Triple

T12573055
Position Surface form Disambiguated ID Type / Status
Subject Kanade–Lucas–Tomasi feature tracker E295650 entity
Predicate relatedTo P37 FINISHED
Object Harris corner detector
The Harris corner detector is a foundational computer vision algorithm used to identify interest points or corners in images for tasks like tracking, matching, and 3D reconstruction.
 E992626 NE FINISHED

How this triple was built (4 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: Harris corner detector | Statement: [Kanade–Lucas–Tomasi feature tracker, relatedTo, Harris corner detector]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Harris corner detector
Context triple: [Kanade–Lucas–Tomasi feature tracker, relatedTo, Harris corner detector]
  • A. 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.
  • B. 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.
  • C. 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.
  • D. 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.
  • E. Prewitt
    Prewitt is a surname of English origin borne by various notable individuals across fields such as academia, politics, and the arts.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Harris corner detector
Triple: [Kanade–Lucas–Tomasi feature tracker, relatedTo, Harris corner detector]
Generated description
The Harris corner detector is a foundational computer vision algorithm used to identify interest points or corners in images for tasks like tracking, matching, and 3D reconstruction.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Harris corner detector
Target entity description: The Harris corner detector is a foundational computer vision algorithm used to identify interest points or corners in images for tasks like tracking, matching, and 3D reconstruction.
  • A. 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.
  • B. 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.
  • C. 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.
  • D. 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.
  • E. Prewitt
    Prewitt is a surname of English origin borne by various notable individuals across fields such as academia, politics, and the arts.
  • F. None of above. chosen

Provenance (5 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.
NEDg Description generation batch_69f660294004819089714099a08085f6 completed May 2, 2026, 8:35 p.m.
NED2 Entity disambiguation (via description) batch_69f6613ce1108190851cf8491fe666c8 completed May 2, 2026, 8:40 p.m.
Created at: April 8, 2026, 11:50 p.m.