Triple

T11003093
Position Surface form Disambiguated ID Type / Status
Subject Neural Architecture Search E260047 entity
Predicate notableMethod P16102 FINISHED
Object NASNet
NASNet is a family of convolutional neural network architectures automatically discovered via neural architecture search, known for achieving state-of-the-art performance on image classification benchmarks.
 E899014 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: NASNet | Statement: [Neural Architecture Search, notableMethod, NASNet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: NASNet
Context triple: [Neural Architecture Search, notableMethod, NASNet]
  • A. GoogLeNet
    GoogLeNet is a deep convolutional neural network developed by Google that popularized the Inception architecture and achieved state-of-the-art performance in image recognition tasks.
  • B. ResNet
    ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
  • C. 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.
  • D. 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.
  • E. DenseNet
    DenseNet is a family of convolutional neural network architectures characterized by densely connected layers that improve information flow and parameter efficiency for image recognition tasks.
  • 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: NASNet
Triple: [Neural Architecture Search, notableMethod, NASNet]
Generated description
NASNet is a family of convolutional neural network architectures automatically discovered via neural architecture search, known for achieving state-of-the-art performance on image classification benchmarks.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: NASNet
Target entity description: NASNet is a family of convolutional neural network architectures automatically discovered via neural architecture search, known for achieving state-of-the-art performance on image classification benchmarks.
  • A. GoogLeNet
    GoogLeNet is a deep convolutional neural network developed by Google that popularized the Inception architecture and achieved state-of-the-art performance in image recognition tasks.
  • B. ResNet
    ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
  • C. 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.
  • D. 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.
  • E. DenseNet
    DenseNet is a family of convolutional neural network architectures characterized by densely connected layers that improve information flow and parameter efficiency for image recognition tasks.
  • 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.