Triple
T15361430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ResNeXt |
E367297
|
entity |
| Predicate | evaluatedOn |
P82415
|
FINISHED |
| Object | ImageNet dataset |
E363692
|
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: ImageNet dataset | Statement: [ResNeXt, evaluatedOn, ImageNet dataset]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ImageNet dataset Context triple: [ResNeXt, evaluatedOn, ImageNet dataset]
-
A.
ImageNet
chosen
ImageNet is a large-scale visual database widely used for training and benchmarking image classification and computer vision algorithms.
-
B.
CIFAR-10
CIFAR-10 is a widely used computer vision dataset of 60,000 labeled low-resolution images across 10 object classes, commonly employed to benchmark image classification algorithms.
-
C.
CIFAR-100
CIFAR-100 is a widely used image classification dataset consisting of 60,000 32×32 color images across 100 object categories, commonly used to benchmark machine learning models.
-
D.
CIFAR
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.
-
E.
ImageNet CNN
ImageNet CNN is a convolutional neural network model trained on the large-scale ImageNet dataset, commonly used as a powerful pretrained feature extractor for various computer vision 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e4607408190ab281a7f7a8012d3 |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff0b4a181c8190bffc1ac1a86e215d |
completed | May 9, 2026, 10:24 a.m. |
Created at: April 10, 2026, 3:18 a.m.