Triple

T12491845
Position Surface form Disambiguated ID Type / Status
Subject Probabilistic Graphical Models: Principles and Techniques E298583 entity
Predicate topic P261 FINISHED
Object Bayesian networks E200666 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 networks | Statement: [Probabilistic Graphical Models: Principles and Techniques, topic, Bayesian networks]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bayesian networks
Context triple: [Probabilistic Graphical Models: Principles and Techniques, topic, Bayesian networks]
  • A. Bayesian networks chosen
    Bayesian networks are probabilistic graphical models that represent variables and their conditional dependencies using directed acyclic graphs, enabling structured reasoning and inference under uncertainty.
  • B. 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.
  • 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. Bayes
    Bayes is a surname most famously associated with Thomas Bayes, the 18th-century statistician and minister whose work led to the development of Bayesian probability theory.
  • E. Bayes rules
    Bayes rules are decision rules in statistical decision theory that minimize expected loss with respect to a prior distribution, forming a central concept in Bayesian optimal decision-making.
  • 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_69d6ada377208190a36011199a4d8558 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94de3076c81909640c982d520ca6b completed April 10, 2026, 7:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69f64bab99bc8190abe6dfb7c6a7fe6f completed May 2, 2026, 7:08 p.m.
Created at: April 8, 2026, 9:56 p.m.