Triple

T27382242
Position Surface form Disambiguated ID Type / Status
Subject Louis Fuzelier E691262 entity
Predicate collaboratedWith P435 FINISHED
Object François Francoeur
François Francoeur was an 18th-century French violinist, composer, and opera director known for his contributions to French Baroque opera and instrumental music.
E1787766 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: François Francoeur | Statement: [Louis Fuzelier, collaboratedWith, François Francoeur]
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: François Francoeur
Triple: [Louis Fuzelier, collaboratedWith, François Francoeur]
Generated description
François Francoeur was an 18th-century French violinist, composer, and opera director known for his contributions to French Baroque opera and instrumental music.

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_69ef52022538819081f873d0c84a6dd6 completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62c87eb3c8190bf418d88c19819ba completed May 2, 2026, 4:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12ec8f9e24819091bdc4db4a4fd68d completed May 24, 2026, 12:18 p.m.
NEDg Description generation batch_6a12ed0fc060819085a0872a16a4badf completed May 24, 2026, 12:20 p.m.
NED2 Entity disambiguation (via description) batch_6a12ed9d47988190a62268071859ede2 completed May 24, 2026, 12:22 p.m.
Created at: April 27, 2026, 12:23 p.m.