Triple
T18205455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hugging Face Accelerate |
E435889
|
entity |
| Predicate | supportsBackend |
P15794
|
FINISHED |
| Object | XLA |
—
|
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: XLA | Statement: [Hugging Face Accelerate, supportsBackend, XLA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: XLA Context triple: [Hugging Face Accelerate, supportsBackend, XLA]
-
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.
OpenXLA project
The OpenXLA project is an open-source ecosystem for optimizing and compiling machine learning models across diverse hardware backends, built around and extending the XLA compiler technology.
-
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.
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.
-
E.
jax.experimental
jax.experimental is a submodule of the JAX library that provides access to experimental, unstable, or cutting-edge numerical and machine learning features not yet part of the stable API.
- 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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e2234b988190bbe2c2164d61f65f |
completed | April 19, 2026, 2:09 p.m. |
Created at: April 10, 2026, 10:32 a.m.