Triple
T3507304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AlexNet |
E74105
|
entity |
| Predicate | competitionOrganizer |
P123
|
FINISHED |
| Object | ImageNet |
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 | Statement: [AlexNet, competitionOrganizer, ImageNet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ImageNet Context triple: [AlexNet, competitionOrganizer, 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.
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.
-
D.
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.
-
E.
Google Brain
Google Brain is a deep learning research team at Google that pioneered many advances in neural networks and artificial intelligence.
- 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc0b635c81909bc95ba2562d8f94 |
completed | March 8, 2026, 6:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b37e6da490819093a5f574ff0b6b00 |
completed | March 13, 2026, 3:03 a.m. |
Created at: March 8, 2026, 3:18 p.m.