Triple
T11002333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hamiltonian Monte Carlo |
E260030
|
entity |
| Predicate | implementedIn |
P2539
|
FINISHED |
| Object |
NumPyro
NumPyro is a lightweight probabilistic programming library for Python that leverages JAX to provide high-performance, scalable Bayesian inference with modern MCMC and variational inference algorithms.
|
E898985
|
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: NumPyro | Statement: [Hamiltonian Monte Carlo, implementedIn, NumPyro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: NumPyro Context triple: [Hamiltonian Monte Carlo, implementedIn, NumPyro]
-
A.
PyMC3
PyMC3 is a Python library for probabilistic programming that enables Bayesian statistical modeling and inference using advanced Markov chain Monte Carlo and variational methods.
-
B.
TensorFlow Probability (JAX backend)
TensorFlow Probability (JAX backend) is a probabilistic programming and statistical modeling library that runs on JAX, providing tools for Bayesian inference, probabilistic layers, and advanced distributions with XLA-accelerated computation.
-
C.
Hamiltonian Monte Carlo
Hamiltonian Monte Carlo is an advanced Markov chain Monte Carlo sampling algorithm that uses concepts from Hamiltonian dynamics to efficiently explore complex, high-dimensional probability distributions.
-
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: NumPyro Triple: [Hamiltonian Monte Carlo, implementedIn, NumPyro]
Generated description
NumPyro is a lightweight probabilistic programming library for Python that leverages JAX to provide high-performance, scalable Bayesian inference with modern MCMC and variational inference algorithms.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: NumPyro Target entity description: NumPyro is a lightweight probabilistic programming library for Python that leverages JAX to provide high-performance, scalable Bayesian inference with modern MCMC and variational inference algorithms.
-
A.
PyMC3
PyMC3 is a Python library for probabilistic programming that enables Bayesian statistical modeling and inference using advanced Markov chain Monte Carlo and variational methods.
-
B.
TensorFlow Probability (JAX backend)
TensorFlow Probability (JAX backend) is a probabilistic programming and statistical modeling library that runs on JAX, providing tools for Bayesian inference, probabilistic layers, and advanced distributions with XLA-accelerated computation.
-
C.
Hamiltonian Monte Carlo
Hamiltonian Monte Carlo is an advanced Markov chain Monte Carlo sampling algorithm that uses concepts from Hamiltonian dynamics to efficiently explore complex, high-dimensional probability distributions.
-
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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d796d760008190930228fa77b61b8b |
completed | April 9, 2026, 12:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3453d181081908cb58a957f4d1295 |
completed | April 18, 2026, 8:47 a.m. |
| NEDg | Description generation | batch_69e35570b0bc8190a939b0c8e3ce8105 |
completed | April 18, 2026, 9:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e359508a388190a16d48a17015e13e |
completed | April 18, 2026, 10:13 a.m. |
Created at: April 8, 2026, 9:25 p.m.