Triple

T18204420
Position Surface form Disambiguated ID Type / Status
Subject T5 E435867 entity
Predicate usesEncoder P130213 FINISHED
Object Transformer encoder 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: Transformer encoder | Statement: [T5, usesEncoder, Transformer encoder]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Transformer encoder
Context triple: [T5, usesEncoder, Transformer encoder]
  • A. Transformer encoder-only chosen
    A Transformer encoder-only model is a neural network architecture that uses only the encoder stack of the Transformer to process input sequences, typically for tasks like classification, retrieval, and masked language modeling.
  • B. Transformer
    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
    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."
  • D. Transformer-XL
    Transformer-XL is a neural network architecture for language modeling that extends the Transformer with segment-level recurrence and relative positional encodings to better capture long-range dependencies.
  • E. EncoderDecoderModel
    EncoderDecoderModel is a Hugging Face Transformers architecture that combines a separate encoder and decoder into a unified sequence-to-sequence model for tasks like translation, summarization, and text generation.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: usesEncoder
Context triple: [T5, usesEncoder, Transformer encoder]
  • A. usesCodec
    Indicates that one entity employs or relies on a specific codec to encode, decode, or process data.
  • B. encodes
    Indicates that one entity contains or represents the information, instructions, or structure of another in a coded or symbolic form.
  • C. encodedIn
    Indicates that one entity is represented, stored, or expressed within another entity using a specific encoding or format.
  • D. imageEncoderType
    Indicates the specific kind or configuration of encoder used to process and represent image data.
  • E. notEncodedIn
    Indicates that a piece of information, data, or content is explicitly absent from or not represented within a given encoding, format, or medium.
  • F. None of above. chosen

Provenance (4 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.
PD Predicate disambiguation batch_69e4332155d88190b106d0dceb4554af completed April 19, 2026, 1:42 a.m.
PDg Predicate description generation batch_69e438f684e48190b38c64b58c518b6a completed April 19, 2026, 2:07 a.m.
Created at: April 10, 2026, 10:32 a.m.