Triple
T21633554
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ludacris |
E533895
|
entity |
| Predicate | notableSong |
P4
|
FINISHED |
| Object | Blueberry Yum Yum |
—
|
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: Blueberry Yum Yum | Statement: [Ludacris, notableSong, Blueberry Yum Yum]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blueberry Yum Yum Context triple: [Ludacris, notableSong, Blueberry Yum Yum]
-
A.
Blueberry Yum Yum
chosen
Blueberry Yum Yum is a hip-hop track best known for its laid-back, cannabis-themed lyrics and smooth, melodic production.
-
B.
Blueberry Faygo
Blueberry Faygo is a viral, melodic hip-hop single by rapper Lil Mosey that became a major streaming hit and TikTok favorite in 2020.
-
C.
Blueberries
"Blueberries" is a poem by Robert Frost that vividly portrays rural life and the labor of berry-picking in New England.
-
D.
Raspberry Swirl
"Raspberry Swirl" is an industrial-tinged electronic rock song by Tori Amos, known for its aggressive sound and exploration of themes around sexuality and emotional turmoil.
-
E.
Blueberry
Blueberry is a translucent light-blue color variant famously used on Apple’s early iMac G3 computers.
- 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_69e0c465ae7481908577b7209fdb2a77 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef52185dbc819096ad2fc5b7d953f8 |
completed | April 27, 2026, 12:10 p.m. |
Created at: April 16, 2026, 6:35 p.m.