Triple
T6154946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black Milk |
E137295
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Fever
"Fever" is a track by the experimental hip-hop group Black Milk, known for its intricate production and innovative approach to rap music.
|
E572744
|
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: Fever | Statement: [Black Milk, notableWork, Fever]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fever Context triple: [Black Milk, notableWork, Fever]
-
A.
Fever
"Fever" is a 2018 Afrobeats single by Nigerian artist Wizkid, known for its sultry vibe and viral music video featuring Tiwa Savage.
-
B.
Fever
"Fever" is a classic, sultry jazz-pop song popularized by Peggy Lee, renowned for its minimalist arrangement and intimate vocal style.
-
C.
Fever 103°
Fever 103° is a confessional poem by Sylvia Plath that vividly explores themes of illness, purification, and transcendence through intense, hallucinatory imagery.
-
D.
Flu
Flu is the popular nickname of Fluminense Football Club, a traditional Brazilian football team based in Rio de Janeiro.
-
E.
Heat
Heat is a 1995 crime thriller film directed by Michael Mann, renowned for its intense heist sequences and the iconic pairing of Al Pacino and Robert De Niro.
- 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: Fever Triple: [Black Milk, notableWork, Fever]
Generated description
"Fever" is a track by the experimental hip-hop group Black Milk, known for its intricate production and innovative approach to rap music.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fever Target entity description: "Fever" is a track by the experimental hip-hop group Black Milk, known for its intricate production and innovative approach to rap music.
-
A.
Fever
"Fever" is a 2018 Afrobeats single by Nigerian artist Wizkid, known for its sultry vibe and viral music video featuring Tiwa Savage.
-
B.
Fever
"Fever" is a classic, sultry jazz-pop song popularized by Peggy Lee, renowned for its minimalist arrangement and intimate vocal style.
-
C.
Fever 103°
Fever 103° is a confessional poem by Sylvia Plath that vividly explores themes of illness, purification, and transcendence through intense, hallucinatory imagery.
-
D.
Flu
Flu is the popular nickname of Fluminense Football Club, a traditional Brazilian football team based in Rio de Janeiro.
-
E.
Heat
Heat is a 1995 crime thriller film directed by Michael Mann, renowned for its intense heist sequences and the iconic pairing of Al Pacino and Robert De Niro.
- 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_69c008a45d008190832a9e19f5d63406 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05d01ddb0819085b5f5338b86a25d |
completed | March 22, 2026, 9:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1418195d8819092743f323430b9a8 |
completed | March 23, 2026, 1:34 p.m. |
| NEDg | Description generation | batch_69c144696f80819092131e86a3bb3b63 |
completed | March 23, 2026, 1:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c144c523c48190a709342dc031d2b8 |
completed | March 23, 2026, 1:48 p.m. |
Created at: March 22, 2026, 4:17 p.m.