Triple
T22740238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shinkyō |
E562390
|
entity |
| Predicate | otherLanguageName |
P13426
|
FINISHED |
| Object | Xinjing |
—
|
NE NERFINISHED |
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: Xinjing | Statement: [Shinkyō, otherLanguageName, Xinjing]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Xinjing Context triple: [Shinkyō, otherLanguageName, Xinjing]
-
A.
Xinjing
chosen
Xinjing was the capital city of the Japanese puppet state of Manchukuo in northeastern China during the 1930s and early 1940s.
-
B.
Shëngjin
Shëngjin is a coastal town and port in northwestern Albania on the Adriatic Sea, historically significant for its strategic maritime position.
-
C.
Yanjing
Yanjing was a historic Chinese capital city, best known as the former name of modern-day Beijing.
-
D.
Nánníng
Nánníng is the capital and largest city of China’s Guangxi Zhuang Autonomous Region, known as a major economic hub and “Green City” for its abundant subtropical vegetation.
-
E.
Jianye
Jianye is an ancient name for the city now known as Nanjing, a historically significant capital in several Chinese dynasties.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245513a5c81908d5cb471b4fc429d |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1797241588190be67db7a37a88f23 |
completed | April 29, 2026, 3:22 a.m. |
Created at: April 17, 2026, 3:23 p.m.