Triple
T682845
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naha |
E13218
|
entity |
| Predicate | hasSisterCity |
P919
|
FINISHED |
| Object | Qingdao |
E130067
|
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: Qingdao | Statement: [Naha, hasSisterCity, Qingdao]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Qingdao Context triple: [Naha, hasSisterCity, Qingdao]
-
A.
Qingdao
chosen
Qingdao is a major coastal city in eastern China known for its port, beaches, German colonial architecture, and Tsingtao Brewery.
-
B.
Jinan
Jinan is the capital city of Shandong Province in eastern China, known for its numerous natural springs and rich historical and cultural heritage.
-
C.
Tianjin
Tianjin is a major port city and industrial hub in northern China, located near Beijing along the Bohai Sea.
-
D.
Shanghai
Shanghai is a major global financial hub and China’s largest city, known for its modern skyline, historic waterfront, and role as a center of international business and trade.
-
E.
Taierzhuang
Taierzhuang is a historic town in eastern China’s Shandong province, best known as the site of a major Chinese victory over Japanese forces during the Second Sino-Japanese War.
- 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_69a4933e0f98819097d22766c49b61b8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a070d4c08190a510a8f9c1ae8076 |
completed | March 1, 2026, 8:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac82e8bd788190a20a580bae9bd94e |
completed | March 7, 2026, 7:56 p.m. |
Created at: March 1, 2026, 7:36 p.m.