Triple

T10485839
Position Surface form Disambiguated ID Type / Status
Subject Faculty of Engineering, University of Nairobi E247293 entity
Predicate locatedIn P40 FINISHED
Object Nairobi, Kenya E6371 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: Nairobi, Kenya | Statement: [Faculty of Engineering, University of Nairobi, locatedIn, Nairobi, Kenya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nairobi, Kenya
Context triple: [Faculty of Engineering, University of Nairobi, locatedIn, Nairobi, Kenya]
  • A. Nairobi chosen
    Nairobi is the capital and largest city of Kenya, serving as a major political, economic, and cultural hub in East Africa.
  • B. Nairobi
    Nairobi is a fan-favorite character from the Spanish series "Money Heist," known for her sharp leadership, optimism, and expertise in overseeing the gang’s money-printing operations.
  • C. Kasarani, Nairobi
    Kasarani, Nairobi is a residential and commercial district in northeastern Nairobi known for its sports facilities, educational institutions, and growing urban development.
  • D. Kabete
    Kabete is a prominent town in Kenya’s Central Region, situated within Kiambu County and known for its agricultural activity and proximity to Nairobi.
  • E. Lipa City
    Lipa City is a highly urbanized city in Batangas, Philippines, known as a commercial, educational, and religious center in the Calabarzon region.
  • 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_69d381c309b88190af78aa681cf6a4c2 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d5096988ec81908d7518b09256c145 completed April 7, 2026, 1:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69d90dc44d7881908391487275b845bf completed April 10, 2026, 2:48 p.m.
Created at: April 6, 2026, 12:23 p.m.