Triple

T21612427
Position Surface form Disambiguated ID Type / Status
Subject Anglican Church of Kenya E533344 entity
Predicate hasDiocesesIn P1774 FINISHED
Object Mombasa 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: Mombasa | Statement: [Anglican Church of Kenya, hasDiocesesIn, Mombasa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mombasa
Context triple: [Anglican Church of Kenya, hasDiocesesIn, Mombasa]
  • A. Mombasa chosen
    Mombasa is a major coastal city in Kenya known as a key regional port and historic trading hub on the Indian Ocean.
  • B. Kilifi
    Kilifi is a coastal town in southeastern Kenya known for its beaches along the Indian Ocean and its role as an administrative and commercial center.
  • C. Malindi
    Malindi is a historic coastal town in southeastern Kenya known for its beaches, Swahili culture, and role as a former trading port on the Indian Ocean.
  • D. Dar es Salaam
    Dar es Salaam is a major coastal metropolis on the Indian Ocean and the principal economic and commercial hub of Tanzania.
  • E. Port of Mombasa
    The Port of Mombasa is Kenya’s largest and busiest seaport, serving as a key gateway for maritime trade in East and Central Africa.
  • 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_69e0c46411108190bba0d4176dffc9f3 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef3ba79424819094e9ee93c4bbcc0b completed April 27, 2026, 10:34 a.m.
Created at: April 16, 2026, 6:33 p.m.