Triple
T29345504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Easy Money |
E744158
|
entity |
| Predicate | hasRodneyDangerfieldFilmographyPosition |
P185308
|
FINISHED |
| Object | One of Rodney Dangerfield's early starring film roles after Caddyshack |
—
|
LITERAL 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: One of Rodney Dangerfield's early starring film roles after Caddyshack | Statement: [Easy Money, hasRodneyDangerfieldFilmographyPosition, One of Rodney Dangerfield's early starring film roles after Caddyshack]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRodneyDangerfieldFilmographyPosition Context triple: [Easy Money, hasRodneyDangerfieldFilmographyPosition, One of Rodney Dangerfield's early starring film roles after Caddyshack]
-
A.
hasGlennFordRole
Indicates that an entity has a role played by the actor Glenn Ford.
-
B.
hasFilmCareer
Indicates that an entity has been professionally involved in the film industry as a career.
-
C.
hasFilmographyType
Indicates the type or category of film-related work associated with an entity (e.g., actor, director, producer) within its filmography.
-
D.
actsIn
Indicates that an entity performs or appears in a creative work, such as a film, play, or show.
-
E.
numberOfFilmsAppearedIn
Indicates the total count of distinct films in which a given entity has appeared.
- F. None of above. chosen
Provenance (4 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_69f0a79a2d748190bc30abd469298b37 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_69f7be53890081909b1d93f30a8f31c6 |
completed | May 3, 2026, 9:29 p.m. |
| PD | Predicate disambiguation | batch_69f7bccacbac8190978976324c67db28 |
completed | May 3, 2026, 9:23 p.m. |
| PDg | Predicate description generation | batch_69f7be520f148190ba200bf3dbf40656 |
completed | May 3, 2026, 9:29 p.m. |
Created at: April 28, 2026, 2:01 p.m.