Triple
T10278855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Itabashi, Tokyo |
E241039
|
entity |
| Predicate | hasFriendshipCity |
P9364
|
FINISHED |
| Object | Wuhan, China |
E1680
|
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: Wuhan, China | Statement: [Itabashi, Tokyo, hasFriendshipCity, Wuhan, China]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wuhan, China Context triple: [Itabashi, Tokyo, hasFriendshipCity, Wuhan, China]
-
A.
Wuhan
chosen
Wuhan is a major city in central China, known as a key industrial, commercial, and transportation hub located at the confluence of the Yangtze and Han rivers.
-
B.
Hsiangcheng, China
Hsiangcheng, China is a town in Henan Province known as the birthplace of author and social critic Os Guinness.
-
C.
Port of Wuhan
The Port of Wuhan is a major inland river port on the Yangtze River in central China, serving as a key hub for regional trade and transportation.
-
D.
Shenzhen, China
Shenzhen, China is a major southern Chinese metropolis known for its rapid transformation into a global technology and manufacturing hub bordering Hong Kong.
-
E.
Huangshi
Huangshi is an industrial city in eastern Hubei Province, China, known for its steel production and location along the Yangtze River.
- 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_69d381a94c1881908fc38fc263d9b9c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4dfbfa26c8190b536655d33112ddf |
completed | April 7, 2026, 10:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f8286b1881908b54037d1798f74a |
completed | April 9, 2026, 12:51 a.m. |
Created at: April 6, 2026, 11:38 a.m.