Triple
T498607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North China |
E10349
|
entity |
| Predicate | economicCenter |
P164
|
FINISHED |
| Object | Beijing |
E2312
|
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: Beijing | Statement: [North China, economicCenter, Beijing]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beijing Context triple: [North China, economicCenter, Beijing]
-
A.
Beijing
chosen
Beijing is the capital city of China, a major political, cultural, and economic center known for its rich history and rapid modern development.
-
B.
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.
-
C.
Tianjin
Tianjin is a major port city and industrial hub in northern China, located near Beijing along the Bohai Sea.
-
D.
Wuhan
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.
-
E.
Shenyang
Shenyang is a major industrial and historical city in northeastern China and the capital of Liaoning Province.
- 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_69a2e847df8481909239ec08ccf1e376 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f1183e988190bce70932a9678134 |
completed | Feb. 28, 2026, 1:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a58030d0e48190a6390478e62f8659 |
completed | March 2, 2026, 12:18 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.