Triple

T35423925
Position Surface form Disambiguated ID Type / Status
Subject Exeter High Street E1023867 entity
Predicate hasNearby P350 FINISHED
Object North Street, Exeter
North Street in Exeter is a central city street that connects to the main shopping and historic core, linking High Street with surrounding urban areas.
 E2146868 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: North Street, Exeter | Statement: [Exeter High Street, hasNearby, North Street, Exeter]
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: North Street, Exeter
Triple: [Exeter High Street, hasNearby, North Street, Exeter]
Generated description
North Street in Exeter is a central city street that connects to the main shopping and historic core, linking High Street with surrounding urban areas.

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_69f76df6704081909900c60be10d5849 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f795905ed88190a9c6573824a56ca9 completed May 3, 2026, 6:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a385bbf67bc819091e37010fff710ad completed June 21, 2026, 9:46 p.m.
NEDg Description generation batch_6a385c22d430819098f330f216900f13 completed June 21, 2026, 9:48 p.m.
NED2 Entity disambiguation (via description) batch_6a385c67481c81908569d20e22af9187 completed June 21, 2026, 9:49 p.m.
Created at: May 3, 2026, 4:03 p.m.