Triple

T29277629
Position Surface form Disambiguated ID Type / Status
Subject Police Regional Offices E742284 entity
Predicate supervise P9418 FINISHED
Object Police Provincial Offices
Police Provincial Offices are local law enforcement headquarters responsible for overseeing and coordinating police operations within a specific province or equivalent administrative area.
E1859832 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: Police Provincial Offices | Statement: [Police Regional Offices, supervise, Police Provincial Offices]
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: Police Provincial Offices
Triple: [Police Regional Offices, supervise, Police Provincial Offices]
Generated description
Police Provincial Offices are local law enforcement headquarters responsible for overseeing and coordinating police operations within a specific province or equivalent administrative area.

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_69f0912124d48190a046642b69407f4c completed April 28, 2026, 10:51 a.m.
NER Named-entity recognition batch_69f665130c7081908d42c2d803ed47d1 completed May 2, 2026, 8:56 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25893d748c81909f427642cfa4f2cd completed June 7, 2026, 3:07 p.m.
NEDg Description generation batch_6a258e5fdc0c8190ac32db7404b5ef6c completed June 7, 2026, 3:29 p.m.
NED2 Entity disambiguation (via description) batch_6a2593a40fa48190b705582f4be60140 completed June 7, 2026, 3:52 p.m.
Created at: April 28, 2026, 12:52 p.m.