Triple
T32169549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 京都大学大学院情報学研究科 |
E821670
|
entity |
| Predicate | affiliation |
P10
|
FINISHED |
| Object |
国立大学法人京都大学
国立大学法人京都大学は、日本を代表する総合研究大学の一つであり、多様な学術分野で世界的な研究と高度な教育を行う国立大学法人です。
|
E914081
|
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: 国立大学法人京都大学 | Statement: [京都大学大学院情報学研究科, affiliation, 国立大学法人京都大学]
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: 国立大学法人京都大学 Triple: [京都大学大学院情報学研究科, affiliation, 国立大学法人京都大学]
Generated description
国立大学法人京都大学は、日本を代表する総合研究大学の一つであり、多様な学術分野で世界的な研究と高度な教育を行う国立大学法人です。
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_69f3490699a48190bbef96b198e8fade |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6ba23d58c8190a56f876c61b6735c |
completed | May 3, 2026, 2:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2f0bd90ffc819083310e0650e4e1f9 |
completed | June 14, 2026, 8:15 p.m. |
| NEDg | Description generation | batch_6a2f15db9f1c8190852475cbdbafdd67 |
completed | June 14, 2026, 8:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a2f16e68f588190b18717146305a932 |
completed | June 14, 2026, 9:02 p.m. |
Created at: May 1, 2026, 12:33 a.m.