Triple
T22102542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Long Riders |
E546205
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Bill Bryden |
—
|
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: Bill Bryden | Statement: [The Long Riders, screenwriter, Bill Bryden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bill Bryden Context triple: [The Long Riders, screenwriter, Bill Bryden]
-
A.
Bill Bryden
chosen
Bill Bryden was a Scottish theatre and film director, producer, and writer known for his innovative stage productions and influential work in British drama.
-
B.
Ed Blandell
Ed Blandell is a relatively obscure figure known primarily as the sibling of American film and radio actress Gloria Blondell.
-
C.
Greg Bensen
Greg Bensen is an actor best known for his role in the 1984 romantic drama film "Bolero."
-
D.
Phil Brent
Phil Brent is a fictional character from the soap opera "All My Children," known for his complex family relationships and dramatic storylines.
-
E.
Dan Rydell
Dan Rydell is a charismatic, quick-witted sports anchor and one of the central protagonists on the television series "Sports Night."
- 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_69e11e378dc08190896d6a51597afd5a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129163b908190b63ace06016f4db8 |
completed | April 28, 2026, 9:39 p.m. |
Created at: April 16, 2026, 8:30 p.m.