Triple

T27052274
Position Surface form Disambiguated ID Type / Status
Subject canton of Aubusson E684803 entity
Predicate governingBody P46 FINISHED
Object Departmental Council of Creuse
The Departmental Council of Creuse is the deliberative and executive assembly responsible for managing local affairs and public policies in the Creuse department of central France.
E1754397 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: Departmental Council of Creuse | Statement: [canton of Aubusson, governingBody, Departmental Council of Creuse]
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: Departmental Council of Creuse
Triple: [canton of Aubusson, governingBody, Departmental Council of Creuse]
Generated description
The Departmental Council of Creuse is the deliberative and executive assembly responsible for managing local affairs and public policies in the Creuse department of central France.

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_69ef14829fac8190914bef9ecc3005d7 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f622b079908190938eece8d54fcc98 completed May 2, 2026, 4:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a123ac9d00c81909f88688d18dfa296 completed May 23, 2026, 11:39 p.m.
NEDg Description generation batch_6a123b8c553081909d6afd9e8a9878af completed May 23, 2026, 11:43 p.m.
NED2 Entity disambiguation (via description) batch_6a123c3427dc8190b6e78dcaabf69fab completed May 23, 2026, 11:45 p.m.
Created at: April 27, 2026, 8:15 a.m.