Triple
T21868744
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jiphyeonjeon |
E539949
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Han Yang |
—
|
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: Han Yang | Statement: [Jiphyeonjeon, locatedIn, Han Yang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Han Yang Context triple: [Jiphyeonjeon, locatedIn, Han Yang]
-
A.
Han Yang
chosen
Han Yang, historically known as the capital of the Joseon Dynasty and now part of Seoul, was a major political and cultural center of Korea.
-
B.
Guandu
Guandu is a district in northern Taipei, Taiwan, known for its riverside wetlands, hot springs, and the historic Guandu Temple.
-
C.
Dajing
Dajing is a Chinese given name notably borne by Olympic short track speed skating champion Wu Dajing.
-
D.
Wafangdian
Wafangdian is a county-level city in Liaoning Province, China, known for its bearing industry and as an important satellite city of Dalian.
-
E.
Yiheyuan
Yiheyuan, known in English as the Summer Palace, is a vast imperial garden and former royal retreat in Beijing famed for its lakes, palaces, and classical Chinese landscape design.
- 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_69e0c478f59081909d54302b57fc1ce3 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f0f33305d081908cd070134420607a |
completed | April 28, 2026, 5:49 p.m. |
Created at: April 16, 2026, 6:57 p.m.