Triple
T23500757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barbara Tucker |
E571833
|
entity |
| Predicate | collaboratedWith |
P435
|
FINISHED |
| Object | Louie Vega |
—
|
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: Louie Vega | Statement: [Barbara Tucker, collaboratedWith, Louie Vega]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Louie Vega Context triple: [Barbara Tucker, collaboratedWith, Louie Vega]
-
A.
Louie Vega
chosen
Louie Vega is a renowned American DJ and producer, best known as one half of the influential house music duo Masters at Work.
-
B.
Gui Boratto
Gui Boratto is a Brazilian electronic music producer and DJ known for his melodic techno and house productions and remixes.
-
C.
Laurent Garnier
Laurent Garnier is a pioneering French DJ, producer, and electronic music figure known for shaping the global techno and house scenes since the late 1980s.
-
D.
Benny Benassi
Benny Benassi is an Italian DJ and record producer best known for his influential electro house hits like "Satisfaction."
-
E.
Massimo Serato
Massimo Serato was an Italian film actor known for his prolific career in European cinema from the 1940s through the 1970s.
- 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_69e245b4829881909b77a70e942bbd54 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a8fc37908190af86a01ab85737d6 |
completed | April 29, 2026, 6:45 a.m. |
Created at: April 17, 2026, 6:06 p.m.