Triple
T19455692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Damsel |
E486726
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Ray Winstone |
—
|
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: Ray Winstone | Statement: [Damsel, starring, Ray Winstone]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ray Winstone Context triple: [Damsel, starring, Ray Winstone]
-
A.
Ray Winstone
chosen
Ray Winstone is an English actor known for his tough-guy roles in British film and television, including performances in movies like "Sexy Beast" and "The Departed."
-
B.
Sam Riley
Sam Riley is an English actor known for roles in films such as "Control," "On the Road," and Disney's "Maleficent" series.
-
C.
David Morrissey
David Morrissey is an English actor and director known for his versatile performances in film and television, including prominent roles in series like "The Walking Dead."
-
D.
Stephen Norton
Stephen Norton is a mathematician known for his contributions to the theory of finite groups and the Monster group in particular.
-
E.
David Craig
David Craig is a senior Royal Air Force officer who rose to become Chief of the Air Staff and later Chief of the Defence Staff of the United Kingdom.
- 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633c2b1108190b492ca23487b91f8 |
completed | April 20, 2026, 2:10 p.m. |
Created at: April 10, 2026, 1:38 p.m.