Triple
T18204457
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BART |
E435868
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | BART-large-XSum |
—
|
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: BART-large-XSum | Statement: [BART, hasVariant, BART-large-XSum]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BART-large-XSum Context triple: [BART, hasVariant, BART-large-XSum]
-
A.
mBART
mBART is a multilingual sequence-to-sequence Transformer model designed for tasks like machine translation and text generation across many languages.
-
B.
Longformer
Longformer is a transformer-based neural network architecture designed for efficient processing of very long sequences using sparse attention mechanisms.
-
C.
Maximal Marginal Relevance (MMR) for information retrieval and summarization
Maximal Marginal Relevance (MMR) is an information retrieval and summarization technique that selects results by jointly maximizing relevance to a query while minimizing redundancy among the chosen items.
-
D.
BERT
BERT is a widely used transformer-based language model developed by Google that learns deep bidirectional representations of text for tasks like question answering and text classification.
-
E.
Unitxt
Unitxt is an experimental electronic music album by German artist Alva Noto, known for its minimalist, glitch-based sound design and conceptual approach to rhythm and data.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: BART-large-XSum Target entity description: 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.
-
A.
mBART
mBART is a multilingual sequence-to-sequence Transformer model designed for tasks like machine translation and text generation across many languages.
-
B.
Longformer
Longformer is a transformer-based neural network architecture designed for efficient processing of very long sequences using sparse attention mechanisms.
-
C.
Maximal Marginal Relevance (MMR) for information retrieval and summarization
Maximal Marginal Relevance (MMR) is an information retrieval and summarization technique that selects results by jointly maximizing relevance to a query while minimizing redundancy among the chosen items.
-
D.
BERT
BERT is a widely used transformer-based language model developed by Google that learns deep bidirectional representations of text for tasks like question answering and text classification.
-
E.
Unitxt
Unitxt is an experimental electronic music album by German artist Alva Noto, known for its minimalist, glitch-based sound design and conceptual approach to rhythm and data.
- F. None of above. chosen
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.