Triple

T7302601
Position Surface form Disambiguated ID Type / Status
Subject Moi International Airport E167893 entity
Predicate locatedIn P40 FINISHED
Object Mombasa E47207 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: Mombasa | Statement: [Moi International Airport, locatedIn, Mombasa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mombasa
Context triple: [Moi International Airport, locatedIn, 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. 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.
  • C. Dar es Salaam
    Dar es Salaam is a major coastal metropolis on the Indian Ocean and the principal economic and commercial hub of Tanzania.
  • D. Zanzibar City
    Zanzibar City is the historic and administrative capital of Zanzibar, Tanzania, renowned for its UNESCO-listed Stone Town and rich Swahili, Arab, and colonial heritage.
  • E. Nairobi
    Nairobi is the capital and largest city of Kenya, serving as a major political, economic, and cultural hub in East Africa.
  • 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_69c6888c820881909fc68f689fe1c251 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6ebb2261c8190ae9095c8e110b528 completed March 27, 2026, 8:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7fa7320bc819081e0b94e8909c4aa completed March 28, 2026, 3:57 p.m.
Created at: March 27, 2026, 3:01 p.m.