Triple
T18200461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dana Ballard |
E435766
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Computer Vision (textbook) |
—
|
NE NERFINISHED |
How this triple was built (3 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: Computer Vision (textbook) | Statement: [Dana Ballard, notableWork, Computer Vision (textbook)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Computer Vision (textbook) Context triple: [Dana Ballard, notableWork, Computer Vision (textbook)]
-
A.
The Psychology of Computer Vision (edited volume)
The Psychology of Computer Vision is an influential edited volume, compiled by Patrick Henry Winston, that brings together foundational research exploring how principles of human perception and cognition can inform and advance computer vision.
-
B.
Learning to See
"Learning to See" is an autobiographical essay by Eudora Welty that reflects on how her early experiences and observations shaped her development as a writer.
-
C.
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.
-
D.
Deep Learning (book)
Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
-
E.
ImageNet Classification with Deep Convolutional Neural Networks
"ImageNet Classification with Deep Convolutional Neural Networks" is the landmark 2012 research paper that introduced the deep CNN model AlexNet, demonstrating a dramatic leap in image recognition performance on the ImageNet benchmark and catalyzing the modern deep learning revolution in computer vision.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Computer Vision (textbook) Target entity description: Computer Vision is an influential textbook by Dana Ballard that provides a foundational, mathematically grounded introduction to the principles and algorithms underlying machine perception and image understanding.
-
A.
The Psychology of Computer Vision (edited volume)
The Psychology of Computer Vision is an influential edited volume, compiled by Patrick Henry Winston, that brings together foundational research exploring how principles of human perception and cognition can inform and advance computer vision.
-
B.
Learning to See
"Learning to See" is an autobiographical essay by Eudora Welty that reflects on how her early experiences and observations shaped her development as a writer.
-
C.
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.
-
D.
Deep Learning (book)
Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
-
E.
ImageNet Classification with Deep Convolutional Neural Networks
"ImageNet Classification with Deep Convolutional Neural Networks" is the landmark 2012 research paper that introduced the deep CNN model AlexNet, demonstrating a dramatic leap in image recognition performance on the ImageNet benchmark and catalyzing the modern deep learning revolution in computer vision.
- F. None of above. chosen
Provenance (2 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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e0d610f88190b4f69b1c433ea6b1 |
completed | April 19, 2026, 2:04 p.m. |
Created at: April 10, 2026, 10:31 a.m.