Triple

T21536048
Position Surface form Disambiguated ID Type / Status
Subject House of Wax E531351 entity
Predicate filmProcess P24651 FINISHED
Object Natural Vision 3D NE NERFINISHED

How this triple was built (3 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: Natural Vision 3D | Statement: [House of Wax, filmProcess, Natural Vision 3D]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Natural Vision 3D
Context triple: [House of Wax, filmProcess, Natural Vision 3D]
  • A. multiple view geometry in computer vision
    Multiple view geometry in computer vision is a foundational field that studies the mathematical relationships between multiple images of a scene to enable tasks like 3D reconstruction, camera calibration, and motion estimation.
  • B. Learning to See
    "Learning to See" is an autobiographical essay by Eudora Welty that reflects on how her early experiences and observations shaped her development as a writer.
  • C. Surface Stereo Imager
    The Surface Stereo Imager is a camera system on NASA's Phoenix Mars lander designed to capture high-resolution, three-dimensional images of the Martian surface and atmosphere.
  • D. Wide Angle Topographic Sensor for Operations and eNgineering
    Wide Angle Topographic Sensor for Operations and eNgineering (WATSON) is a Mars rover camera system designed to capture detailed close-up and wide-angle images of the Martian surface for scientific analysis and engineering operations.
  • E. Lucas–Kanade optical flow algorithm
    The Lucas–Kanade optical flow algorithm is a widely used computer vision method for estimating the motion of features between consecutive images by assuming locally constant motion and solving a least-squares problem.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Natural Vision 3D
Target entity description: Natural Vision 3D is an early stereoscopic film process used in the 1950s that projected separate left- and right-eye images to create a three-dimensional viewing experience.
  • A. multiple view geometry in computer vision
    Multiple view geometry in computer vision is a foundational field that studies the mathematical relationships between multiple images of a scene to enable tasks like 3D reconstruction, camera calibration, and motion estimation.
  • B. Learning to See
    "Learning to See" is an autobiographical essay by Eudora Welty that reflects on how her early experiences and observations shaped her development as a writer.
  • C. Surface Stereo Imager
    The Surface Stereo Imager is a camera system on NASA's Phoenix Mars lander designed to capture high-resolution, three-dimensional images of the Martian surface and atmosphere.
  • D. Wide Angle Topographic Sensor for Operations and eNgineering
    Wide Angle Topographic Sensor for Operations and eNgineering (WATSON) is a Mars rover camera system designed to capture detailed close-up and wide-angle images of the Martian surface for scientific analysis and engineering operations.
  • E. Lucas–Kanade optical flow algorithm
    The Lucas–Kanade optical flow algorithm is a widely used computer vision method for estimating the motion of features between consecutive images by assuming locally constant motion and solving a least-squares problem.
  • F. None of above. chosen

Provenance (2 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_69e0c45e5b8881908ac18fc2f493b114 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ee9d0ce8d08190b3233d7117a9b1ca completed April 26, 2026, 11:17 p.m.
Created at: April 16, 2026, 6:27 p.m.