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.