Triple
T18016447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faster R-CNN |
E431008
|
entity |
| Predicate | proposedBy |
P32
|
FINISHED |
| Object | Shaoqing Ren |
—
|
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: Shaoqing Ren | Statement: [Faster R-CNN, proposedBy, Shaoqing Ren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shaoqing Ren Context triple: [Faster R-CNN, proposedBy, Shaoqing Ren]
-
A.
Shaoqing Ren
chosen
Shaoqing Ren is a Chinese computer vision researcher best known as a co-developer of deep learning architectures such as ResNet and Faster R-CNN that have significantly advanced image recognition and object detection.
-
B.
Liqun Luo
Liqun Luo is a prominent neuroscientist known for his work on neural circuit assembly and function, particularly using Drosophila and mouse models.
-
C.
Zhen Luo
Zhen Luo, better known as Empress Zhen of Wei, was a consort of Cao Pi and posthumous empress of the state of Cao Wei during China’s Three Kingdoms period.
-
D.
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.
-
E.
Saining Xie
Saining Xie is a computer vision researcher known for his influential work on deep convolutional neural network architectures, including the ResNeXt model.
- 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b523f588819097389e067dda7f23 |
completed | April 19, 2026, 10:57 a.m. |
Created at: April 10, 2026, 10:24 a.m.