Triple
T22850118
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baima language |
E566334
|
entity |
| Predicate | spokenInCounty |
P105511
|
FINISHED |
| Object | Nanping area |
—
|
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: Nanping area | Statement: [Baima language, spokenInCounty, Nanping area]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nanping area Context triple: [Baima language, spokenInCounty, Nanping area]
-
A.
Amoy region
The Amoy region is a coastal area in southern Fujian, China, centered around the city of Xiamen, known historically as a major port and cultural hub of the Southern Min–speaking world.
-
B.
Nanping
Nanping is a town-level division within Yixian County in China, known as one of its local administrative settlements.
-
C.
Nanping
chosen
Nanping is a prefecture-level city in northern Fujian Province, China, known for its mountainous terrain, rich biodiversity, and role as a regional transport and economic hub.
-
D.
Nanshi area
The Nanshi area is Shanghai’s historic old city quarter, known for its traditional streets, markets, and cultural heritage within the modern Huangpu District.
-
E.
Lianzhou
Lianzhou is a town-level settlement located within Doumen District of Zhuhai in Guangdong Province, China.
- 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_69e2458750b481908a8e4cf4609cc6cf |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17eb74700819090d191b3a7a17034 |
completed | April 29, 2026, 3:44 a.m. |
Created at: April 17, 2026, 3:36 p.m.