Triple
T4326117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TPUs (via XLA integrations) |
E96636
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | PyTorch/XLA project ecosystem |
E431013
|
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: PyTorch/XLA project ecosystem | Statement: [TPUs (via XLA integrations), partOf, PyTorch/XLA project ecosystem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PyTorch/XLA project ecosystem Context triple: [TPUs (via XLA integrations), partOf, PyTorch/XLA project ecosystem]
-
A.
XLA
chosen
XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra that optimizes and accelerates machine learning computations on hardware such as TPUs and GPUs.
-
B.
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.
-
C.
TPUs (via XLA integrations)
TPUs (via XLA integrations) are Google's specialized tensor processing units that can be used as accelerators for PyTorch models through the XLA compilation framework.
-
D.
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.
-
E.
PlaidML
PlaidML is an open-source, hardware-agnostic deep learning engine designed to accelerate neural network computation on a wide range of GPUs and other devices.
- 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_69b34542fd908190b11b08faad8decfd |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3513020f481909ff2fec3934f3002 |
completed | March 12, 2026, 11:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5db98ae888190aac5b5b7839ae7dd |
completed | March 14, 2026, 10:05 p.m. |
Created at: March 12, 2026, 11:13 p.m.