Triple

T13266991
Position Surface form Disambiguated ID Type / Status
Subject Dirichlet process models E315947 entity
Predicate relatedTo P37 FINISHED
Object Dirichlet process mixture model 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 mixture model | Statement: [Dirichlet process models, relatedTo, Dirichlet process mixture model]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dirichlet process mixture model
Context triple: [Dirichlet process models, relatedTo, Dirichlet process mixture model]
  • 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. Gaussian mixture models
    Gaussian mixture models are probabilistic clustering models that represent data as a combination of multiple Gaussian distributions, allowing soft cluster assignments and more flexible cluster shapes than KMeans.
  • C. 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.
  • D. Latent Dirichlet Allocation
    Latent Dirichlet Allocation is a generative probabilistic model commonly used in natural language processing to discover latent topics within large collections of documents.
  • E. Hidden Markov Model
    A Hidden Markov Model is a statistical model that represents systems with unobserved (hidden) states generating observable outputs, widely used for sequence analysis tasks such as speech recognition, bioinformatics, and natural language processing.
  • 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_69f70a4cc20881909b1ca6623e5b1988 completed May 3, 2026, 8:41 a.m.
Created at: April 9, 2026, 9:25 p.m.