. Kenya chosen
    Kenya is an East African country known for its diverse wildlife, scenic landscapes from savannas to highlands, and a coastline along the Indian Ocean.
  • B. Nyeri, Kenya
    Nyeri, Kenya is a town in central Kenya near the Aberdare Range, known for its colonial-era history, agricultural economy, and as the final resting place of Scouting founder Robert Baden-Powell.
  • C. Kamba (Kenya)
    Kamba (Kenya) is a Bantu language spoken primarily by the Kamba people in eastern Kenya.
  • D. Uganda
    Uganda is a landlocked country in East Africa known for its diverse landscapes, abundant wildlife, and location along the equator.
  • E. Maasailand
    Maasailand is a cultural region in East Africa traditionally inhabited by the Maasai people, spanning parts of southern Kenya and northern Tanzania.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b51ad810819098c12392c8e55f6c completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e77ecfb7d481909cb73f95c3ddd9ed completed April 21, 2026, 1:42 p.m.
Created at: April 16, 2026, 4:39 p.m.