Triple

T15361380
Position Surface form Disambiguated ID Type / Status
Subject Xiangyu Zhang E367296 entity
Predicate notableWork P4 FINISHED
Object ResNeXt: Aggregated Residual Transformations for Deep Neural Networks E367297 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: ResNeXt: Aggregated Residual Transformations for Deep Neural Networks | Statement: [Xiangyu Zhang, notableWork, ResNeXt: Aggregated Residual Transformations for Deep Neural Networks]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ResNeXt: Aggregated Residual Transformations for Deep Neural Networks
Context triple: [Xiangyu Zhang, notableWork, ResNeXt: Aggregated Residual Transformations for Deep Neural Networks]
  • A. Aggregated Residual Transformations for Deep Neural Networks
    "Aggregated Residual Transformations for Deep Neural Networks" is the research paper that introduced the ResNeXt architecture, a deep convolutional neural network design that improves accuracy and efficiency by using grouped convolutions and aggregated residual transformations.
  • B. ResNeXt chosen
    ResNeXt is a deep convolutional neural network architecture that extends ResNet by using grouped convolutions and a split-transform-merge strategy to improve accuracy and efficiency in image recognition tasks.
  • C. 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.
  • D. 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.
  • E. Reformer architecture
    The Reformer architecture is a neural network model that improves Transformer efficiency by using locality-sensitive hashing attention and reversible layers to greatly reduce memory and computational costs.
  • 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_69d85a1483788190ad93c2748e8af34b completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e4607408190ab281a7f7a8012d3 completed April 16, 2026, 1:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff1343862481908962dfe0ab946b97 completed May 9, 2026, 10:58 a.m.
Created at: April 10, 2026, 3:18 a.m.