Triple
T13428824
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northern Tai |
E313552
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object | Fusui Zhuang |
E1042373
|
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: Fusui Zhuang | Statement: [Northern Tai, hasMember, Fusui Zhuang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fusui Zhuang Context triple: [Northern Tai, hasMember, Fusui Zhuang]
-
A.
Fusui Zhuang
chosen
Fusui Zhuang is a variety of the Zhuang language spoken by the Zhuang people in the Fusui region of Guangxi, southern China.
-
B.
Fangzhuang
Fangzhuang is a residential neighborhood and commercial area in Beijing, China, known as one of the city’s earlier large-scale planned communities.
-
C.
Jingxi Zhuang
Jingxi Zhuang is a variety of the Zhuang language spoken in the Jingxi region of Guangxi, China, belonging to the Central Tai branch of the Tai–Kadai language family.
-
D.
Xingyuan
Xingyuan was the Chinese era name used during part of Emperor Dezong of Tang’s reign in the late eighth century.
-
E.
Shangyuan
Shangyuan was a Chinese imperial era name used during the reign of Emperor Suzong of the Tang dynasty.
- 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_69d806ad0c44819088833ae1ec9e9690 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaed304ac8190a8021f749de8164c |
completed | April 12, 2026, 2:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75d86d32c8190a1d9ce72e99426e2 |
completed | May 3, 2026, 2:36 p.m. |
Created at: April 9, 2026, 9:40 p.m.