Triple
T20622758
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black Flag |
E506741
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Black Coffee |
—
|
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: Black Coffee | Statement: [Black Flag, notableWork, Black Coffee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Black Coffee Context triple: [Black Flag, notableWork, Black Coffee]
-
A.
Black Coffee
"Black Coffee" is a popular hip hop track by Heavy D & the Boyz known for its smooth production and socially conscious lyrics.
-
B.
Black Coffee
chosen
Black Coffee is a renowned South African DJ and record producer celebrated for his influential contributions to house music on the global stage.
-
C.
Black Caffeine
"Black Caffeine" is a song featured on the country album *Old Yellow Moon* by Emmylou Harris and Rodney Crowell.
-
D.
Koffee
Koffee is a Jamaican reggae and dancehall singer, songwriter, and rapper known for her Grammy-winning EP "Rapture" and hit single "Toast."
-
E.
Coffee (Fucking)
"Coffee (Fucking)" is a remix version of Kelly Rowland's song "Coffee," featuring more explicit lyrics and a sensual, R&B-influenced production.
- 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_69e0b4bc90988190ac360aaf645efc1d |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6abe3177c8190ad1b2ca8b1e0a560 |
completed | April 20, 2026, 10:42 p.m. |
Created at: April 16, 2026, 11:42 a.m.