Triple

T32643599
Position Surface form Disambiguated ID Type / Status
Subject Isabel (Lower City) E834543 entity
Predicate hasScreenTimeCharacteristic P189735 FINISHED
Object appears throughout most of the film 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: appears throughout most of the film | Statement: [Isabel (Lower City), hasScreenTimeCharacteristic, appears throughout most of the film]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasScreenTimeCharacteristic
Context triple: [Isabel (Lower City), hasScreenTimeCharacteristic, appears throughout most of the film]
  • A. hasScreenTimeIn
    Indicates that an entity appears on screen for a certain duration within a specified audiovisual work or segment.
  • B. hasScreenTimeType
    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. screenTimeProportion
    Indicates the proportion of total time that an entity spends looking at or using a screen relative to a defined overall time period.
  • E. screenTimeRelation
    Indicates a relationship between entities based on the amount, duration, or pattern of time spent using screens or digital devices.
  • 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_69f3492e773c81908afc10651e46cad3 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69fbca6c066c8190a1599202f341417f completed May 6, 2026, 11:10 p.m.
PD Predicate disambiguation batch_69fbc8ec03ac8190a757563f96fab283 completed May 6, 2026, 11:04 p.m.
PDg Predicate description generation batch_69fbc9d0854c8190aa00093274afebb8 completed May 6, 2026, 11:08 p.m.
Created at: May 1, 2026, 1:07 a.m.