Triple

T35690020
Position Surface form Disambiguated ID Type / Status
Subject beta-Bernoulli process construction E1031260 entity
Predicate supportsInferenceVia P207096 FINISHED
Object Markov chain Monte Carlo E46140 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 chain Monte Carlo | Statement: [beta-Bernoulli process construction, supportsInferenceVia, Markov chain Monte Carlo]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: supportsInferenceVia
Context triple: [beta-Bernoulli process construction, supportsInferenceVia, Markov chain Monte Carlo]
  • A. supportsInferenceOf
    Indicates that one entity provides a logical basis or justification for concluding or deriving another entity.
  • B. supportedIn
    Indicates that one entity is valid, applicable, or functionally enabled within the context, environment, platform, or scope defined by another entity.
  • C. theorySupports
    Indicates that one theory provides justification, evidence, or logical backing for another claim, theory, or proposition.
  • D. supportsInstruction
    Indicates that one entity provides assistance, resources, or functionality that enables or facilitates the instruction or teaching activities of another entity.
  • E. supportsAt
    Indicates that one entity provides assistance, endorsement, or backing to another entity in a specific context, location, or point in time.
  • F. None of above. chosen

Provenance (5 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_69f76e0c73ec819080ab60a9e2f5f1f6 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_6a037c92f03c8190ae2751270b195423 completed May 12, 2026, 7:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3885e2d3448190a6072d2ecde2eb3f completed June 22, 2026, 12:46 a.m.
PD Predicate disambiguation batch_6a037a04d8348190a4819666eab42c9b completed May 12, 2026, 7:05 p.m.
PDg Predicate description generation batch_6a037c82179081908325a59b8539b3a8 completed May 12, 2026, 7:16 p.m.
Created at: May 3, 2026, 4:05 p.m.