Triple
T12573054
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kanade–Lucas–Tomasi feature tracker |
E295650
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object |
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.
|
E989632
|
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: Shi–Tomasi corner detector | Statement: [Kanade–Lucas–Tomasi feature tracker, relatedTo, Shi–Tomasi corner detector]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shi–Tomasi corner detector Context triple: [Kanade–Lucas–Tomasi feature tracker, relatedTo, Shi–Tomasi corner detector]
-
A.
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.
-
B.
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.
-
C.
European Conference on Computer Vision
The European Conference on Computer Vision (ECCV) is a leading biennial research conference that showcases cutting-edge advances in computer vision and pattern recognition.
-
D.
ILD detector concept
The ILD detector concept is a proposed high-precision particle physics detector design for the International Linear Collider, optimized for detailed reconstruction of complex collision events.
-
E.
ANSAC
ANSAC is the abbreviation for the Applied and Natural Science Accreditation Commission, a body that accredits applied and natural science degree programs.
- 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: Shi–Tomasi corner detector Triple: [Kanade–Lucas–Tomasi feature tracker, relatedTo, Shi–Tomasi corner detector]
Generated description
The Shi–Tomasi corner detector is a computer vision algorithm that identifies good feature points (corners) in images for robust tracking and recognition tasks.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shi–Tomasi corner detector Target entity description: The Shi–Tomasi corner detector is a computer vision algorithm that identifies good feature points (corners) in images for robust tracking and recognition tasks.
-
A.
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.
-
B.
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.
-
C.
European Conference on Computer Vision
The European Conference on Computer Vision (ECCV) is a leading biennial research conference that showcases cutting-edge advances in computer vision and pattern recognition.
-
D.
ILD detector concept
The ILD detector concept is a proposed high-precision particle physics detector design for the International Linear Collider, optimized for detailed reconstruction of complex collision events.
-
E.
ANSAC
ANSAC is the abbreviation for the Applied and Natural Science Accreditation Commission, a body that accredits applied and natural science degree programs.
- 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_69f657b1b13c8190984300f24c0b2083 |
completed | May 2, 2026, 7:59 p.m. |
Created at: April 8, 2026, 11:50 p.m.