Triple

T36053179
Position Surface form Disambiguated ID Type / Status
Subject Diocese of Gap E1042870 entity
Predicate mergedWith P77 FINISHED
Object Diocese of Embrun
The Diocese of Embrun was a historic Roman Catholic diocese in southeastern France, centered on the town of Embrun in the Alps and notable for its medieval ecclesiastical importance.
E2189680 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: Diocese of Embrun | Statement: [Diocese of Gap, mergedWith, Diocese of Embrun]
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: Diocese of Embrun
Triple: [Diocese of Gap, mergedWith, Diocese of Embrun]
Generated description
The Diocese of Embrun was a historic Roman Catholic diocese in southeastern France, centered on the town of Embrun in the Alps and notable for its medieval ecclesiastical importance.

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_69f76e2e41f8819091f9fb0536920fec completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7b1e5cee88190a767176ef76e63c2 completed May 3, 2026, 8:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39e6c0c56481909ecfbe55f1e6ffaa completed June 23, 2026, 1:52 a.m.
NEDg Description generation batch_6a39ebefe4f481908ebd0a82e7502415 completed June 23, 2026, 2:14 a.m.
NED2 Entity disambiguation (via description) batch_6a39f0220d6481909df262c411aa1a72 completed June 23, 2026, 2:32 a.m.
Created at: May 3, 2026, 4:07 p.m.