Triple

T18204846
Position Surface form Disambiguated ID Type / Status
Subject XLM-R E435876 entity
Predicate tokenizationMethod P21075 FINISHED
Object SentencePiece 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: SentencePiece | Statement: [XLM-R, tokenizationMethod, SentencePiece]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: SentencePiece
Context triple: [XLM-R, tokenizationMethod, SentencePiece]
  • A. SentencePiece chosen
    SentencePiece is an unsupervised text tokenizer and detokenizer library, widely used in modern NLP models to perform subword segmentation independent of language- or whitespace-specific rules.
  • B. TensorFlow Text
    TensorFlow Text is a library of text-related ops and utilities that extends TensorFlow for building, training, and serving natural language processing models.
  • C. Fairseq
    Fairseq is a Facebook AI Research (FAIR) sequence modeling toolkit for training and evaluating state-of-the-art neural networks for tasks like machine translation, summarization, and language modeling.
  • D. Hugging Face Transformers
    Hugging Face Transformers is a widely used open-source library that provides state-of-the-art transformer-based models and tools for natural language processing and related machine learning tasks.
  • E. DistilBERT
    DistilBERT is a smaller, faster, and lighter-weight distilled version of the BERT language model designed to retain most of its performance while being more efficient for practical NLP applications.
  • 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: tokenizationMethod
Context triple: [XLM-R, tokenizationMethod, SentencePiece]
  • A. tokenType
    Indicates the classification or category assigned to a token within a sequence, such as its syntactic, semantic, or functional role.
  • B. tokenizerType chosen
    Indicates the specific tokenization method or algorithm used to split text into tokens.
  • C. cardVerificationMethod
    Indicates the method or process used to verify the authenticity or validity of a card during a transaction or interaction.
  • D. decodingMethod
    Indicates the technique or process used to convert encoded or encrypted data back into its original, interpretable form.
  • E. nativeToken
    Indicates that the referenced token is the primary, built-in cryptocurrency or asset of a given blockchain or platform, as opposed to a secondary or issued token.
  • F. None of above.

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_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.
Created at: April 10, 2026, 10:32 a.m.