Triple
T18016703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | XLA |
E431013
|
entity |
| Predicate | integratedWith |
P2830
|
FINISHED |
| Object | PyTorch (via experimental backends and projects) |
—
|
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: PyTorch (via experimental backends and projects) | Statement: [XLA, integratedWith, PyTorch (via experimental backends and projects)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PyTorch (via experimental backends and projects) Context triple: [XLA, integratedWith, PyTorch (via experimental backends and projects)]
-
A.
PyTorch
chosen
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.
-
B.
torchvision (ecosystem)
torchvision is a PyTorch-based computer vision library providing datasets, model architectures, and image transformations commonly used for training and evaluating deep learning models.
-
C.
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.
-
D.
Caffe2
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.
-
E.
NVIDIA Triton Inference Server
NVIDIA Triton Inference Server is an open-source, production-ready platform for serving and scaling AI model inference across GPUs and CPUs with support for multiple frameworks and deployment environments.
- 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b9be5d0c819097e006f32d98753a |
completed | April 19, 2026, 11:17 a.m. |
Created at: April 10, 2026, 10:24 a.m.