Triple
T22039717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Here Comes the Fuzz |
E544305
|
entity |
| Predicate | hasGuestVocalist |
P44280
|
FINISHED |
| Object | Alex Greenwald |
—
|
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: Alex Greenwald | Statement: [Here Comes the Fuzz, hasGuestVocalist, Alex Greenwald]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alex Greenwald Context triple: [Here Comes the Fuzz, hasGuestVocalist, Alex Greenwald]
-
A.
Alex Greenwald
chosen
Alex Greenwald is an American musician and actor best known as the lead vocalist and guitarist of the rock band Phantom Planet.
-
B.
Dave Greenfield
Dave Greenfield was an English keyboardist best known for his distinctive, classically influenced playing in the punk and new wave band The Stranglers.
-
C.
Daniel Green
Daniel Green is a music producer known for his work on the track "Paradise."
-
D.
Craig Greenberg
Craig Greenberg is an American businessman, attorney, and Democratic politician serving as the mayor of Louisville, Kentucky.
-
E.
Sam Greenfield
Sam Greenfield is the perpetually unlucky young woman who becomes the central heroine of the animated fantasy film "Luck," navigating a secret world of good and bad fortune.
- 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_69e11e2f98c8819083e11eab90942a78 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127f532b08190be80c5af039b4c29 |
completed | April 28, 2026, 9:34 p.m. |
Created at: April 16, 2026, 8:25 p.m.