Triple
T12573013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucas–Kanade optical flow algorithm |
E295649
|
entity |
| Predicate | implementedIn |
P2539
|
FINISHED |
| Object |
MATLAB Computer Vision Toolbox
MATLAB Computer Vision Toolbox is a specialized add-on for MATLAB that provides algorithms, functions, and tools for designing, simulating, and deploying computer vision and video processing applications.
|
E989303
|
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: MATLAB Computer Vision Toolbox | Statement: [Lucas–Kanade optical flow algorithm, implementedIn, MATLAB Computer Vision Toolbox]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MATLAB Computer Vision Toolbox Context triple: [Lucas–Kanade optical flow algorithm, implementedIn, MATLAB Computer Vision Toolbox]
-
A.
MATLAB
MATLAB is a high-level programming language and interactive environment widely used for numerical computing, data analysis, algorithm development, and visualization, particularly in engineering and scientific research.
-
B.
Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox is a MATLAB add-on that provides functions and apps for statistical analysis, predictive modeling, and machine learning.
-
C.
Parallel Computing Toolbox
Parallel Computing Toolbox is a MATLAB add-on that enables users to perform parallel and distributed computing to accelerate large-scale numerical and data-intensive computations.
-
D.
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.
-
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.
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: MATLAB Computer Vision Toolbox Triple: [Lucas–Kanade optical flow algorithm, implementedIn, MATLAB Computer Vision Toolbox]
Generated description
MATLAB Computer Vision Toolbox is a specialized add-on for MATLAB that provides algorithms, functions, and tools for designing, simulating, and deploying computer vision and video processing applications.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MATLAB Computer Vision Toolbox Target entity description: MATLAB Computer Vision Toolbox is a specialized add-on for MATLAB that provides algorithms, functions, and tools for designing, simulating, and deploying computer vision and video processing applications.
-
A.
MATLAB
MATLAB is a high-level programming language and interactive environment widely used for numerical computing, data analysis, algorithm development, and visualization, particularly in engineering and scientific research.
-
B.
Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox is a MATLAB add-on that provides functions and apps for statistical analysis, predictive modeling, and machine learning.
-
C.
Parallel Computing Toolbox
Parallel Computing Toolbox is a MATLAB add-on that enables users to perform parallel and distributed computing to accelerate large-scale numerical and data-intensive computations.
-
D.
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.
-
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 (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_69f65595826081908035655f7930f55a |
completed | May 2, 2026, 7:50 p.m. |
| NEDg | Description generation | batch_69f656a86ff48190bd3debd30e11df80 |
completed | May 2, 2026, 7:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f657aa1bf48190a884e0dfce31e30e |
completed | May 2, 2026, 7:59 p.m. |
Created at: April 8, 2026, 11:50 p.m.