Triple
T18204470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BART |
E435868
|
entity |
| Predicate | openSourceImplementation |
P7052
|
FINISHED |
| Object | Hugging Face Transformers |
—
|
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: Hugging Face Transformers | Statement: [BART, openSourceImplementation, Hugging Face Transformers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hugging Face Transformers Context triple: [BART, openSourceImplementation, Hugging Face Transformers]
-
A.
Hugging Face Transformers
chosen
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.
-
B.
Hugging Face
Hugging Face is an AI company and open-source community best known for its tools and libraries that make it easy to build, share, and deploy state-of-the-art machine learning models.
-
C.
Hugging Face Tokenizers
Hugging Face Tokenizers is a fast, production-ready library for building and using modern text tokenization pipelines optimized for natural language processing and machine learning models.
-
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.
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.
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.