Triple
T22431874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siyi Yue |
E554518
|
entity |
| Predicate | hasDialectRegion |
P48907
|
FINISHED |
| Object | Kaiping |
—
|
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: Kaiping | Statement: [Siyi Yue, hasDialectRegion, Kaiping]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kaiping Context triple: [Siyi Yue, hasDialectRegion, Kaiping]
-
A.
Kaiping
chosen
Kaiping is a county-level city in Guangdong Province, China, known for its distinctive diaolou watchtowers and as part of the Sze Yup region with a strong overseas Chinese heritage.
-
B.
Kaiping District
Kaiping District is an urban district under the administration of Tangshan City in Hebei Province, China, known for its industrial base and coal-related industries.
-
C.
Lianyungang
Lianyungang is a major coastal city and seaport in eastern China, serving as an important transportation and trade hub on the Yellow Sea.
-
D.
Huludao
Huludao is a coastal city in southwestern Liaoning Province, China, known for its port, shipbuilding industry, and seaside tourism.
-
E.
Panjin
Panjin is an industrial and oil-producing city in northeastern China, best known for its striking Red Beach wetlands along the Bohai Sea.
- 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_69e11e4f2d0c819091aa3558ea2ee630 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15a32139481909baaf9275f5e0257 |
completed | April 29, 2026, 1:09 a.m. |
Created at: April 16, 2026, 8:47 p.m.