Triple

T9674971
Position Surface form Disambiguated ID Type / Status
Subject MXNet E234123 entity
Predicate hasComponent P35 FINISHED
Object MXNet Engine E234123 NE 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: MXNet Engine | Statement: [MXNet, hasComponent, MXNet Engine]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MXNet Engine
Context triple: [MXNet, hasComponent, MXNet Engine]
  • A. MXNet chosen
    MXNet is an open-source deep learning framework designed for efficient, scalable training and inference across multiple GPUs and distributed systems.
  • B. NVIDIA TensorRT
    NVIDIA TensorRT is a high-performance deep learning inference optimizer and runtime library designed to accelerate AI models on NVIDIA GPUs in production environments.
  • C. Microsoft Cognitive Toolkit
    Microsoft Cognitive Toolkit (CNTK) is an open-source deep learning framework developed by Microsoft for building, training, and deploying neural networks at scale.
  • D. NVIDIA Triton Inference Server
    NVIDIA Triton Inference Server is an open-source, production-ready platform for serving and scaling AI model inference across GPUs and CPUs with support for multiple frameworks and deployment environments.
  • E. PlaidML
    PlaidML is an open-source, hardware-agnostic deep learning engine designed to accelerate neural network computation on a wide range of GPUs and other devices.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69ca848f55e48190b3f67252571c3d45 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9c6d6dd48190a77c486337a58cb6 completed April 1, 2026, 10:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69d18a30883081909e8f70225b6ee820 completed April 4, 2026, 10:01 p.m.
Created at: March 30, 2026, 8:15 p.m.