Triple
T8919401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beijing–Shanghai railway |
E212372
|
entity |
| Predicate | connects |
P390
|
FINISHED |
| Object | Kunshan |
E292977
|
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: Kunshan | Statement: [Beijing–Shanghai railway, connects, Kunshan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kunshan Context triple: [Beijing–Shanghai railway, connects, Kunshan]
-
A.
Kunshan
chosen
Kunshan is a rapidly developing county-level city in Jiangsu Province, China, known for its strong manufacturing economy and proximity to Shanghai and Suzhou.
-
B.
Changshu
Changshu is a county-level city in Jiangsu Province, eastern China, known for its textile industry, historic sites, and location near Suzhou and Shanghai.
-
C.
Zhangjiagang
Zhangjiagang is a county-level city in Jiangsu Province, China, known as a prosperous port and industrial hub along the Yangtze River.
-
D.
Zhenjiang
Zhenjiang is a historic port city in eastern China known for its strategic location on the Yangtze River and its rich cultural and culinary heritage.
-
E.
Liyang
Liyang is a county-level city in Jiangsu Province, China, known for its scenic attractions such as Tianmu Lake and its administration under the prefecture-level city of Changzhou.
- 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_69ca8393b1808190bd4336787ffa2c40 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6613639881909090d060f388a865 |
completed | April 1, 2026, 12:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d047408b20819084d0b9b831f0f2c0 |
completed | April 3, 2026, 11:03 p.m. |
Created at: March 30, 2026, 6:56 p.m.