Triple

T18204519
Position Surface form Disambiguated ID Type / Status
Subject ALBERT E435869 entity
Predicate hasVariant P455 FINISHED
Object ALBERT-large — NE NERFINISHED

How this triple was built (3 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: ALBERT-large | Statement: [ALBERT, hasVariant, ALBERT-large]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ALBERT-large
Context triple: [ALBERT, hasVariant, ALBERT-large]
  • A. Megatron-LM
    Megatron-LM is a large-scale language model training framework developed by NVIDIA, designed to efficiently train massive transformer models through model, tensor, and pipeline parallelism.
  • B. Longformer
    Longformer is a transformer-based neural network architecture designed for efficient processing of very long sequences using sparse attention mechanisms.
  • C. LLaMA
    LLaMA is a family of large language models developed by Meta AI, designed for efficient training and inference across a range of natural language processing tasks.
  • D. RoBERTa
    RoBERTa is a robustly optimized transformer-based language model developed by Facebook AI that improves upon BERT through enhanced training strategies and larger-scale data.
  • E. DeepScale
    DeepScale was an AI startup focused on efficient deep learning and computer vision models for resource-constrained devices, particularly in the automotive and embedded systems space.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ALBERT-large
Target entity description: ALBERT-large is a larger, higher-capacity configuration of the ALBERT language model designed to improve performance on natural language understanding tasks while maintaining parameter efficiency.
  • A. Megatron-LM
    Megatron-LM is a large-scale language model training framework developed by NVIDIA, designed to efficiently train massive transformer models through model, tensor, and pipeline parallelism.
  • B. Longformer
    Longformer is a transformer-based neural network architecture designed for efficient processing of very long sequences using sparse attention mechanisms.
  • C. LLaMA
    LLaMA is a family of large language models developed by Meta AI, designed for efficient training and inference across a range of natural language processing tasks.
  • D. RoBERTa
    RoBERTa is a robustly optimized transformer-based language model developed by Facebook AI that improves upon BERT through enhanced training strategies and larger-scale data.
  • E. DeepScale
    DeepScale was an AI startup focused on efficient deep learning and computer vision models for resource-constrained devices, particularly in the automotive and embedded systems space.
  • F. None of above. chosen

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_69d8b90dba6481908e119eb9aa4ca0cb completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4e222831081908f7d5500424e3acb completed April 19, 2026, 2:09 p.m.
Created at: April 10, 2026, 10:32 a.m.