Triple

T36491768
Position Surface form Disambiguated ID Type / Status
Subject miniImageNet E899067 entity
Predicate instanceOf P0 FINISHED
Object meta-learning benchmark C13913 CONCEPT FINISHED

How this triple was built (1 step)

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.

CD Concept disambiguation gpt-5-mini-2025-08-07 
Target class: meta-learning benchmark
Context triple: [miniImageNet, instanceOf, meta-learning benchmark]
  • A. metric-based meta-learning method
    A metric-based meta-learning method is an approach that learns a similarity measure or embedding space so that new tasks can be solved by comparing query examples to a small set of labeled support examples using distance-based inference.
  • B. benchmark in artificial intelligence
    A benchmark in artificial intelligence is a standardized task, dataset, or evaluation protocol used to quantitatively compare and assess the performance of AI models and algorithms.
  • C. benchmark dataset chosen
    A benchmark dataset is a standardized collection of data designed to objectively evaluate, compare, and validate the performance of algorithms, models, or systems on specific tasks.
  • D. meta-estimator
    A meta-estimator is a higher-level model that wraps or combines one or more base estimators to extend, modify, or coordinate their behavior for tasks like ensembling, preprocessing, or model selection.
  • E. large-scale language model training framework
    A large-scale language model training framework is a software system that orchestrates data processing, distributed computation, optimization, and resource management to efficiently train and fine-tune massive language models across many machines.
  • F. None of above.

Provenance (1 batch)

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_69f76e5ad4588190bdbce60c52fbb785 completed May 3, 2026, 3:48 p.m.
Created at: May 3, 2026, 4:10 p.m.