Triple
T23950387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 北区 (蔚山市) |
E603029
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object |
庆尚南道(广域市升格前历史区域)
庆尚南道(广域市升格前历史区域)是韩国东南部以农业、渔业和重工业为主的传统行政区域,后因广域市设立等行政区划调整而发生边界和管辖变动。
|
E1609849
|
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: 庆尚南道(广域市升格前历史区域) | Statement: [北区 (蔚山市), locatedIn, 庆尚南道(广域市升格前历史区域)]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: 庆尚南道(广域市升格前历史区域) Triple: [北区 (蔚山市), locatedIn, 庆尚南道(广域市升格前历史区域)]
Generated description
庆尚南道(广域市升格前历史区域)是韩国东南部以农业、渔业和重工业为主的传统行政区域,后因广域市设立等行政区划调整而发生边界和管辖变动。
Provenance (5 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_69e2953e4924819093f1c24c03476b42 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d03140f08190b4356626628ff56f |
completed | April 29, 2026, 9:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0f765369908190b92e22095f9a3032 |
completed | May 21, 2026, 9:17 p.m. |
| NEDg | Description generation | batch_6a0f770a063c81909f356346c9c521ad |
completed | May 21, 2026, 9:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0f782b47c08190a221c196ddc9d566 |
completed | May 21, 2026, 9:24 p.m. |
Created at: April 17, 2026, 9:19 p.m.