Triple
T22928083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beixiao Dao |
E569363
|
entity |
| Predicate | hasNearbyIslands |
P19482
|
FINISHED |
| Object | Chiwei Yu |
—
|
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: Chiwei Yu | Statement: [Beixiao Dao, hasNearbyIslands, Chiwei Yu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chiwei Yu Context triple: [Beixiao Dao, hasNearbyIslands, Chiwei Yu]
-
A.
Chiwei Yu
chosen
Chiwei Yu is a small, remote islet in the East China Sea that is part of the disputed Diaoyutai/Senkaku Islands archipelago.
-
B.
Jun-Yan Zhu
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.
-
C.
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.
-
D.
Mingda Chen
Mingda Chen is a researcher in natural language processing known for work on large-scale language models and representation learning, including contributions to the ALBERT model.
-
E.
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.
- 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_69e2458f7d008190901dccbaebeaba24 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f180db6db88190bc8efb691dcdfdba |
completed | April 29, 2026, 3:54 a.m. |
Created at: April 17, 2026, 3:43 p.m.