Triple
T32421631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 情報理工学系研究科 |
E828474
|
entity |
| Predicate | officialName |
P66
|
FINISHED |
| Object |
東京大学大学院情報理工学系研究科
東京大学大学院情報理工学系研究科は、東京大学において情報科学・情報工学分野の高度な教育と最先端研究を担う大学院研究科です。
|
E2006329
|
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: 東京大学大学院情報理工学系研究科 | Statement: [情報理工学系研究科, officialName, 東京大学大学院情報理工学系研究科]
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: 東京大学大学院情報理工学系研究科 Triple: [情報理工学系研究科, officialName, 東京大学大学院情報理工学系研究科]
Generated description
東京大学大学院情報理工学系研究科は、東京大学において情報科学・情報工学分野の高度な教育と最先端研究を担う大学院研究科です。
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_69f3491b28bc8190b75cea7a507f337b |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c28269d08190a72a4ca90e219286 |
completed | May 3, 2026, 3:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a344f26753881909bae5c3a867dce5a |
completed | June 18, 2026, 8:03 p.m. |
| NEDg | Description generation | batch_6a3450be40708190a12bfb352cd508d0 |
completed | June 18, 2026, 8:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a345c04b9c88190b95ae85c88b42f17 |
completed | June 18, 2026, 8:58 p.m. |
Created at: May 1, 2026, 12:54 a.m.