Triple
T12670468
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steve Martin as Roger Cobb |
E302664
|
entity |
| Predicate | primaryRoleInFilm |
P14496
|
FINISHED |
| Object | protagonist |
—
|
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: protagonist | Statement: [Steve Martin as Roger Cobb, primaryRoleInFilm, protagonist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryRoleInFilm Context triple: [Steve Martin as Roger Cobb, primaryRoleInFilm, protagonist]
-
A.
roleInFilmEcosystem
Indicates the specific function or position an entity holds within the broader network of activities, stakeholders, and processes that make up the film ecosystem.
-
B.
hasMainRole
chosen
Indicates that an entity holds the primary or most significant role in relation to another entity or context.
-
C.
primaryActor
Indicates that the referenced entity is the main participant or most central party responsible for the action or event in the relationship.
-
D.
replacesInLeadRole
Indicates that one entity takes over or substitutes for another entity in the primary or leading role within a given context or production.
-
E.
givenNameInFilm
Indicates that a person is referred to by a particular given (first) name within the context of a specific film.
- F. None of above.
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_69d7bdee64a08190801c6d470aefd723 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961ae493481908f82e0d05dce20bd |
completed | April 10, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69d960bb64ec8190bd0400cf0cc8b0a7 |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:20 p.m.