Triple
T19504405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deezer |
E487981
|
entity |
| Predicate | founder |
P104
|
FINISHED |
| Object | Jonathan Benassaya |
—
|
NE NERFINISHED |
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: Jonathan Benassaya | Statement: [Deezer, founder, Jonathan Benassaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jonathan Benassaya Context triple: [Deezer, founder, Jonathan Benassaya]
-
A.
Jonathan Benassaya
chosen
Jonathan Benassaya is a French entrepreneur best known for co-founding the music streaming service Deezer.
-
B.
Aaron Benenson
Aaron Benenson is an individual notable enough to be recognized as a significant bearer of the surname Benenson.
-
C.
Nathaniel Buzolic
Nathaniel Buzolic is an Australian actor best known for his roles in television series such as "The Vampire Diaries" and "The Originals," as well as appearances in various film and TV projects.
-
D.
Andrew Miano
Andrew Miano is an American film producer known for his work on independent and critically acclaimed movies, often collaborating with director Tom Ford and others.
-
E.
Jonathan Sela
Jonathan Sela is a cinematographer known for his dynamic, high-energy visual work on major action films and thrillers.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d9d1c88190b01cd78b8be49384 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e635105db8819084915dc2d047188d |
completed | April 20, 2026, 2:15 p.m. |
Created at: April 10, 2026, 1:40 p.m.