Triple

T29235063
Position Surface form Disambiguated ID Type / Status
Subject Kasarani Constituency E741170 entity
Predicate contains P35 FINISHED
Object Kahawa West (historically / boundary area)
Kahawa West is a residential neighborhood in Nairobi, Kenya, known for its dense housing, vibrant local commerce, and proximity to major educational institutions.
E1855790 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: Kahawa West (historically / boundary area) | Statement: [Kasarani Constituency, contains, Kahawa West (historically / boundary area)]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Kahawa West (historically / boundary area)
Triple: [Kasarani Constituency, contains, Kahawa West (historically / boundary area)]
Generated description
Kahawa West is a residential neighborhood in Nairobi, Kenya, known for its dense housing, vibrant local commerce, and proximity to major educational institutions.

Provenance (5 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_69f0911dd6fc819097d1abb287016489 completed April 28, 2026, 10:51 a.m.
NER Named-entity recognition batch_69f6646309648190806dece783dd9e55 completed May 2, 2026, 8:53 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2569ddd3ec8190a4d5b70492bb3b99 completed June 7, 2026, 12:53 p.m.
NEDg Description generation batch_6a256e1a5a7481909bd3a9e3a719bba5 completed June 7, 2026, 1:11 p.m.
NED2 Entity disambiguation (via description) batch_6a25720109c481908ee70d2bbeb32403 completed June 7, 2026, 1:28 p.m.
Created at: April 28, 2026, 12:28 p.m.