Triple
T33464625
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 정명훈 |
E857011
|
entity |
| Predicate | 지휘한악단 |
P120659
|
FINISHED |
| Object |
산타 체칠리아 국립 아카데미 오케스트라
산타 체칠리아 국립 아카데미 오케스트라는 이탈리아 로마를 기반으로 한 세계적인 명성을 지닌 대표적인 교향악단이다.
|
E2052449
|
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: 산타 체칠리아 국립 아카데미 오케스트라 | Statement: [정명훈, 지휘한악단, 산타 체칠리아 국립 아카데미 오케스트라]
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: 산타 체칠리아 국립 아카데미 오케스트라 Triple: [정명훈, 지휘한악단, 산타 체칠리아 국립 아카데미 오케스트라]
Generated description
산타 체칠리아 국립 아카데미 오케스트라는 이탈리아 로마를 기반으로 한 세계적인 명성을 지닌 대표적인 교향악단이다.
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_69f34973461481909c701c98ebd75623 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f66651cc8190bfb5249d15ca8614 |
completed | May 3, 2026, 7:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a35816d81148190bc7a689b12bafe4f |
completed | June 19, 2026, 5:50 p.m. |
| NEDg | Description generation | batch_6a358f677ca48190ada1e1aba6dac731 |
completed | June 19, 2026, 6:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a358fd0dcf48190a52bcd61b642541a |
completed | June 19, 2026, 6:52 p.m. |
Created at: May 1, 2026, 1:37 a.m.