Triple
T2258263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coffee (Fucking) |
E49777
|
entity |
| Predicate | remixOf |
P9639
|
FINISHED |
| Object | Coffee |
E49777
|
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: Coffee | Statement: [Coffee (Fucking), remixOf, Coffee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Coffee Context triple: [Coffee (Fucking), remixOf, Coffee]
-
A.
Coffee
chosen
"Coffee" is a song by the American singer-songwriter Miguel, known for its smooth blend of R&B and sensual, atmospheric production.
-
B.
Blue Mountains coffee
Blue Mountains coffee is a highly prized Jamaican coffee variety renowned for its mild flavor, smooth body, and low bitterness, grown at high elevations in the Blue Mountains region.
-
C.
Mocha
Mocha is a popular JavaScript test framework used primarily for running unit and integration tests in Node.js and browser-based applications.
-
D.
Cocoa
Cocoa is Apple’s native object-oriented application framework for building graphical user interfaces and other software on macOS.
-
E.
Coffee and Cigarettes
Coffee and Cigarettes is an independent anthology film by Jim Jarmusch composed of a series of black-and-white vignettes in which various actors and musicians converse over coffee and cigarettes.
- 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_69a88aaa9250819095e127d0d77e8a32 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc15839fc8190b17e040c4c765a8c |
completed | March 7, 2026, 6:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae71c8c9b881909b5292110a87301a |
completed | March 9, 2026, 7:07 a.m. |
Created at: March 4, 2026, 7:48 p.m.