Triple
T18178381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PyMC3 |
E435219
|
entity |
| Predicate | successor |
P78
|
FINISHED |
| Object | PyMC (v4+) |
—
|
NE NERFINISHED |
How this triple was built (3 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: PyMC (v4+) | Statement: [PyMC3, successor, PyMC (v4+)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PyMC (v4+) Context triple: [PyMC3, successor, PyMC (v4+)]
-
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.
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.
-
C.
ArviZ
ArviZ is an open-source Python library for exploratory analysis and visualization of Bayesian models and probabilistic programming outputs.
-
D.
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.
-
E.
Bayesian linear regression
Bayesian linear regression is a statistical modeling approach that treats regression coefficients and predictions probabilistically by placing prior distributions on parameters and updating them with observed data.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: PyMC (v4+) Target entity description: PyMC (v4+) is a modern probabilistic programming library in Python that builds on PyTensor to provide flexible, high-level tools for Bayesian statistical modeling and inference.
-
A.
PyMC3
chosen
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.
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.
-
C.
ArviZ
ArviZ is an open-source Python library for exploratory analysis and visualization of Bayesian models and probabilistic programming outputs.
-
D.
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.
-
E.
Bayesian linear regression
Bayesian linear regression is a statistical modeling approach that treats regression coefficients and predictions probabilistically by placing prior distributions on parameters and updating them with observed data.
- F. None of above.
Provenance (2 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_69d8b90c7ec081909b4694ccecb449c6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4df5b68f081908aac8210270f1499 |
completed | April 19, 2026, 1:57 p.m. |
Created at: April 10, 2026, 10:31 a.m.