Triple
T23248888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Tom Club |
E581668
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object | Bob Funk |
—
|
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: Bob Funk | Statement: [Tom Tom Club, hasMember, Bob Funk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bob Funk Context triple: [Tom Tom Club, hasMember, Bob Funk]
-
A.
Bob Funk
chosen
Bob Funk is a musician best known as a member of the new wave band Tom Tom Club.
-
B.
Bill Finegan
Bill Finegan was an American jazz arranger and composer best known for his innovative big band arrangements and co-leading the Sauter-Finegan Orchestra in the 1950s.
-
C.
Bob Sikes
Bob Sikes was a long-serving U.S. Congressman from Florida known for his influence on military and infrastructure development in the state.
-
D.
Ernie McCracken
Ernie McCracken is the flamboyantly villainous professional bowler and main antagonist portrayed by Bill Murray in the comedy film "Kingpin."
-
E.
Mel Farr
Mel Farr was an American NFL running back best known for his standout early career with the Detroit Lions and later success as a prominent automobile dealer.
- 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_69e24606b17c81908aba1a4911c8a8ba |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f193f4d7e4819085eec6279696db34 |
completed | April 29, 2026, 5:15 a.m. |
Created at: April 17, 2026, 4:10 p.m.