Triple
T17554255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | WLUP-FM |
E427548
|
entity |
| Predicate | notablePersonality |
P7128
|
FINISHED |
| Object | Steve Dahl |
—
|
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: Steve Dahl | Statement: [WLUP-FM, notablePersonality, Steve Dahl]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steve Dahl Context triple: [WLUP-FM, notablePersonality, Steve Dahl]
-
A.
Steve Dahl
chosen
Steve Dahl is a Chicago radio personality and humorist best known for his role in the infamous 1979 "Disco Demolition Night" promotion and his influential, irreverent style on FM talk radio.
-
B.
Ted Daughety
Ted Daughety is an American physician and pulmonologist best known as the husband of Kansas Governor Laura Kelly.
-
C.
John Diehl
John Diehl is an American character actor best known for his role as Detective Larry Zito on the 1980s television series "Miami Vice."
-
D.
Phil Dusenberry
Phil Dusenberry was an influential American advertising executive and creative director, best known for his groundbreaking work at BBDO and for shaping major campaigns for brands like Pepsi.
-
E.
Dale Van Sickel
Dale Van Sickel was an American actor and pioneering Hollywood stuntman known for his work in numerous action films and serials from the 1930s through the 1950s.
- 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_69d889df6dc081908f67dbadc03c07ee |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e45620983c81909e71f938ce934efa |
completed | April 19, 2026, 4:12 a.m. |
Created at: April 10, 2026, 5:50 a.m.