Triple
T5166499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Big Operator |
E116572
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Ray Danton |
E211233
|
NE FINISHED |
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 Danton | Statement: [The Big Operator, hasCastMember, Ray Danton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ray Danton Context triple: [The Big Operator, hasCastMember, Ray Danton]
-
A.
Ray Danton
chosen
Ray Danton was an American actor and director best known for his suave, often villainous roles in film and television during the 1950s and 1960s.
-
B.
Jack Oaker
Jack Oaker was the husband of silent film actress Belle Bennett, known primarily in relation to her life and career.
-
C.
Bill Sullivan
Bill Sullivan is the father of James P. "Sulley" Sullivan, the main monster character in Pixar's animated film "Monsters, Inc."
-
D.
Dick Lundy
Dick Lundy was an American animator and director best known for his influential work at Walt Disney Studios, where he helped shape the personality and style of classic characters like Donald Duck.
-
E.
Ray Ferraro
Ray Ferraro is a former Canadian professional ice hockey player and prominent NHL broadcaster known for his long playing career and work as a television analyst.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69bd445edb3881909b93b34d260717fc |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd792c5ea88190b6aa0e519c744155 |
completed | March 20, 2026, 4:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bed93b85188190927d448e09a46425 |
completed | March 21, 2026, 5:45 p.m. |
Created at: March 20, 2026, 1:44 p.m.