Triple

T18204486
Position Surface form Disambiguated ID Type / Status
Subject ALBERT E435869 entity
Predicate hasArchitecture P4631 FINISHED
Object Transformer NE NERFINISHED

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: Transformer | Statement: [ALBERT, hasArchitecture, Transformer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Transformer
Context triple: [ALBERT, hasArchitecture, Transformer]
  • A. Transformer
    Transformer is a 1972 glam rock album by Lou Reed, co-produced by David Bowie and Mick Ronson, known for its influential sound and iconic tracks like "Walk on the Wild Side."
  • B. Transformer chosen
    Transformer is a neural network architecture based on self-attention mechanisms that has become the foundation for modern large language models and many state-of-the-art systems in natural language processing.
  • C. Transformer decoder
    A Transformer decoder is a neural network component that generates output sequences step-by-step using self-attention and cross-attention over encoder representations, widely used in modern sequence-to-sequence models.
  • D. Transform
    Transform is a TensorFlow Extended (TFX) component used for scalable data preprocessing and feature engineering in machine learning pipelines.
  • 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 (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.