Triple
T5694550
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anupam Kher as Dr. Cliff Patel |
E125504
|
entity |
| Predicate | screenTimeCategory |
P47557
|
FINISHED |
| Object | supportingRole |
—
|
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: supportingRole | Statement: [Anupam Kher as Dr. Cliff Patel, screenTimeCategory, supportingRole]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: screenTimeCategory Context triple: [Anupam Kher as Dr. Cliff Patel, screenTimeCategory, supportingRole]
-
A.
screenTimeFocus
Indicates the amount or proportion of time an entity’s attention or activity is concentrated on a particular screen or digital display.
-
B.
timePeriodCategory
Indicates the classification of a time period into a specific category or type (e.g., era, phase, or temporal grouping).
-
C.
screenTimeImportance
Indicates how important or significant the amount of time spent using screens or digital devices is considered in a given context.
-
D.
hasScreenTimeIn
Indicates that an entity appears on screen for a certain duration within a specified audiovisual work or segment.
-
E.
durationCategory
chosen
Indicates the classification of an event or state based on how long it lasts, grouping it into a specific duration range or type.
- 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_69c0082bb19c8190823a4facd3cba79b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c029014588819094a2a0f6f9b66bab |
completed | March 22, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c021c0e0408190ab6c3cd3f907e80f |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:45 p.m.