Triple
T6255328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhenjiang |
E140148
|
entity |
| Predicate | hasCountyLevelCity |
P27799
|
FINISHED |
| Object | Danyang |
E184511
|
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: Danyang | Statement: [Zhenjiang, hasCountyLevelCity, Danyang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Danyang Context triple: [Zhenjiang, hasCountyLevelCity, Danyang]
-
A.
Danyang
chosen
Danyang was an early capital city of the ancient Chinese State of Chu, significant in the formative period of the Chu kingdom’s political and cultural development.
-
B.
Jiangde
Jiangde is a riverside city in China situated along the Xin’an River, known for its scenic landscapes and water-centered local life.
-
C.
Qianjiang
Qianjiang is a city in China known for its regional industry and cultural exchanges, including international town twinning partnerships.
-
D.
Kunshan
Kunshan is a rapidly developing county-level city in Jiangsu Province, China, known for its strong manufacturing economy and proximity to Shanghai and Suzhou.
-
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_69c008b4858c819095b0199114a9a87b |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06363d6008190bf05e003b1f74497 |
completed | March 22, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c2443138788190835ed3fd99f21827 |
completed | March 24, 2026, 7:58 a.m. |
Created at: March 22, 2026, 4:24 p.m.