Triple
T12207484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CycleGAN |
E290871
|
entity |
| Predicate | introducedBy |
P513
|
FINISHED |
| Object | Jun-Yan Zhu |
E326792
|
NE FINISHED |
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: Jun-Yan Zhu | Statement: [CycleGAN, introducedBy, Jun-Yan Zhu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jun-Yan Zhu Context triple: [CycleGAN, introducedBy, Jun-Yan Zhu]
-
A.
Jun-Yan Zhu
chosen
Jun-Yan Zhu is a computer scientist and researcher known for his influential work in computer vision and generative models, particularly in image-to-image translation.
-
B.
Chiwei Yu
Chiwei Yu is a small, remote islet in the East China Sea that is part of the disputed Diaoyutai/Senkaku Islands archipelago.
-
C.
Langche Zeng
Langche Zeng is a political scientist and quantitative methodologist known for his collaborative work with Gary King on statistical methods in social science research.
-
D.
Geling Yan
Geling Yan is a Chinese-American novelist and screenwriter known for her emotionally powerful works that often explore the human impact of war, political upheaval, and social change in modern Chinese history.
-
E.
Ziyu Wang
Ziyu Wang is a machine learning researcher best known for co-developing the dueling deep Q-network (Dueling DQN) architecture in deep reinforcement learning.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6ab65923081909acfc61b7a612233 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91c7d8f5c8190a46e9caa2a920fa9 |
completed | April 10, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60a9d2f0c81908352cd9f0167c6ab |
completed | May 2, 2026, 2:30 p.m. |
Created at: April 8, 2026, 9:51 p.m.