Triple

T9866951
Position Surface form Disambiguated ID Type / Status
Subject Wigmore Street E239856 entity
Predicate hasJunctionWith P1018 FINISHED
Object Duke Street
Duke Street is a central London street in the City of Westminster, known for its proximity to Oxford Street and its mix of shops, offices, and residential buildings.
 E2296002 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: Duke Street | Statement: [Wigmore Street, hasJunctionWith, Duke 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: Duke Street
Triple: [Wigmore Street, hasJunctionWith, Duke Street]
Generated description
Duke Street is a central London street in the City of Westminster, known for its proximity to Oxford Street and its mix of shops, offices, and residential buildings.

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_69ca84e7506c819095cbde4ff16512bb completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb3d209ac8190b9bc9ff017a132da completed April 2, 2026, 12:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a8221ba2be08190942ee67d2d1d69a6 completed Aug. 16, 2026, 8:46 p.m.
NEDg Description generation batch_6a8222152f148190ad062cd8e2f7c177 completed Aug. 16, 2026, 8:48 p.m.
NED2 Entity disambiguation (via description) batch_6a8222675d708190b17d468d208abff6 completed Aug. 16, 2026, 8:49 p.m.
Created at: March 30, 2026, 8:36 p.m.