Triple

T8691235
Position Surface form Disambiguated ID Type / Status
Subject Kasarani E206293 entity
Predicate partOf P40 FINISHED
Object Nairobi Metropolitan Area E216466 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 Metropolitan Area | Statement: [Kasarani, partOf, Nairobi Metropolitan Area]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nairobi Metropolitan Area
Context triple: [Kasarani, partOf, Nairobi Metropolitan Area]
  • A. Nairobi Metropolitan Region chosen
    Nairobi Metropolitan Region is the expansive urban and economic area centered on Kenya’s capital, Nairobi, encompassing the city and its surrounding counties and towns.
  • B. Nairobi
    Nairobi is the capital and largest city of Kenya, serving as a major political, economic, and cultural hub in East Africa.
  • C. 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.
  • D. Lipa City
    Lipa City is a highly urbanized city in Batangas, Philippines, known as a commercial, educational, and religious center in the Calabarzon region.
  • E. Nairobi West
    Nairobi West is a residential and commercial neighborhood in Nairobi, Kenya, known for its proximity to the city center and mixed middle-income housing.
  • 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_69ca835481fc819084e33d3bc883bfa6 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5735ffdc819094126e2698f98511 completed March 31, 2026, 11:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69cfeafd26f4819092f5adc1ac70148f completed April 3, 2026, 4:29 p.m.
Created at: March 30, 2026, 6:33 p.m.