Triple

T12573025
Position Surface form Disambiguated ID Type / Status
Subject Kanade–Lucas–Tomasi feature tracker E295650 entity
Predicate basedOn P98 FINISHED
Object Lucas–Kanade optical flow method E295649 NE 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: Lucas–Kanade optical flow method | Statement: [Kanade–Lucas–Tomasi feature tracker, basedOn, Lucas–Kanade optical flow method]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lucas–Kanade optical flow method
Context triple: [Kanade–Lucas–Tomasi feature tracker, basedOn, Lucas–Kanade optical flow method]
  • A. Lucas–Kanade optical flow algorithm chosen
    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.
  • B. Horn–Schunck optical flow method
    The Horn–Schunck optical flow method is a classic global variational approach in computer vision that estimates dense motion fields between image frames by enforcing both brightness constancy and smoothness constraints.
  • C. Kanade–Lucas–Tomasi feature tracker
    The Kanade–Lucas–Tomasi feature tracker is a widely used computer vision algorithm for robustly tracking distinctive image features across video frames, building on the Lucas–Kanade optical flow method with Tomasi’s feature selection criteria.
  • D. Shi–Tomasi corner detector
    The Shi–Tomasi corner detector is a computer vision algorithm that identifies good feature points (corners) in images for robust tracking and recognition tasks.
  • E. Ada Optical Flow Accelerator
    The Ada Optical Flow Accelerator is a dedicated hardware engine in NVIDIA’s Ada Lovelace GPUs that rapidly computes high-quality motion vectors to enhance tasks like video processing, frame interpolation, and AI-powered motion analysis.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6ad9cac2c81908e8a7bed82d1e21d completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d954a52c788190beac128a97e34dc1 completed April 10, 2026, 7:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69f65eb8ec888190b46a0b48840efd20 completed May 2, 2026, 8:29 p.m.
Created at: April 8, 2026, 11:50 p.m.