Triple
T2345991
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baoshan District |
E45131
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Yuepu Town |
E257580
|
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: Yuepu Town | Statement: [Baoshan District, contains, Yuepu Town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yuepu Town Context triple: [Baoshan District, contains, Yuepu Town]
-
A.
Miaohang Town
Miaohang Town is a suburban township-level division in the northern part of Shanghai, China, known for its mix of residential, industrial, and developing urban areas.
-
B.
Xikou Town
Xikou Town is a historic town in Fenghua District, Ningbo, Zhejiang Province, best known as the hometown of Chiang Kai-shek and a popular cultural and tourist destination.
-
C.
Jinhu Township
Jinhu Township is a rural coastal township in Kinmen County, Taiwan, known for its military history, traditional villages, and role as a frontline outpost near mainland China.
-
D.
Luodian Town
chosen
Luodian Town is a suburban town in Shanghai, China, known for its residential communities and local commerce within Baoshan District.
-
E.
Xinzhuang
Xinzhuang is a major suburban town and transportation hub in Shanghai, China, known for its busy commercial areas and key metro and rail connections.
- 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_69a88917935081909b755dbf38e81024 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abc6c7cb9481909405aeb503f804ae |
completed | March 7, 2026, 6:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af2b6940f4819094998ddbb62efd19 |
completed | March 9, 2026, 8:19 p.m. |
Created at: March 4, 2026, 7:52 p.m.