Triple
T18222323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | knitr |
E436335
|
entity |
| Predicate | developer |
P73
|
FINISHED |
| Object | Yihui Xie |
—
|
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: Yihui Xie | Statement: [knitr, developer, Yihui Xie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yihui Xie Context triple: [knitr, developer, Yihui Xie]
-
A.
Yihui Xie
chosen
Yihui Xie is a statistician and software developer best known for creating the R Markdown ecosystem and other influential tools for reproducible research in R.
-
B.
Xindong Wu
Xindong Wu is a prominent computer scientist known for his influential contributions to data mining and knowledge discovery research.
-
C.
Yiping Gan
Yiping Gan is a regional variety of Gan Chinese, a Sinitic language spoken primarily in Jiangxi province and surrounding areas.
-
D.
Saining Xie
Saining Xie is a computer vision researcher known for his influential work on deep convolutional neural network architectures, including the ResNeXt model.
-
E.
Xiaodong Chen
Xiaodong Chen is a prominent materials scientist and nanotechnology researcher who serves as editor-in-chief of the journal ACS Nano.
- 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_69d8b9103a8081908bbb0836fef10efd |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e47c85108190bd9707b40bdfdb38 |
completed | April 19, 2026, 2:19 p.m. |
Created at: April 10, 2026, 10:32 a.m.