Triple
T9674943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MXNet |
E234123
|
entity |
| Predicate | hasAPI |
P182
|
FINISHED |
| Object | NDArray API |
E814032
|
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: NDArray API | Statement: [MXNet, hasAPI, NDArray API]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: NDArray API Context triple: [MXNet, hasAPI, NDArray API]
-
A.
NDArray API
chosen
The NDArray API is MXNet’s core multi-dimensional array interface for efficient numerical computation and deep learning operations.
-
B.
NumPy
NumPy is a fundamental Python library that provides efficient multi-dimensional arrays and numerical computing tools widely used in scientific computing and data analysis.
-
C.
tensor programs framework
The tensor programs framework is a theoretical approach developed by Greg Yang that rigorously analyzes and characterizes the behavior and scaling limits of large neural networks using tools from probability and random matrix theory.
-
D.
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.
-
E.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
- 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_69ca848f55e48190b3f67252571c3d45 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9c6d6dd48190a77c486337a58cb6 |
completed | April 1, 2026, 10:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d190f9bf78819093542adae997a668 |
completed | April 4, 2026, 10:30 p.m. |
Created at: March 30, 2026, 8:15 p.m.