Triple

T12207451
Position Surface form Disambiguated ID Type / Status
Subject Wasserstein GAN E290870 entity
Predicate trainingProperty P32757 FINISHED
Object critic outputs real-valued scores instead of probabilities LITERAL 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: critic outputs real-valued scores instead of probabilities | Statement: [Wasserstein GAN, trainingProperty, critic outputs real-valued scores instead of probabilities]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: trainingProperty
Context triple: [Wasserstein GAN, trainingProperty, critic outputs real-valued scores instead of probabilities]
  • A. trainingCharacteristic chosen
    Indicates that an entity has a specific property, feature, or quality related to training (such as method, intensity, or style).
  • B. trainingUse
    Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
  • C. trainingComponent
    Indicates that one entity functions as a training-related part, module, or element within a larger training process or system involving another entity.
  • D. training
    Indicates that one entity is teaching, coaching, or otherwise helping another entity acquire or improve a skill, behavior, or capability.
  • E. trainingMethod
    Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
  • F. None of above.

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_69d6ab65923081909acfc61b7a612233 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d920e312708190b4aede2e21f5f697 completed April 10, 2026, 4:10 p.m.
PD Predicate disambiguation batch_69d91c3d669c81908eea7ad61122d275 completed April 10, 2026, 3:50 p.m.
Created at: April 8, 2026, 9:51 p.m.