Triple
T8658053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ulanqab |
E205471
|
entity |
| Predicate | seat |
P75
|
FINISHED |
| Object | Jining District |
E785695
|
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: Jining District | Statement: [Ulanqab, seat, Jining District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jining District Context triple: [Ulanqab, seat, Jining District]
-
A.
Jining District
chosen
Jining District is an urban administrative district in Inner Mongolia, China, serving as the political and economic center of Ulanqab.
-
B.
Zhangdian District
Zhangdian District is the central urban district and administrative, commercial, and transportation hub of Zibo in Shandong Province, China.
-
C.
Tieshangang District
Tieshangang District is an administrative district of the coastal city of Beihai in Guangxi, China, known for its port and industrial activities.
-
D.
Quanshan District
Quanshan District is an urban administrative district of Xuzhou in Jiangsu Province, China, known as one of the city’s central built-up areas.
-
E.
Licheng District
Licheng District is a central urban district of Quanzhou in Fujian Province, China, known for its historic architecture and cultural heritage.
- 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_69ca8350897c819086cde7596fbe5fe7 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc486d576081908ad28749c7971432 |
completed | March 31, 2026, 10:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d07719ee048190ac4045017d89e938 |
completed | April 4, 2026, 2:27 a.m. |
Created at: March 30, 2026, 6:30 p.m.