Triple
T20524162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emma Jacklin |
E503889
|
entity |
| Predicate | hasScreenTimeContext |
P123671
|
FINISHED |
| Object | major role in The Turning Point |
—
|
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: major role in The Turning Point | Statement: [Emma Jacklin, hasScreenTimeContext, major role in The Turning Point]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasScreenTimeContext Context triple: [Emma Jacklin, hasScreenTimeContext, major role in The Turning Point]
-
A.
hasScreenTimeIn
Indicates that an entity appears on screen for a certain duration within a specified audiovisual work or segment.
-
B.
hasScreenTimeType
chosen
Indicates the type or category of screen time associated with an entity (e.g., usage mode, content type, or context of screen use).
-
C.
screenTime
Indicates the amount of time an entity spends viewing or interacting with a screen-based device.
-
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_69e0b4b3a6e08190ae663701f50fab8e |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69f48691c8190af0ac959e92e10d9 |
completed | April 20, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69e59fdb7ad88190924176c32a195db3 |
completed | April 20, 2026, 3:39 a.m. |
Created at: April 16, 2026, 11:36 a.m.