Triple
T19693271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Inception v1 |
E472887
|
entity |
| Predicate | trainingDataset |
P21227
|
FINISHED |
| Object | ImageNet |
—
|
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: ImageNet | Statement: [Inception v1, trainingDataset, ImageNet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ImageNet Context triple: [Inception v1, trainingDataset, ImageNet]
-
A.
ImageNet
chosen
ImageNet is a large-scale visual database widely used for training and benchmarking image classification and computer vision algorithms.
-
B.
SVRC
SVRC is the abbreviation for the Singapore Volunteer Rifle Corps, a 19th-century volunteer military unit formed to bolster the defense of colonial Singapore.
-
C.
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.
-
D.
PASCAL VOC
PASCAL VOC is a benchmark dataset and challenge in computer vision, widely used for evaluating algorithms on tasks like object detection and image segmentation.
-
E.
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.
- 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.