Triple

T28990654
Position Surface form Disambiguated ID Type / Status
Subject Banbury Cross junction E736016 entity
Predicate hasNearbyStreet P8235 FINISHED
Object North Bar Street
North Bar Street is a main thoroughfare in the market town of Banbury, Oxfordshire, forming part of the historic central street network near Banbury Cross.
E2287442 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 Bar Street | Statement: [Banbury Cross junction, hasNearbyStreet, North Bar Street]
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 Bar Street
Triple: [Banbury Cross junction, hasNearbyStreet, North Bar Street]
Generated description
North Bar Street is a main thoroughfare in the market town of Banbury, Oxfordshire, forming part of the historic central street network near Banbury Cross.

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_69f077eacd0481908ef0bafd74491cd0 completed April 28, 2026, 9:03 a.m.
NER Named-entity recognition batch_69f65f7d3b5c8190937aaddff2879989 completed May 2, 2026, 8:33 p.m.
NED1 Entity disambiguation (via context triple) batch_6a59f095473881909bb4bd53bad91ea2 completed July 17, 2026, 9:06 a.m.
NEDg Description generation batch_6a59f2942b54819087964ecd1871b04b completed July 17, 2026, 9:15 a.m.
NED2 Entity disambiguation (via description) batch_6a59f3800a048190a379c6aa68e86f2c completed July 17, 2026, 9:18 a.m.
Created at: April 28, 2026, 9:24 a.m.