Triple
T3507273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AlexNet |
E74105
|
entity |
| Predicate | competition |
P563
|
FINISHED |
| Object | ImageNet Large Scale Visual Recognition Challenge |
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 Large Scale Visual Recognition Challenge | Statement: [AlexNet, competition, ImageNet Large Scale Visual Recognition Challenge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ImageNet Large Scale Visual Recognition Challenge Context triple: [AlexNet, competition, ImageNet Large Scale Visual Recognition Challenge]
-
A.
ImageNet
chosen
ImageNet is a large-scale visual database widely used for training and benchmarking image classification and computer vision algorithms.
-
B.
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
"Long-term Recurrent Convolutional Networks for Visual Recognition and Description" is a research paper that introduces a deep learning architecture combining convolutional and recurrent neural networks to perform tasks like video recognition and automatic image or video captioning.
-
C.
Inception architecture
The Inception architecture is a deep convolutional neural network design that introduced parallel multi-scale processing modules to achieve state-of-the-art image recognition performance with improved computational efficiency.
-
D.
Scene Completion Using Millions of Photographs
"Scene Completion Using Millions of Photographs" is a seminal computer vision and graphics paper that introduced a data-driven method for automatically filling in missing regions of images by searching a massive online photo collection for visually compatible patches.
-
E.
Show and Tell: A Neural Image Caption Generator
"Show and Tell: A Neural Image Caption Generator" is a pioneering deep learning model that automatically generates natural-language descriptions for images by combining convolutional and recurrent neural networks.
- 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.