Triple
T1120344
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wuhan |
E11195
|
entity |
| Predicate | hasNickname |
P39
|
FINISHED |
| Object | Jiangcheng |
E136087
|
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: Jiangcheng | Statement: [Wuhan, hasNickname, Jiangcheng]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jiangcheng Context triple: [Wuhan, hasNickname, Jiangcheng]
-
A.
Jiangcheng
chosen
Jiangcheng is a Chinese city commonly known by the nickname "River City," reflecting its close association with nearby waterways.
-
B.
Shëngjin
Shëngjin is a coastal town and port in northwestern Albania on the Adriatic Sea, historically significant for its strategic maritime position.
-
C.
Zhizhong
Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
-
D.
Xinjing
Xinjing was the capital city of the Japanese puppet state of Manchukuo in northeastern China during the 1930s and early 1940s.
-
E.
Ma’anshan
Ma’anshan is an industrial city in eastern China known for its steel production and location along the lower 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_69a493252a648190ac48f8742474a5e8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4bbbca3348190a607ce147b2ae70e |
completed | March 1, 2026, 10:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac7f2dc92481909ee6d9d6d4257f1b |
completed | March 7, 2026, 7:40 p.m. |
Created at: March 1, 2026, 7:43 p.m.