Triple

T5454331
Position Surface form Disambiguated ID Type / Status
Subject VistaVision E122441 entity
Predicate advantageOverStandard35mm P64127 FINISHED
Object higher negative resolution for reduction printing 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: higher negative resolution for reduction printing | Statement: [VistaVision, advantageOverStandard35mm, higher negative resolution for reduction printing]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: advantageOverStandard35mm
Context triple: [VistaVision, advantageOverStandard35mm, higher negative resolution for reduction printing]
  • A. hasLongFocalLength
    Indicates that one entity possesses or is characterized by a focal length that is relatively long compared to a standard or reference.
  • B. hasWideCamera
    Indicates that an entity is equipped with or features a wide-angle camera.
  • C. telephotoCameraResolution
    Indicates the image resolution capability of a device’s telephoto camera in a given context.
  • D. typicalMagnification
    Indicates the usual or characteristic degree to which something is enlarged or magnified under normal or standard conditions.
  • E. focalLength
    Indicates the distance between a lens or mirror and its focal point, determining how strongly it converges or diverges light.
  • 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_69bd46424248819085282ddf50a565f3 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd927c946c8190aef40679199fede3 completed March 20, 2026, 6:31 p.m.
PD Predicate disambiguation batch_69bd91a0d96c8190bd1299edbf764bbb completed March 20, 2026, 6:27 p.m.
PDg Predicate description generation batch_69bd927b0b4c81909d5e0f594822e3f9 completed March 20, 2026, 6:31 p.m.
Created at: March 20, 2026, 2:08 p.m.