Triple

T11002259
Position Surface form Disambiguated ID Type / Status
Subject Gibbs sampling E260029 entity
Predicate usedIn P98 FINISHED
Object Markov random fields E260046 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: Markov random fields | Statement: [Gibbs sampling, usedIn, Markov random fields]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Markov random fields
Context triple: [Gibbs sampling, usedIn, Markov random fields]
  • A. Markov random fields chosen
    Markov random fields are probabilistic graphical models that represent the joint distribution of a set of random variables with local dependencies encoded by an undirected graph, widely used in areas like statistical physics, computer vision, and spatial statistics.
  • B. Modeling image patches with a directed hierarchy of Markov random fields
    "Modeling image patches with a directed hierarchy of Markov random fields" is a research paper that introduces a probabilistic hierarchical model for capturing complex statistical structure in image patches using directed Markov random fields.
  • 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. 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.
  • E. 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.
  • 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_69d6aa8a6a548190a750f944ccdc8064 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d796d760008190930228fa77b61b8b completed April 9, 2026, 12:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3453d181081908cb58a957f4d1295 completed April 18, 2026, 8:47 a.m.
Created at: April 8, 2026, 9:25 p.m.