Triple
T12572997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucas–Kanade optical flow algorithm |
E295649
|
entity |
| Predicate | originalPublicationTitle |
P33185
|
FINISHED |
| Object | An iterative image registration technique with an application to stereo vision |
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: An iterative image registration technique with an application to stereo vision | Statement: [Lucas–Kanade optical flow algorithm, originalPublicationTitle, An iterative image registration technique with an application to stereo vision]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: An iterative image registration technique with an application to stereo vision Context triple: [Lucas–Kanade optical flow algorithm, originalPublicationTitle, An iterative image registration technique with an application to stereo vision]
-
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.
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.
High Resolution Stereo Camera
The High Resolution Stereo Camera is a sophisticated imaging instrument designed to capture detailed, three-dimensional views of planetary surfaces, notably used for mapping and studying Mars.
-
D.
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.
-
E.
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.
- 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_69f65595826081908035655f7930f55a |
completed | May 2, 2026, 7:50 p.m. |
Created at: April 8, 2026, 11:50 p.m.