Triple
T167097
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wuhan campaign |
E3038
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | Wuhan |
E1680
|
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: Wuhan | Statement: [Wuhan campaign, relatedTo, Wuhan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wuhan Context triple: [Wuhan campaign, relatedTo, Wuhan]
-
A.
Wuhan
chosen
Wuhan is a major city in central China, known as a key industrial, commercial, and transportation hub located at the confluence of the Yangtze and Han rivers.
-
B.
Guangzhou
Guangzhou is a major port city in southern China and the capital of Guangdong Province, known as a key commercial and manufacturing hub in the Pearl River Delta.
-
C.
Shanghai
Shanghai is a major global financial hub and China’s largest city, known for its modern skyline, historic waterfront, and role as a center of international business and trade.
-
D.
Beijing
Beijing is the capital city of China, a major political, cultural, and economic center known for its rich history and rapid modern development.
-
E.
Hefei
Hefei is the capital and largest city of Anhui Province in eastern China, known as a major industrial, scientific, and educational center.
- 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_69a2524ce1e48190ab066bf72859f474 |
completed | Feb. 28, 2026, 2:26 a.m. |
| NER | Named-entity recognition | batch_69a25884c8d88190bc4cb4e3541e8116 |
completed | Feb. 28, 2026, 2:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2e3bf71ac819085b47fd03c853d9e |
completed | Feb. 28, 2026, 12:46 p.m. |
Created at: Feb. 28, 2026, 2:34 a.m.