Triple

T37581610
Position Surface form Disambiguated ID Type / Status
Subject Fantômas se déchaîne E934981 entity
Predicate musicBy P1952 FINISHED
Object Georges Garvarentz
Georges Garvarentz was an Armenian-French composer best known for his prolific film scores and popular songs, particularly his collaborations with singer Charles Aznavour.
E2233996 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: Georges Garvarentz | Statement: [Fantômas se déchaîne, musicBy, Georges Garvarentz]
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: Georges Garvarentz
Triple: [Fantômas se déchaîne, musicBy, Georges Garvarentz]
Generated description
Georges Garvarentz was an Armenian-French composer best known for his prolific film scores and popular songs, particularly his collaborations with singer Charles Aznavour.

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_69f76ece61dc8190a0ab33f8d87d0a7e completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fba88b490c81908f1a30f4b0cda412 completed May 6, 2026, 8:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40a7f565b08190916d318c897c19a9 completed June 28, 2026, 4:49 a.m.
NEDg Description generation batch_6a40a8d3abd881908651ced357bc36ec completed June 28, 2026, 4:53 a.m.
NED2 Entity disambiguation (via description) batch_6a40a970bc8c81909713a1cea994881b completed June 28, 2026, 4:56 a.m.
Created at: May 3, 2026, 4:17 p.m.