Triple
T15653185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gyptian |
E376362
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Wine Slow
"Wine Slow" is a popular reggae fusion song by Jamaican artist Gyptian, known for its smooth, sensual rhythm and romantic lyrics.
|
E1169539
|
NE FINISHED |
How this triple was built (4 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: Wine Slow | Statement: [Gyptian, notableWork, Wine Slow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wine Slow Context triple: [Gyptian, notableWork, Wine Slow]
-
A.
Slow Wine
"Slow Wine" is an R&B song by the American group Tony! Toni! Toné! known for its smooth, romantic groove and soulful vocal harmonies.
-
B.
Fast Wine
"Fast Wine" is a popular soca song by Trinidadian artist Machel Montano, known for its infectious rhythm and dance-focused party vibe.
-
C.
In the Wine Time
"In the Wine Time" is a 1968 play by African American dramatist Ed Bullins that portrays life and struggles in an urban Black community as part of his influential Black Arts Movement–era work.
-
D.
Slow Whine
Slow Whine is a track featured on the album "Superstar," known for its smooth, laid-back vibe and melodic style.
-
E.
In Vino
In Vino is a dark comedy film featuring Jennifer Candy in a prominent role, centered on secrets and tensions that erupt during a wine-fueled gathering.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Wine Slow Triple: [Gyptian, notableWork, Wine Slow]
Generated description
"Wine Slow" is a popular reggae fusion song by Jamaican artist Gyptian, known for its smooth, sensual rhythm and romantic lyrics.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wine Slow Target entity description: "Wine Slow" is a popular reggae fusion song by Jamaican artist Gyptian, known for its smooth, sensual rhythm and romantic lyrics.
-
A.
Slow Wine
"Slow Wine" is an R&B song by the American group Tony! Toni! Toné! known for its smooth, romantic groove and soulful vocal harmonies.
-
B.
Fast Wine
"Fast Wine" is a popular soca song by Trinidadian artist Machel Montano, known for its infectious rhythm and dance-focused party vibe.
-
C.
In the Wine Time
"In the Wine Time" is a 1968 play by African American dramatist Ed Bullins that portrays life and struggles in an urban Black community as part of his influential Black Arts Movement–era work.
-
D.
Slow Whine
Slow Whine is a track featured on the album "Superstar," known for its smooth, laid-back vibe and melodic style.
-
E.
In Vino
In Vino is a dark comedy film featuring Jennifer Candy in a prominent role, centered on secrets and tensions that erupt during a wine-fueled gathering.
- F. None of above. chosen
Provenance (5 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_69d85cd1564c8190991adda63bfab4b0 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04ef089948190902ec22f4d7bc932 |
completed | April 16, 2026, 2:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff6797954c8190ac05ee3db634efa7 |
completed | May 9, 2026, 4:57 p.m. |
| NEDg | Description generation | batch_69ff68481ff881909c23ae20bd3a9ff8 |
completed | May 9, 2026, 5 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff6911a76c819088c8a86d2106b6c6 |
completed | May 9, 2026, 5:04 p.m. |
Created at: April 10, 2026, 4:15 a.m.