Triple
T21942966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Enping |
E541866
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Yangjiang |
—
|
NE NERFINISHED |
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: Yangjiang | Statement: [Enping, borders, Yangjiang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yangjiang Context triple: [Enping, borders, Yangjiang]
-
A.
Yangjiang
chosen
Yangjiang is a coastal prefecture-level city in southwestern Guangdong Province, China, known for its cutlery industry and beaches along the South China Sea.
-
B.
Qianjiang
Qianjiang is a city in China known for its regional industry and cultural exchanges, including international town twinning partnerships.
-
C.
Sichun
Sichun is a Chinese given name notably borne by actress Ma Sichun, known for her roles in contemporary Chinese cinema and television.
-
D.
Jingjiang
Jingjiang is a county-level city in Jiangsu Province, China, situated along the Yangtze River and known for its river port and industrial economy.
-
E.
Lengshuijiang
Lengshuijiang is a county-level city under the administration of Loudi in Hunan Province, China, known for its industrial development and mining resources.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1242345dc8190aa6ddf61cf864e2d |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:56 p.m.