Triple

T13267067
Position Surface form Disambiguated ID Type / Status
Subject Stick-breaking construction for the Indian buffet process E315949 entity
Predicate relatesTo P37 FINISHED
Object Dirichlet process E315947 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: Dirichlet process | Statement: [Stick-breaking construction for the Indian buffet process, relatesTo, Dirichlet process]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dirichlet process
Context triple: [Stick-breaking construction for the Indian buffet process, relatesTo, Dirichlet process]
  • A. Dirichlet process models chosen
    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.
  • B. Dirichlet distribution
    The Dirichlet distribution is a family of continuous multivariate probability distributions commonly used as a prior over categorical or multinomial parameters in Bayesian statistics.
  • C. 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.
  • 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. Bayesian nonparametrics
    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.
  • 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_69d806b1d9ac8190852c5571d5bd5f0f completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d9901e44bc8190966f87ae219d6bf4 completed April 11, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69f716cd8c2c8190a28d901fde98dc26 completed May 3, 2026, 9:35 a.m.
Created at: April 9, 2026, 9:25 p.m.