Triple
T2045513
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple M2 Pro |
E45440
|
entity |
| Predicate | feature |
P374
|
FINISHED |
| Object | Neural Engine |
E38961
|
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: Neural Engine | Statement: [Apple M2 Pro, feature, Neural Engine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Neural Engine Context triple: [Apple M2 Pro, feature, Neural Engine]
-
A.
Apple Neural Engine
chosen
Apple Neural Engine is Apple’s dedicated on-chip hardware accelerator designed to efficiently perform machine learning and AI computations on its devices.
-
B.
Tensor Cores
Tensor Cores are specialized processing units in NVIDIA GPUs designed to accelerate matrix operations for deep learning and AI workloads.
-
C.
NVIDIA Ampere architecture
NVIDIA Ampere architecture is a GPU microarchitecture from NVIDIA that powers RTX 30-series graphics cards, delivering significant improvements in ray tracing, AI performance, and power efficiency over previous generations.
-
D.
NVIDIA DRIVE
NVIDIA DRIVE is NVIDIA’s automotive computing platform designed to power advanced driver-assistance systems and autonomous driving capabilities in vehicles.
-
E.
NVIDIA Jetson embedded modules
NVIDIA Jetson embedded modules are compact, power-efficient computing platforms designed for edge AI and robotics applications, integrating GPU-accelerated processing for tasks like computer vision and deep learning.
- 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_69a8891948208190ab7898da21824c77 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb9728f688190939d7c4df524f9b4 |
completed | March 7, 2026, 5:36 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae200125f081909ab40b6a04adaa25 |
completed | March 9, 2026, 1:18 a.m. |
Created at: March 4, 2026, 7:39 p.m.