Triple

T36946583
Position Surface form Disambiguated ID Type / Status
Subject John Brydon E913927 entity
Predicate notableWork P4 FINISHED
Object Bethnal Green Town Hall
Bethnal Green Town Hall is a historic municipal building in East London, noted for its Edwardian Baroque architecture and later Art Deco extensions, which has since been converted into a boutique hotel and event venue.
E2207126 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: Bethnal Green Town Hall | Statement: [John Brydon, notableWork, Bethnal Green Town Hall]
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: Bethnal Green Town Hall
Triple: [John Brydon, notableWork, Bethnal Green Town Hall]
Generated description
Bethnal Green Town Hall is a historic municipal building in East London, noted for its Edwardian Baroque architecture and later Art Deco extensions, which has since been converted into a boutique hotel and event venue.

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_69f76e8b28848190abd81fe7a7374910 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f9fed857c48190865d4631aa73f22b completed May 5, 2026, 2:29 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3e2c322f0481908de54b36b857327c completed June 26, 2026, 7:37 a.m.
NEDg Description generation batch_6a3e313cca808190b748693a5690b8b2 completed June 26, 2026, 7:58 a.m.
NED2 Entity disambiguation (via description) batch_6a3e46f283a08190a7aaf2d3099e9827 completed June 26, 2026, 9:31 a.m.
Created at: May 3, 2026, 4:13 p.m.