Triple
T15533997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sweet Magnolias |
E370295
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | Dan Paulson |
—
|
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: Dan Paulson | Statement: [Sweet Magnolias, executiveProducer, Dan Paulson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Paulson Context triple: [Sweet Magnolias, executiveProducer, Dan Paulson]
-
A.
Dan Paulson
chosen
Dan Paulson is a film and television producer best known for his work on action films like "Passenger 57."
-
B.
Dan Kolsrud
Dan Kolsrud is a film producer best known for his work on the family road-trip comedy "Are We There Yet?" starring Ice Cube.
-
C.
Don Brautigam
Don Brautigam was an American illustrator best known for his striking, realistic cover art for horror and thriller novels, including works by Stephen King.
-
D.
Dean Paul Larson
Dean Paul Larson is a fictional character from the television series "The Chair."
-
E.
Bill Nelsen
Bill Nelsen was an American professional football quarterback best known for leading the Cleveland Browns in the late 1960s and early 1970s.
- 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_69d85cc521a08190921fb50319dddc34 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e0414877d88190804ee76566004e13 |
completed | April 16, 2026, 1:54 a.m. |
Created at: April 10, 2026, 4:06 a.m.