Triple
T4962276
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nanshan District |
E111435
|
entity |
| Predicate | localLanguage |
P1252
|
FINISHED |
| Object | Hakka |
E34449
|
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: Hakka | Statement: [Nanshan District, localLanguage, Hakka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hakka Context triple: [Nanshan District, localLanguage, Hakka]
-
A.
Hakka
chosen
Hakka is a Sinitic language spoken primarily by the Hakka people across southern China and various overseas Chinese communities.
-
B.
Hokkien
Hokkien is a Southern Min Chinese language variety widely spoken in Taiwan, Southeast Asia, and parts of southern China, known for its rich tonal system and distinct vocabulary from Mandarin.
-
C.
Chōmin
Chōmin was the pen name of Nakae Chōmin, a prominent Japanese political theorist, journalist, and early advocate of liberal democracy during the Meiji era.
-
D.
Guanggu
Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
-
E.
Hui
The Hui are a predominantly Muslim ethnic group in China known for their integration of Islamic faith with Han Chinese language and cultural practices.
- 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_69bd4419393c819086319a6fe4bf8542 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd71f3e6148190b99f35734220ffc3 |
completed | March 20, 2026, 4:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be81ea8cc08190af40098ca99364f1 |
completed | March 21, 2026, 11:32 a.m. |
Created at: March 20, 2026, 1:32 p.m.