Triple
T7183373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chizhou |
E167508
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Jingdezhen |
E187002
|
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: Jingdezhen | Statement: [Chizhou, borders, Jingdezhen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jingdezhen Context triple: [Chizhou, borders, Jingdezhen]
-
A.
Jingdezhen
chosen
Jingdezhen is a historic Chinese city world-renowned as the country’s premier center of porcelain and ceramics production.
-
B.
Yixing
Yixing is a county-level city in southern Jiangsu Province, China, renowned for its traditional purple clay teapots and ceramics.
-
C.
Taian
Taian is a prefecture-level city in eastern China's Shandong province, best known as the gateway to the sacred Mount Tai.
-
D.
Quanzhou
Quanzhou is a historic coastal city in southeastern China that flourished as one of the world’s busiest maritime trade hubs, especially during the Song and Yuan dynasties.
-
E.
Nanchang
Nanchang is the capital and largest city of Jiangxi Province in southeastern China, known as an important regional industrial and transportation hub.
- 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_69c6888a7c548190a3d39b52a393080f |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e8bd52648190a22412300254e5d4 |
completed | March 27, 2026, 8:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7b94390d48190b7443c9c8cc41625 |
completed | March 28, 2026, 11:19 a.m. |
Created at: March 27, 2026, 2:49 p.m.