Triple

T3884390
Position Surface form Disambiguated ID Type / Status
Subject Blackmail (1929 film) E92902 entity
Predicate featuresTechnique P51617 FINISHED
Object subjective point-of-view shots 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: subjective point-of-view shots | Statement: [Blackmail (1929 film), featuresTechnique, subjective point-of-view shots]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: featuresTechnique
Context triple: [Blackmail (1929 film), featuresTechnique, subjective point-of-view shots]
  • A. technologicalFeature
    Indicates that one entity possesses, exhibits, or is characterized by a specific technological capability, component, or functionality in relation to another entity.
  • B. featuresMethod
    Indicates that an entity includes or provides a particular method as part of its functionality or behavior.
  • C. featuresSample
    Indicates that an entity includes or presents a particular sample as one of its components or examples.
  • D. featuresSuit
    Indicates that one entity includes or presents a particular suit (e.g., clothing, armor, or outfit) as a notable component or attribute.
  • E. engineeringFeature
    Indicates that one entity serves as an engineering-related feature, component, or characteristic of another entity within a technical or designed system.
  • 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_69aed9697de0819087c2559295ff3d12 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69aeec9029908190a7b36a3827734db1 completed March 9, 2026, 3:51 p.m.
PD Predicate disambiguation batch_69aee759609c8190985e96ec6d96dedd completed March 9, 2026, 3:29 p.m.
PDg Predicate description generation batch_69aee80858a481909961a33fb50ff8d1 completed March 9, 2026, 3:32 p.m.
Created at: March 9, 2026, 3:20 p.m.