Triple

T27464157
Position Surface form Disambiguated ID Type / Status
Subject Roman Catholic Diocese of Le Mans E693130 entity
Predicate governingBody P46 FINISHED
Object Bishop of Le Mans
The Bishop of Le Mans is the senior Roman Catholic prelate responsible for leading the diocese centered in the city of Le Mans in western France.
E1775567 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: Bishop of Le Mans | Statement: [Roman Catholic Diocese of Le Mans, governingBody, Bishop of Le Mans]
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: Bishop of Le Mans
Triple: [Roman Catholic Diocese of Le Mans, governingBody, Bishop of Le Mans]
Generated description
The Bishop of Le Mans is the senior Roman Catholic prelate responsible for leading the diocese centered in the city of Le Mans in western 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_69ef538105548190a771cc5a0cf8c211 completed April 27, 2026, 12:16 p.m.
NER Named-entity recognition batch_69f62dfbe6508190a6871084c5afe20f completed May 2, 2026, 5:01 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12bbdcabbc8190b26943ef425d7397 completed May 24, 2026, 8:50 a.m.
NEDg Description generation batch_6a12bcf8cd9c81909f9f001e8a1d4a81 completed May 24, 2026, 8:55 a.m.
NED2 Entity disambiguation (via description) batch_6a12bd75c610819081ae1b4f7fedb4cb completed May 24, 2026, 8:57 a.m.
Created at: April 27, 2026, 12:51 p.m.