Triple
T16635824
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Penelope |
E404199
|
entity |
| Predicate | hasScreenTimeType |
P123671
|
FINISHED |
| Object | recurring main 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: recurring main character | Statement: [Penelope, hasScreenTimeType, recurring main character]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasScreenTimeType Context triple: [Penelope, hasScreenTimeType, recurring main character]
-
A.
hasScreenTimeIn
Indicates that an entity appears on screen for a certain duration within a specified audiovisual work or segment.
-
B.
screenTime
Indicates the amount of time an entity spends viewing or interacting with a screen-based device.
-
C.
screenTimeRelation
Indicates a relationship between entities based on the amount, duration, or pattern of time spent using screens or digital devices.
-
D.
hasScreenType
Indicates the specific kind or category of screen associated with or used by an entity.
-
E.
screenTimeEra
Indicates the period or era during which a particular screen-based technology, medium, or usage pattern is prevalent or characteristic.
- F. None of above. chosen
Provenance (4 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_69d8838a41f08190b0c3f79c47df5078 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e378e999d48190bff680040dbc883d |
completed | April 18, 2026, 12:28 p.m. |
| PD | Predicate disambiguation | batch_69e296ad3f148190af09223dc35b155c |
completed | April 17, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69e2d7fb02f481908885a226c2191231 |
completed | April 18, 2026, 1:01 a.m. |
Created at: April 10, 2026, 5:17 a.m.