Triple
T6730572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scott Evil |
E153622
|
entity |
| Predicate | screenTimeType |
P62983
|
FINISHED |
| Object | supporting character |
—
|
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 character | Statement: [Scott Evil, screenTimeType, supporting character]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: screenTimeType Context triple: [Scott Evil, screenTimeType, supporting character]
-
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.
hasScreenTimeIn
chosen
Indicates that an entity appears on screen for a certain duration within a specified audiovisual work or segment.
-
C.
timeType
Indicates the specific temporal category or classification associated with a time-related entity or value (e.g., duration, point in time, interval, or recurrence type).
-
D.
screenTimeImportance
Indicates how important or significant the amount of time spent using screens or digital devices is considered in a given context.
-
E.
propertyType_ptime
Indicates that a property has an associated time-related characteristic or classification (e.g., duration, timestamp, or temporal category).
- 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_69c6880bdd68819097de8b6099992682 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d354177481908ab3cf5437c095e2 |
completed | March 27, 2026, 6:58 p.m. |
| PD | Predicate disambiguation | batch_69c6d08e8a2c8190ae4e8d8c039be7ce |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:09 p.m.