Triple

T18015969
Position Surface form Disambiguated ID Type / Status
Subject NumFOCUS E430998 entity
Predicate supportsProject P12986 FINISHED
Object ArviZ
ArviZ is an open-source Python library for exploratory analysis and visualization of Bayesian models and probabilistic programming outputs.
 E1300973 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: ArviZ | Statement: [NumFOCUS, supportsProject, ArviZ]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ArviZ
Context triple: [NumFOCUS, supportsProject, ArviZ]
  • 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. 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. Gibbs sampling
    Gibbs sampling is a Markov chain Monte Carlo algorithm that generates samples from complex multivariate probability distributions by iteratively sampling each variable from its conditional distribution given the others.
  • E. Bayes factor
    The Bayes factor is a Bayesian model comparison metric that quantifies how much more strongly data support one statistical model or hypothesis over another.
  • 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: ArviZ
Triple: [NumFOCUS, supportsProject, ArviZ]
Generated description
ArviZ is an open-source Python library for exploratory analysis and visualization of Bayesian models and probabilistic programming outputs.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ArviZ
Target entity description: ArviZ is an open-source Python library for exploratory analysis and visualization of Bayesian models and probabilistic programming outputs.
  • 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. 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. Gibbs sampling
    Gibbs sampling is a Markov chain Monte Carlo algorithm that generates samples from complex multivariate probability distributions by iteratively sampling each variable from its conditional distribution given the others.
  • E. Bayes factor
    The Bayes factor is a Bayesian model comparison metric that quantifies how much more strongly data support one statistical model or hypothesis over another.
  • 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_69d8b904530081908bf341d842464856 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4b523f588819097389e067dda7f23 completed April 19, 2026, 10:57 a.m.
NED1 Entity disambiguation (via context triple) batch_6a034324daec8190a9bbec1ad80c70f9 completed May 12, 2026, 3:11 p.m.
NEDg Description generation batch_6a0343dc91688190ae8e2f051cefef85 completed May 12, 2026, 3:14 p.m.
NED2 Entity disambiguation (via description) batch_6a0344b6f4e081908ff2fbc7bfa4c4e1 completed May 12, 2026, 3:18 p.m.
Created at: April 10, 2026, 10:24 a.m.