Triple
T19693165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GoogLeNet |
E472885
|
entity |
| Predicate | paperVenue |
P53492
|
FINISHED |
| Object | CVPR 2015 |
—
|
NE NERFINISHED |
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: CVPR 2015 | Statement: [GoogLeNet, paperVenue, CVPR 2015]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CVPR 2015 Context triple: [GoogLeNet, paperVenue, CVPR 2015]
-
A.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition
chosen
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.
-
B.
IEEE International Conference on Computer Vision
The IEEE International Conference on Computer Vision (ICCV) is a premier biennial research conference that showcases cutting-edge advances in computer vision and pattern recognition.
-
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.
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.
-
E.
Proceedings of Imaging Understanding Workshop
Proceedings of Imaging Understanding Workshop is a research conference publication focused on advances in computer vision and image understanding.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d8e515bef88190bc30781aea50537a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e64211e5d481908358d922e0dca271 |
completed | April 20, 2026, 3:11 p.m. |
Created at: April 10, 2026, 1:46 p.m.