Triple

T11003097
Position Surface form Disambiguated ID Type / Status
Subject Neural Architecture Search E260047 entity
Predicate notableMethod P16102 FINISHED
Object ProxylessNAS
ProxylessNAS is a neural architecture search method that directly optimizes neural network architectures on target tasks and hardware without relying on proxy models, enabling efficient and hardware-aware network design.
 E899018 NE FINISHED

How this triple was built (4 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: ProxylessNAS | Statement: [Neural Architecture Search, notableMethod, ProxylessNAS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ProxylessNAS
Context triple: [Neural Architecture Search, notableMethod, ProxylessNAS]
  • A. Neural Architecture Search
    Neural Architecture Search is an automated machine learning technique that uses algorithms to design and optimize neural network architectures without extensive human intervention.
  • B. Learning Transferable Architectures for Scalable Image Recognition
    "Learning Transferable Architectures for Scalable Image Recognition" is a research paper that introduced NASNet, a neural architecture search–designed convolutional network that achieved state-of-the-art performance on large-scale image recognition tasks.
  • C. MobileNetV2
    MobileNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, widely used in computer vision applications and available in libraries like torchvision.
  • D. ShuffleNetV2
    ShuffleNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, emphasizing speed and low computational cost.
  • E. SqueezeNet
    SqueezeNet is a compact deep convolutional neural network architecture designed to achieve AlexNet-level image classification accuracy with dramatically fewer parameters, making it efficient for deployment on resource-constrained devices.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: ProxylessNAS
Triple: [Neural Architecture Search, notableMethod, ProxylessNAS]
Generated description
ProxylessNAS is a neural architecture search method that directly optimizes neural network architectures on target tasks and hardware without relying on proxy models, enabling efficient and hardware-aware network design.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ProxylessNAS
Target entity description: ProxylessNAS is a neural architecture search method that directly optimizes neural network architectures on target tasks and hardware without relying on proxy models, enabling efficient and hardware-aware network design.
  • A. Neural Architecture Search
    Neural Architecture Search is an automated machine learning technique that uses algorithms to design and optimize neural network architectures without extensive human intervention.
  • B. Learning Transferable Architectures for Scalable Image Recognition
    "Learning Transferable Architectures for Scalable Image Recognition" is a research paper that introduced NASNet, a neural architecture search–designed convolutional network that achieved state-of-the-art performance on large-scale image recognition tasks.
  • C. MobileNetV2
    MobileNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, widely used in computer vision applications and available in libraries like torchvision.
  • D. ShuffleNetV2
    ShuffleNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, emphasizing speed and low computational cost.
  • E. SqueezeNet
    SqueezeNet is a compact deep convolutional neural network architecture designed to achieve AlexNet-level image classification accuracy with dramatically fewer parameters, making it efficient for deployment on resource-constrained devices.
  • F. None of above. chosen

Provenance (5 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_69d6aa8a6a548190a750f944ccdc8064 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d797546f448190946ee6442d657dc5 completed April 9, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3453d181081908cb58a957f4d1295 completed April 18, 2026, 8:47 a.m.
NEDg Description generation batch_69e35570b0bc8190a939b0c8e3ce8105 completed April 18, 2026, 9:57 a.m.
NED2 Entity disambiguation (via description) batch_69e359508a388190a16d48a17015e13e completed April 18, 2026, 10:13 a.m.
Created at: April 8, 2026, 9:25 p.m.