Triple

T13444125
Position Surface form Disambiguated ID Type / Status
Subject Kolmogorov extension theorem E320435 entity
Predicate usedIn P98 FINISHED
Object Bayesian nonparametrics E1031259 NE FINISHED

How this triple was built (2 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: Bayesian nonparametrics | Statement: [Kolmogorov extension theorem, usedIn, Bayesian nonparametrics]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bayesian nonparametrics
Context triple: [Kolmogorov extension theorem, usedIn, Bayesian nonparametrics]
  • A. Bayesian nonparametrics chosen
    Bayesian nonparametrics is a branch of Bayesian statistics that uses flexible, potentially infinite-dimensional models to let data determine model complexity rather than fixing a finite set of parameters in advance.
  • B. Dirichlet process models
    Dirichlet process models are a class of Bayesian nonparametric models that allow flexible, potentially infinite mixture modeling without fixing the number of components in advance.
  • C. Bayesian inference
    Bayesian inference is a statistical framework that updates the probability of hypotheses as more evidence or data becomes available, using Bayes’ theorem to combine prior beliefs with observed information.
  • D. Stick-breaking construction for the Indian buffet process
    "Stick-breaking construction for the Indian buffet process" is a research paper by Yee-Whye Teh that introduces a stick-breaking representation for the Indian buffet process, providing a constructive and interpretable way to model infinite latent feature allocations in Bayesian nonparametrics.
  • E. Pitman–Yor process models
    Pitman–Yor process models are Bayesian nonparametric models that generalize Dirichlet process models by allowing power-law behavior and heavier-tailed distributions over clusters.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d80761e6cc8190a90c844589998ecc completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbaee881888190811ddf01bc699864 completed April 12, 2026, 2:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69f739965ef081909e85881ce805bbb5 completed May 3, 2026, 12:03 p.m.
Created at: April 9, 2026, 9:40 p.m.