Triple
T19203648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yangqing Jia |
E480174
|
entity |
| Predicate | contributedTo |
P37
|
FINISHED |
| Object | Caffe2 |
—
|
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: Caffe2 | Statement: [Yangqing Jia, contributedTo, Caffe2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caffe2 Context triple: [Yangqing Jia, contributedTo, Caffe2]
-
A.
Caffe2
chosen
Caffe2 is a lightweight, modular deep learning framework developed by Facebook (Meta) designed for scalable training and deployment of neural networks on mobile and large-scale production environments.
-
B.
PaddlePaddle
PaddlePaddle is an open-source deep learning platform developed by Baidu, designed for large-scale distributed training and deployment of neural networks.
-
C.
NVIDIA TensorRT
NVIDIA TensorRT is a high-performance deep learning inference optimizer and runtime library designed to accelerate AI models on NVIDIA GPUs in production environments.
-
D.
PyTorch
PyTorch is an open-source deep learning framework widely used for building and training neural networks, known for its dynamic computation graph and strong support for research and production in Python.
-
E.
TensorFlow Metal
TensorFlow Metal is an integration that enables TensorFlow to run efficiently on Apple GPUs via the Metal framework, accelerating machine learning workloads on macOS and iOS devices.
- 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_69d8e8cb8c348190b52075823911c869 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5f99b37c081908c13e0b4cca52aa4 |
completed | April 20, 2026, 10:02 a.m. |
Created at: April 10, 2026, 1:15 p.m.