Triple

T18301214
Position Surface form Disambiguated ID Type / Status
Subject jax.experimental E438360 entity
Predicate isNamespaceFor P79649 FINISHED
Object jax.experimental.optimizers NE NERFINISHED

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: jax.experimental.optimizers | Statement: [jax.experimental, isNamespaceFor, jax.experimental.optimizers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: jax.experimental.optimizers
Context triple: [jax.experimental, isNamespaceFor, jax.experimental.optimizers]
  • A. Adam optimizer
    The Adam optimizer is a popular stochastic gradient descent method in machine learning that adaptively adjusts learning rates for each parameter using estimates of first and second moments of gradients.
  • B. jax.experimental chosen
    jax.experimental is a submodule of the JAX library that provides access to experimental, unstable, or cutting-edge numerical and machine learning features not yet part of the stable API.
  • C. AdaGrad
    AdaGrad is an adaptive gradient descent optimization algorithm that adjusts learning rates for individual parameters based on their historical gradients, often improving convergence in sparse settings.
  • D. AdaDelta
    AdaDelta is an adaptive learning rate optimization algorithm for training neural networks that improves upon methods like RMSProp by eliminating the need to manually set a global learning rate.
  • E. RMSProp
    RMSProp is an adaptive gradient-based optimization algorithm commonly used to efficiently train deep neural networks by adjusting learning rates for individual parameters.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b915e3e881909125d760c15d0c29 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e5017f63dc819083a675d570620f2f completed April 19, 2026, 4:23 p.m.
Created at: April 10, 2026, 10:35 a.m.