Triple
T1603654
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hakka |
E34449
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object | Sixian 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: Sixian Hakka | Statement: [Hakka, hasDialect, Sixian Hakka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sixian Hakka Context triple: [Hakka, hasDialect, Sixian Hakka]
-
A.
Hakka
chosen
Hakka is a Sinitic language spoken primarily by the Hakka people across southern China and various overseas Chinese communities.
-
B.
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.
-
C.
Shina
Shina is an Indo-Aryan language spoken primarily in the Gilgit-Baltistan region of Pakistan and surrounding Himalayan areas.
-
D.
Wei-kuo
Wei-kuo is the given name of Chiang Wei-kuo, a Chinese military officer and adopted son of Chiang Kai-shek who served in both German and later Republic of China forces.
-
E.
Chō
Chō is a Japanese surname borne by various notable individuals across fields such as the military, arts, and entertainment.
- 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_69a885fea6a481909fe83ba6441f1774 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9094f96ec819090286c21b3dfddd5 |
completed | March 5, 2026, 4:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad51bcdebc81909520786c560598b6 |
completed | March 8, 2026, 10:38 a.m. |
Created at: March 4, 2026, 7:28 p.m.