Triple
T23327910
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Let the Beat Build |
E591348
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Deezle |
—
|
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: Deezle | Statement: [Let the Beat Build, producer, Deezle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deezle Context triple: [Let the Beat Build, producer, Deezle]
-
A.
Deezle
chosen
Deezle is an American hip-hop and R&B record producer best known for his work with Lil Wayne, including contributions to the hit album "Tha Carter III."
-
B.
Doozer
Doozer is a television production company founded by Bill Lawrence, best known for producing series such as Scrubs, Cougar Town, and Ted Lasso.
-
C.
Zebstrika
Zebstrika is an Electric-type zebra Pokémon known for its lightning-fast speed and the ability to generate powerful electrical discharges.
-
D.
Deeba
Deeba is the resourceful and courageous young heroine of China Miéville's fantasy novel "Un Lun Dun," who becomes an unexpected savior in a strange alternate London.
-
E.
Garthdee
Garthdee is a riverside area in the southwest of Aberdeen, Scotland, known for hosting the main campus of Robert Gordon University.
- 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_69e25d1effe4819096907f95f610dbff |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f197eb6cd481908acd5619b3af246d |
completed | April 29, 2026, 5:32 a.m. |
Created at: April 17, 2026, 5:13 p.m.