Triple

T16728266
Position Surface form Disambiguated ID Type / Status
Subject Limuru Road E406519 entity
Predicate passesThrough P225 FINISHED
Object Gigiri E89552 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: Gigiri | Statement: [Limuru Road, passesThrough, Gigiri]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gigiri
Context triple: [Limuru Road, passesThrough, Gigiri]
  • A. Gigiri chosen
    Gigiri is an affluent diplomatic and residential district in Nairobi, Kenya, known for hosting major international institutions and embassies.
  • B. Meru
    Meru is a town in eastern Kenya that serves as a commercial and administrative hub for the surrounding agricultural region near Mount Kenya.
  • C. Karatu
    Karatu is a small town in northern Tanzania that serves as a popular gateway to the Ngorongoro Conservation Area and Serengeti National Park.
  • D. Kiliwa
    Kiliwa is an indigenous people of northern Baja California, Mexico, known for their distinct Yuman language and traditional hunter-gatherer culture.
  • E. Mount Ntringui
    Mount Ntringui is a volcanic peak on the island of Anjouan in the Comoros, known for its lush forests and prominence in the island’s rugged landscape.
  • 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_69d8838f242881908abd8bc138795886 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e38749baa48190892b2e2b978f6eb6 completed April 18, 2026, 1:29 p.m.
NED1 Entity disambiguation (via context triple) batch_6a009d483a8c8190b127f32dcc21be5a completed May 10, 2026, 2:59 p.m.
Created at: April 10, 2026, 5:20 a.m.