Triple
T18204459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BART |
E435868
|
entity |
| Predicate | introducedInPaper |
P513
|
FINISHED |
| Object | BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension |
—
|
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: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension | Statement: [BART, introducedInPaper, BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Context triple: [BART, introducedInPaper, BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension]
-
A.
Language Models are Unsupervised Multitask Learners
"Language Models are Unsupervised Multitask Learners" is a 2019 OpenAI research paper that demonstrated how large-scale unsupervised language models like GPT-2 can perform a wide range of tasks without task-specific training.
-
B.
mBART
chosen
mBART is a multilingual sequence-to-sequence Transformer model designed for tasks like machine translation and text generation across many languages.
-
C.
Attention Is All You Need
"Attention Is All You Need" is the landmark 2017 research paper that introduced the Transformer architecture and revolutionized modern natural language processing and sequence modeling.
-
D.
Exploring the Limits of Language Modeling
"Exploring the Limits of Language Modeling" is a research paper that investigates how far large-scale neural language models can be pushed in terms of performance, scalability, and generalization on natural language tasks.
-
E.
Neural Programmer-Interpreters
Neural Programmer-Interpreters are a class of neural network models designed to learn and execute programs by combining differentiable memory, control flow, and modular subroutines for complex algorithmic reasoning tasks.
- 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.