Triple
T12667391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Hanks as Colonel Tom Parker |
E302591
|
entity |
| Predicate | narrativeRoleInFilm |
P101050
|
FINISHED |
| Object | primary narrator |
—
|
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: primary narrator | Statement: [Tom Hanks as Colonel Tom Parker, narrativeRoleInFilm, primary narrator]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: narrativeRoleInFilm Context triple: [Tom Hanks as Colonel Tom Parker, narrativeRoleInFilm, primary narrator]
-
A.
narrativeRoleInSeries
Indicates the specific narrative function or role an entity plays within a particular series or serialized work.
-
B.
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.
-
C.
inNarrativeRole
chosen
Indicates that one entity participates in relation to another by occupying a specific narrative function or role within a story or discourse.
-
D.
roleInScene
Indicates that an entity participates in a particular scene with a specific role or function within that scene.
-
E.
metaNarrativeRole
Indicates the narrative function or role that one element (such as a character, voice, or device) plays in commenting on, framing, or reflecting the story itself at a meta-level.
- 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_69d7bded71a88190bb76e2413af9ea66 |
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.