Triple

T24452727
Position Surface form Disambiguated ID Type / Status
Subject Brotherhood of the Wolf E616590 entity
Predicate editedBy P1954 FINISHED
Object Sébastien Prangère
Sébastien Prangère is a French film editor best known for his work on genre and action films, including the cult historical horror film "Brotherhood of the Wolf."
E1904846 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: Sébastien Prangère | Statement: [Brotherhood of the Wolf, editedBy, Sébastien Prangère]
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: Sébastien Prangère
Triple: [Brotherhood of the Wolf, editedBy, Sébastien Prangère]
Generated description
Sébastien Prangère is a French film editor best known for his work on genre and action films, including the cult historical horror film "Brotherhood of the Wolf."

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_69e2d7edca608190aafefc8877a1b4da completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f29859b824819087d4c7550dcbc426 completed April 29, 2026, 11:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a276413753c8190bc46aa676b646345 completed June 9, 2026, 12:53 a.m.
NEDg Description generation batch_6a2764dcc7148190b7ba48ce073f845f completed June 9, 2026, 12:57 a.m.
NED2 Entity disambiguation (via description) batch_6a2765db45d88190817f04133b5efd75 completed June 9, 2026, 1:01 a.m.
Created at: April 18, 2026, 2:18 a.m.