Triple
T7986191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flesh Tone |
E185686
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | David Guetta |
E39421
|
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: David Guetta | Statement: [Flesh Tone, producer, David Guetta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Guetta Context triple: [Flesh Tone, producer, David Guetta]
-
A.
David Guetta
chosen
David Guetta is a French DJ and record producer known for pioneering mainstream EDM and creating numerous global pop and dance hits through high-profile collaborations.
-
B.
Remy van Buuren
Remy van Buuren is one of the children of Dutch trance DJ and record producer Armin van Buuren.
-
C.
Martin Garrix
Martin Garrix is a Dutch DJ and electronic music producer known for global EDM hits like "Animals" and for headlining major festivals worldwide.
-
D.
Benny Benassi
Benny Benassi is an Italian DJ and record producer best known for his influential electro house hits like "Satisfaction."
-
E.
Paul van Dyk
Paul van Dyk is a German DJ and producer widely regarded as one of the pioneers and leading figures of the trance music genre.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca829a2cfc819083d591d58ec04075 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3c4b87e48190a797f5363c8f0a04 |
completed | March 31, 2026, 3:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbe0e6f3c48190a0132fa90eec6420 |
completed | March 31, 2026, 2:57 p.m. |
Created at: March 30, 2026, 5:15 p.m.