Triple
T15532268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shaoqing Ren |
E370248
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object | ResNet |
E74928
|
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: ResNet | Statement: [Shaoqing Ren, knownFor, ResNet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ResNet Context triple: [Shaoqing Ren, knownFor, ResNet]
-
A.
ResNet
chosen
ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
-
B.
NASNet
NASNet is a family of convolutional neural network architectures automatically discovered via neural architecture search, known for achieving state-of-the-art performance on image classification benchmarks.
-
C.
DenseNet
DenseNet is a family of convolutional neural network architectures characterized by densely connected layers that improve information flow and parameter efficiency for image recognition tasks.
-
D.
GoogLeNet
GoogLeNet is a deep convolutional neural network developed by Google that popularized the Inception architecture and achieved state-of-the-art performance in image recognition tasks.
-
E.
ResNeXt
ResNeXt is a deep convolutional neural network architecture that extends ResNet by using grouped convolutions and a split-transform-merge strategy to improve accuracy and efficiency in image recognition 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_69d85cc521a08190921fb50319dddc34 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e0414877d88190804ee76566004e13 |
completed | April 16, 2026, 1:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d5e82a48190bb0a10ebc2412129 |
completed | May 9, 2026, 1:57 p.m. |
Created at: April 10, 2026, 4:06 a.m.