Triple
T18204454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BART |
E435868
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | BART-large |
—
|
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: BART-large | Statement: [BART, hasVariant, BART-large]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BART-large Context triple: [BART, hasVariant, BART-large]
-
A.
BART-base
BART-base is a smaller, 12-layer variant of Facebook AI’s BART sequence-to-sequence transformer model, commonly used for tasks like text generation, summarization, and translation.
-
B.
mBART
mBART is a multilingual sequence-to-sequence Transformer model designed for tasks like machine translation and text generation across many languages.
-
C.
ALBERT-large
ALBERT-large is a larger, higher-capacity configuration of the ALBERT language model designed to improve performance on natural language understanding tasks while maintaining parameter efficiency.
-
D.
BART-large-XSum
chosen
BART-large-XSum is a fine-tuned variant of Facebook AI’s BART-large model specialized for abstractive summarization on the XSum (Extreme Summarization) dataset.
-
E.
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.
- 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.