Triple

T2739074
Position Surface form Disambiguated ID Type / Status
Subject Takeo Kanade E60704 entity
Predicate knownFor P22 FINISHED
Object 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.
E295649 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: Lucas–Kanade optical flow algorithm | Statement: [Takeo Kanade, knownFor, Lucas–Kanade optical flow algorithm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lucas–Kanade optical flow algorithm
Context triple: [Takeo Kanade, knownFor, Lucas–Kanade optical flow algorithm]
  • A. SLAM
    SLAM is a major art museum in St. Louis, Missouri, renowned for its extensive collection spanning thousands of years and diverse cultures.
  • B. IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    The IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) is a premier annual international research conference showcasing cutting-edge advances in computer vision, machine learning, and pattern recognition.
  • C. Gradient-based learning applied to document recognition
    "Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
  • D. Kurzweil OCR (optical character recognition) systems
    Kurzweil OCR (optical character recognition) systems are pioneering software tools that convert printed text into digital, machine-readable form, widely used for document digitization and accessibility for the visually impaired.
  • E. Kirchhoff diffraction theory
    Kirchhoff diffraction theory is a classical wave optics framework that models light propagation and diffraction by treating wavefronts as superpositions of secondary spherical waves emitted from an aperture.
  • 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: Lucas–Kanade optical flow algorithm
Triple: [Takeo Kanade, knownFor, Lucas–Kanade optical flow algorithm]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Lucas–Kanade optical flow algorithm
Target entity description: 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.
  • A. SLAM
    SLAM is a major art museum in St. Louis, Missouri, renowned for its extensive collection spanning thousands of years and diverse cultures.
  • B. IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    The IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) is a premier annual international research conference showcasing cutting-edge advances in computer vision, machine learning, and pattern recognition.
  • C. Gradient-based learning applied to document recognition
    "Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
  • D. Kurzweil OCR (optical character recognition) systems
    Kurzweil OCR (optical character recognition) systems are pioneering software tools that convert printed text into digital, machine-readable form, widely used for document digitization and accessibility for the visually impaired.
  • E. Kirchhoff diffraction theory
    Kirchhoff diffraction theory is a classical wave optics framework that models light propagation and diffraction by treating wavefronts as superpositions of secondary spherical waves emitted from an aperture.
  • 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_69ab4b77febc819095603eb012cd141b completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdb147a588190829b74fe05b3a114 completed March 7, 2026, 8 a.m.
NED1 Entity disambiguation (via context triple) batch_69afbbc607d88190bc35ce56ac26dbf9 completed March 10, 2026, 6:35 a.m.
NEDg Description generation batch_69afbce109f48190be1a31d9300dbee6 completed March 10, 2026, 6:40 a.m.
NED2 Entity disambiguation (via description) batch_69afbda490ac8190bb12598e26b91677 completed March 10, 2026, 6:43 a.m.
Created at: March 6, 2026, 9:56 p.m.