Triple
T5910835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alex Krizhevsky |
E131452
|
entity |
| Predicate | usedDataset |
P11520
|
FINISHED |
| Object | ImageNet |
E363692
|
NE FINISHED |
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: ImageNet | Statement: [Alex Krizhevsky, usedDataset, ImageNet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ImageNet Context triple: [Alex Krizhevsky, usedDataset, ImageNet]
-
A.
ImageNet
chosen
ImageNet is a large-scale visual database widely used for training and benchmarking image classification and computer vision algorithms.
-
B.
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.
-
C.
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.
-
D.
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.
-
E.
AlexNet
AlexNet is a pioneering deep convolutional neural network architecture that dramatically advanced image recognition performance and helped spark the modern deep learning revolution after winning the 2012 ImageNet competition.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedDataset Context triple: [Alex Krizhevsky, usedDataset, ImageNet]
-
A.
usedDataFrom
chosen
Indicates that one entity utilized or relied on data originating from another entity.
-
B.
evaluationDataset
Indicates that a dataset is used as a benchmark or test set for evaluating the performance or quality of a system, model, or method.
-
C.
usedAt
Indicates that something is employed, applied, or utilized at a particular place, time, or context.
-
D.
usedCross
Indicates that one entity made use of a cross-shaped object or structure, or traversed by means of a crossing point such as a crosswalk or intersection.
-
E.
usedDocument
Indicates that one entity has employed, referenced, or otherwise made use of a particular document in performing an action or fulfilling a purpose.
- F. None of above.
Provenance (4 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_69c008593a44819081a07ae0efe6c574 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c048fc112c8190b905bf561c9de096 |
completed | March 22, 2026, 7:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b17375488190a3053d37712501b3 |
completed | March 23, 2026, 3:20 a.m. |
| PD | Predicate disambiguation | batch_69c03352208c8190968efed05a9fd416 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:59 p.m.