Triple

T38466682
Position Surface form Disambiguated ID Type / Status
Subject Ahero E912586 entity
Predicate hasTransportConnection P845 FINISHED
Object Nairobi–Kisumu highway
The Nairobi–Kisumu highway is a major road in Kenya that links the capital city Nairobi with the western city of Kisumu, serving as a key corridor for regional trade and travel.
 E2271063 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–Kisumu highway | Statement: [Ahero, hasTransportConnection, Nairobi–Kisumu highway]
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: Nairobi–Kisumu highway
Triple: [Ahero, hasTransportConnection, Nairobi–Kisumu highway]
Generated description
The Nairobi–Kisumu highway is a major road in Kenya that links the capital city Nairobi with the western city of Kisumu, serving as a key corridor for regional trade and travel.

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_69f76e861d8c81908559031dc66e3c15 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fcd1facbcc8190af0faa49f68f6904 completed May 7, 2026, 5:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41ccb8f2248190ac41e04f300ce221 completed June 29, 2026, 1:39 a.m.
NEDg Description generation batch_6a41ce1d285c8190b3ef12f70b023803 completed June 29, 2026, 1:45 a.m.
NED2 Entity disambiguation (via description) batch_6a41ce834bc481908d5255609162bdbd completed June 29, 2026, 1:46 a.m.
Created at: May 3, 2026, 4:31 p.m.