Triple

T29938637
Position Surface form Disambiguated ID Type / Status
Subject Megatron-LM E760435 entity
Predicate instanceOf P0 FINISHED
Object large-scale language model training framework C56554 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: large-scale language model training framework
Context triple: [Megatron-LM, instanceOf, large-scale language model training framework]
  • A. large-scale model
    A large-scale model is a computational model, often in machine learning or simulation, that operates with vast numbers of parameters or variables to capture complex patterns or behaviors across extensive datasets or systems.
  • B. multimodal large language model family
    A multimodal large language model family is a group of related neural models that can jointly process and generate multiple data modalities—such as text, images, audio, or video—using shared architectures, training objectives, and parameterizations.
  • C. large language model family
    A large language model family is a group of related neural network models that share a common architecture and training paradigm but vary in size, capabilities, and specialization to handle diverse natural language understanding and generation tasks.
  • D. hierarchical transformer model
    A hierarchical transformer model is a neural network architecture that processes data at multiple levels of granularity (e.g., tokens, sentences, documents) using stacked transformer layers to capture both local and global contextual dependencies efficiently.
  • E. scalable RL architecture
    A scalable RL architecture is a modular, distributed system design that efficiently trains and serves reinforcement learning agents across large state-action spaces, high data volumes, and many concurrent tasks or environments.
  • F. None of above. chosen

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_69f22463f3648190a603c3ff305c660b completed April 29, 2026, 3:31 p.m.
Created at: April 29, 2026, 6:21 p.m.