Triple
T9547090
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nujiang |
E230319
|
entity |
| Predicate | borderProvince |
P32798
|
FINISHED |
| Object | Baoshan City |
E193111
|
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: Baoshan City | Statement: [Nujiang, borderProvince, Baoshan City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baoshan City Context triple: [Nujiang, borderProvince, Baoshan City]
-
A.
Baoshan
chosen
Baoshan is a prefecture-level city in southwestern China known for its mountainous landscapes, border trade, and location along historical routes connecting Yunnan to Myanmar.
-
B.
Changning City
Changning City is a county-level city in Hunan Province, China, administered by the prefecture-level city of Hengyang.
-
C.
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.
-
D.
Xinhui
Xinhui is a district in Jiangmen, Guangdong Province, China, historically known as a significant hometown of overseas Chinese and part of the Pearl River Delta region.
-
E.
Zhangjiagang
Zhangjiagang is a county-level city in Jiangsu Province, China, known as a prosperous port and industrial hub along the Yangtze River.
- 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_69ca847c70b8819088a0a0bad64a50d6 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9902fca081909125660ae6336d3f |
completed | April 1, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d14c747e608190b2fa470324fff454 |
completed | April 4, 2026, 5:37 p.m. |
Created at: March 30, 2026, 8:02 p.m.