Triple
T1635160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CIFAR |
E35343
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | CIFAR |
E35343
|
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: CIFAR | Statement: [CIFAR, abbreviation, CIFAR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CIFAR Context triple: [CIFAR, abbreviation, CIFAR]
-
A.
CIFAR
chosen
CIFAR (the Canadian Institute for Advanced Research) is a Canadian global research organization that supports long-term, collaborative, interdisciplinary research, including major initiatives in artificial intelligence.
-
B.
MNIST
MNIST is a widely used benchmark dataset of handwritten digit images commonly employed for training and evaluating image classification algorithms in machine learning and computer vision.
-
C.
LeNet
LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
-
D.
CLIP
CLIP is an OpenAI model that learns joint representations of images and text, enabling tasks like zero-shot image classification and natural language-based image retrieval.
-
E.
ResNet
ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
- 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_69a886036bc081909ff5de16dbe5e8ea |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90a1679408190a9faa7b22c388c4b |
completed | March 5, 2026, 4:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad58dbd0608190be207ab2bcdc9eef |
completed | March 8, 2026, 11:09 a.m. |
Created at: March 4, 2026, 7:28 p.m.