Triple

T10700852
Position Surface form Disambiguated ID Type / Status
Subject Viterbi algorithm E252270 entity
Predicate basedOn P98 FINISHED
Object Bayesian inference E40249 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 inference | Statement: [Viterbi algorithm, basedOn, Bayesian inference]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bayesian inference
Context triple: [Viterbi algorithm, basedOn, Bayesian inference]
  • A. Bayesian inference chosen
    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.
  • B. 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.
  • C. Bayesian networks
    Bayesian networks are probabilistic graphical models that represent variables and their conditional dependencies using directed acyclic graphs, enabling structured reasoning and inference under uncertainty.
  • D. Bayes factor
    The Bayes factor is a Bayesian model comparison metric that quantifies how much more strongly data support one statistical model or hypothesis over another.
  • 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_69d6aa5cbabc8190973e683950d89faf completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6fd8bb7408190a350840e1df3b910 completed April 9, 2026, 1:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69d998ed78e481908537ae10d55e6f65 completed April 11, 2026, 12:42 a.m.
Created at: April 8, 2026, 9:12 p.m.