Triple
T18178619
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tile IR |
E435224
|
entity |
| Predicate | usedBy |
P260
|
FINISHED |
| Object | PlaidML optimizer |
—
|
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: PlaidML optimizer | Statement: [Tile IR, usedBy, PlaidML optimizer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PlaidML optimizer Context triple: [Tile IR, usedBy, PlaidML optimizer]
-
A.
PlaidML
chosen
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.
-
B.
Adam optimizer
The Adam optimizer is a popular stochastic gradient descent method in machine learning that adaptively adjusts learning rates for each parameter using estimates of first and second moments of gradients.
-
C.
DeepSpeed
DeepSpeed is a deep learning optimization library from Microsoft that enables efficient, large-scale training of models across distributed GPU systems.
-
D.
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.
-
E.
AdaDelta
AdaDelta is an adaptive learning rate optimization algorithm for training neural networks that improves upon methods like RMSProp by eliminating the need to manually set a global learning rate.
- 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_69d8b90c7ec081909b4694ccecb449c6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4df5b68f081908aac8210270f1499 |
completed | April 19, 2026, 1:57 p.m. |
Created at: April 10, 2026, 10:31 a.m.