Triple

T18204958
Position Surface form Disambiguated ID Type / Status
Subject BigBird E435879 entity
Predicate instanceOf P0 FINISHED
Object sparse attention model C37717 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: sparse attention model
Context triple: [BigBird, instanceOf, sparse attention model]
  • A. large-scale model chosen
    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. associative memory model
    An associative memory model is a computational or theoretical framework that stores and retrieves information based on learned relationships or patterns between items, enabling recall of one item when presented with another related cue.
  • C. plate margin network
    A plate margin network is the interconnected system of tectonic plate boundaries and their associated geological structures and processes that collectively govern the distribution and interaction of Earth’s lithospheric plates.
  • D. 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.
  • 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.

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_69d8b90dba6481908e119eb9aa4ca0cb completed April 10, 2026, 8:47 a.m.
Created at: April 10, 2026, 10:32 a.m.