Triple

T2987335
Position Surface form Disambiguated ID Type / Status
Subject Yee-Whye Teh E80657 entity
Predicate hasWrittenWork P4 FINISHED
Object Hierarchical Dirichlet Processes 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: Hierarchical Dirichlet Processes | Statement: [Yee-Whye Teh, hasWrittenWork, Hierarchical Dirichlet Processes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hierarchical Dirichlet Processes
Context triple: [Yee-Whye Teh, hasWrittenWork, Hierarchical Dirichlet Processes]
  • 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. 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.
  • C. Probabilistic Graphical Models: Principles and Techniques
    Probabilistic Graphical Models: Principles and Techniques is a foundational textbook that systematically presents the theory, algorithms, and applications of probabilistic graphical models in machine learning and artificial intelligence.
  • D. Helmholtz machine
    The Helmholtz machine is a pioneering generative neural network model that learns internal representations by using separate recognition and generative pathways to perform unsupervised learning.
  • E. Bayesian Occam factor
    The Bayesian Occam factor is a term in Bayesian model comparison that automatically penalizes overly complex models by integrating over their larger parameter spaces, thereby implementing Occam’s razor in probabilistic inference.
  • 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_69ad8b16c3488190b47b6aa7a59a335b completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad99c88f608190bf734e0b744bf3d1 completed March 8, 2026, 3:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1de999ff88190824ecdc164496d37 completed March 11, 2026, 9:28 p.m.
Created at: March 8, 2026, 2:59 p.m.