Triple
T18724242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Llion Jones |
E457855
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | "Attention Is All You Need" |
—
|
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: "Attention Is All You Need" | Statement: [Llion Jones, notableWork, "Attention Is All You Need"]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: "Attention Is All You Need" Context triple: [Llion Jones, notableWork, "Attention Is All You Need"]
-
A.
Attention Is All You Need
chosen
"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.
-
B.
Bidirectional Encoder Representations from Transformers
Bidirectional Encoder Representations from Transformers (BERT) is a widely used deep learning language model developed by Google that learns contextual word representations by jointly conditioning on both left and right context in text.
-
C.
Transformer-XL
Transformer-XL is a neural network architecture for language modeling that extends the Transformer with segment-level recurrence and relative positional encodings to better capture long-range dependencies.
-
D.
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.
-
E.
Sequence to Sequence Learning with Neural Networks
"Sequence to Sequence Learning with Neural Networks" is a seminal 2014 paper that introduced the sequence-to-sequence (seq2seq) neural network framework for tasks like machine translation, laying the groundwork for many modern NLP models.
- 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_69d8d393ba9c8190a8b03b04ddbb0a09 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e56abcfc048190a01dee959e768768 |
completed | April 19, 2026, 11:52 p.m. |
Created at: April 10, 2026, 11:50 a.m.