Triple
T22888354
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Namuyi language |
E567666
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Panzhihua |
—
|
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: Panzhihua | Statement: [Namuyi language, region, Panzhihua]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Panzhihua Context triple: [Namuyi language, region, Panzhihua]
-
A.
Panzhihua
chosen
Panzhihua is a major industrial city in southwestern China known for its rich mineral resources, especially vanadium-titanium magnetite, and its role as a key steel-producing center.
-
B.
Shicheng
Shicheng is a Chinese given name notably borne by the late Qing dynasty general Nie Shicheng.
-
C.
Xichang
Xichang is a city in Sichuan Province, China, known as a major hub for the country’s space launch activities and related aerospace industry.
-
D.
Huicheng
Huicheng is a principal urban town and administrative center within Xinhui District of Jiangmen, Guangdong, China.
-
E.
Quidong
Quidong is a rural locality in New South Wales, Australia, situated within the Snowy Monaro region.
- 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_69e2458a92ec81908fc1cd5f6407d2ab |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17fc2adb4819081bce7e6849ba31a |
completed | April 29, 2026, 3:49 a.m. |
Created at: April 17, 2026, 3:40 p.m.