Triple
T12258273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Big Wiz |
E292155
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Fever |
E292160
|
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: Fever | Statement: [Big Wiz, notableWork, Fever]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fever Context triple: [Big Wiz, notableWork, Fever]
-
A.
Fever
"Fever" is a 2014 blues-rock single by American rock duo The Black Keys, known for its catchy organ riff and appearance on their album "Turn Blue."
-
B.
Fever
chosen
"Fever" is a 2018 Afrobeats single by Nigerian artist Wizkid, known for its sultry vibe and viral music video featuring Tiwa Savage.
-
C.
Fever
"Fever" is a classic, sultry jazz-pop song popularized by Peggy Lee, renowned for its minimalist arrangement and intimate vocal style.
-
D.
Fever
"Fever" is a track by the experimental hip-hop group Black Milk, known for its intricate production and innovative approach to rap music.
-
E.
Fever
"Fever" is a popular dancehall track by Jamaican artist Adidja Palmer, better known as Vybz Kartel, noted for its infectious rhythm and widespread club and radio play.
- 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_69d6ab67950c8190be08450a06228c4b |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91ccadc3c81908fe68adc3fdcc851 |
completed | April 10, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60abfc8588190ab9300c6e5e59092 |
completed | May 2, 2026, 2:31 p.m. |
Created at: April 8, 2026, 9:52 p.m.