Triple
T13549430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nicole Kidman as Sue Brierley |
E323601
|
entity |
| Predicate | screenTimeRoleSize |
P101227
|
FINISHED |
| Object | supporting role |
—
|
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: supporting role | Statement: [Nicole Kidman as Sue Brierley, screenTimeRoleSize, supporting role]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: screenTimeRoleSize Context triple: [Nicole Kidman as Sue Brierley, screenTimeRoleSize, supporting role]
-
A.
screenTimeRole
chosen
Indicates the role or capacity in which an entity appears on screen, such as main, supporting, cameo, or other participation type.
-
B.
screenTime
Indicates the amount of time an entity spends viewing or interacting with a screen-based device.
-
C.
screenTimeFunction
Indicates a functional relationship that maps an entity (such as a user, device, or app) to the amount of time its screen is active or in use.
-
D.
screenTimeEra
Indicates the period or era during which a particular screen-based technology, medium, or usage pattern is prevalent or characteristic.
-
E.
screenTimePhase
Indicates the specific stage or period within a broader screen-time process, session, or lifecycle that an entity is currently in.
- 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbbb9ee3f081909056dc1a92c40b7a |
completed | April 12, 2026, 3:34 p.m. |
| PD | Predicate disambiguation | batch_69dbae13bec4819084c1770638c00ed9 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:46 p.m.