Triple
T4425388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | JAX |
E95194
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object |
jax.random
jax.random is JAX’s module for generating and manipulating pseudo-random numbers in a functional, reproducible way using PRNG keys.
|
E438359
|
NE FINISHED |
How this triple was built (4 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: jax.random | Statement: [JAX, hasComponent, jax.random]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: jax.random Context triple: [JAX, hasComponent, jax.random]
-
A.
cuRAND
cuRAND is NVIDIA's GPU-accelerated random number generation library designed to efficiently produce high-quality random numbers for parallel applications using CUDA.
-
B.
Random
Random is a fictional character who serves as the main protagonist in the story "Out of the Blue."
-
C.
Random
Random is a Julia standard library module that provides functionality for generating and manipulating random numbers and random processes.
-
D.
Chainer
Chainer is an open-source deep learning framework for Python that pioneered a flexible "define-by-run" computation graph approach to building neural networks.
-
E.
Theano
Theano is an open-source numerical computation library for Python that allows efficient definition, optimization, and evaluation of mathematical expressions, particularly those involving multi-dimensional arrays, and was widely used as a backend for deep learning frameworks.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: jax.random Triple: [JAX, hasComponent, jax.random]
Generated description
jax.random is JAX’s module for generating and manipulating pseudo-random numbers in a functional, reproducible way using PRNG keys.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: jax.random Target entity description: jax.random is JAX’s module for generating and manipulating pseudo-random numbers in a functional, reproducible way using PRNG keys.
-
A.
cuRAND
cuRAND is NVIDIA's GPU-accelerated random number generation library designed to efficiently produce high-quality random numbers for parallel applications using CUDA.
-
B.
Random
Random is a fictional character who serves as the main protagonist in the story "Out of the Blue."
-
C.
Random
Random is a Julia standard library module that provides functionality for generating and manipulating random numbers and random processes.
-
D.
Chainer
Chainer is an open-source deep learning framework for Python that pioneered a flexible "define-by-run" computation graph approach to building neural networks.
-
E.
Theano
Theano is an open-source numerical computation library for Python that allows efficient definition, optimization, and evaluation of mathematical expressions, particularly those involving multi-dimensional arrays, and was widely used as a backend for deep learning frameworks.
- F. None of above. chosen
Provenance (5 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_69b3453c2a0c8190926b574c90766db9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3554e40ec8190982acc0948da2f42 |
completed | March 13, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5f633a69c8190b062c2a78b0f8319 |
completed | March 14, 2026, 11:58 p.m. |
| NEDg | Description generation | batch_69b5f6bcfa0481909d07ffb2a975a350 |
completed | March 15, 2026, 12:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5f733c660819081c68dc3ec342e12 |
completed | March 15, 2026, 12:02 a.m. |
Created at: March 12, 2026, 11:30 p.m.