Triple

T13328665
Position Surface form Disambiguated ID Type / Status
Subject Modeling image patches with a directed hierarchy of Markov random fields E317508 entity
Predicate usesConcept P531 FINISHED
Object Markov random field 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 field | Statement: [Modeling image patches with a directed hierarchy of Markov random fields, usesConcept, Markov random field]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Markov random field
Context triple: [Modeling image patches with a directed hierarchy of Markov random fields, usesConcept, Markov random field]
  • 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
    Probabilistic graphical models are a framework in machine learning and statistics that represent complex joint probability distributions using graphs to capture conditional dependencies among random variables.
  • D. 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.
  • E. 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.
  • 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_69d806b4d62c81908d4ced1665414be5 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d9992e4f908190a6f172bf910cffb8 completed April 11, 2026, 12:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69f71f329e148190a7741344b27ea663 completed May 3, 2026, 10:10 a.m.
Created at: April 9, 2026, 9:30 p.m.