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.