Triple

T28353091
Position Surface form Disambiguated ID Type / Status
Subject Ann Widdecombe E718151 entity
Predicate positionHeld P8 FINISHED
Object Minister of State for Prisons
The Minister of State for Prisons is a UK government ministerial role responsible for overseeing the prison system, offender management, and related aspects of criminal justice policy.
E1814186 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: Minister of State for Prisons | Statement: [Ann Widdecombe, positionHeld, Minister of State for Prisons]
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: Minister of State for Prisons
Triple: [Ann Widdecombe, positionHeld, Minister of State for Prisons]
Generated description
The Minister of State for Prisons is a UK government ministerial role responsible for overseeing the prison system, offender management, and related aspects of criminal justice policy.

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_69eff6ec27b481908c8d7b86c47893d9 completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64c0d6560819087fafc08ce48e158 completed May 2, 2026, 7:10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1627c6d11c81908df516c01dcfaf02 completed May 26, 2026, 11:07 p.m.
NEDg Description generation batch_6a16290c6a808190817f7bee27d4e0ee completed May 26, 2026, 11:13 p.m.
NED2 Entity disambiguation (via description) batch_6a162a28a0bc81909d87cabc75fdb1c3 completed May 26, 2026, 11:18 p.m.
Created at: April 28, 2026, 12:47 a.m.