Triple
T18204461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BART |
E435868
|
entity |
| Predicate | paperAuthors |
P2002
|
FINISHED |
| Object | Yinhan Liu |
—
|
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: Yinhan Liu | Statement: [BART, paperAuthors, Yinhan Liu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yinhan Liu Context triple: [BART, paperAuthors, Yinhan Liu]
-
A.
Yinhan Liu
chosen
Yinhan Liu is a natural language processing researcher best known for co-developing the RoBERTa language model and related advances in large-scale pretraining.
-
B.
Huan Liu
Huan Liu is a prominent computer scientist known for his influential research in data mining and machine learning, particularly in feature selection and social media analytics.
-
C.
Tingye Li
Tingye Li was a pioneering Chinese-American optical engineer and physicist renowned for his foundational contributions to laser and fiber-optic communications.
-
D.
Zhilin Yang
Zhilin Yang is a computer scientist and AI researcher known for his work on large-scale language models and as a lead author of the XLNet architecture.
-
E.
Yuhuai Wu
Yuhuai Wu is an AI researcher and entrepreneur known for his work on large language models and as a member of Elon Musk’s xAI team.
- 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.