Triple

T28696664
Position Surface form Disambiguated ID Type / Status
Subject Council of Civil Service Unions E729434 entity
Predicate hasMember P10 FINISHED
Object Prison Officers Association
The Prison Officers Association is a trade union in the United Kingdom that represents the interests and working conditions of prison officers and related staff.
E1832132 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: Prison Officers Association | Statement: [Council of Civil Service Unions, hasMember, Prison Officers Association]
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: Prison Officers Association
Triple: [Council of Civil Service Unions, hasMember, Prison Officers Association]
Generated description
The Prison Officers Association is a trade union in the United Kingdom that represents the interests and working conditions of prison officers and related staff.

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_69f043e6e9688190b6bdd6e5665498ff completed April 28, 2026, 5:21 a.m.
NER Named-entity recognition batch_69f656b0f9ac819090660f9a778ff7dc completed May 2, 2026, 7:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1ccf4ac2308190b63eb7ab03ef789b completed June 1, 2026, 12:16 a.m.
NEDg Description generation batch_6a249437ba308190b0e40496c8e38562 completed June 6, 2026, 9:42 p.m.
NED2 Entity disambiguation (via description) batch_6a2498d0c39481908a79cf81b510f6d7 completed June 6, 2026, 10:01 p.m.
Created at: April 28, 2026, 5:40 a.m.