Triple
T3190440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Katō Tomosaburō |
E66808
|
entity |
| Predicate | nativeName |
P15
|
FINISHED |
| Object |
加藤 友三郎
加藤友三郎 was a Japanese admiral and statesman who served as Prime Minister of Japan in the early 1920s.
|
E336614
|
NE FINISHED |
How this triple was built (4 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: 加藤 友三郎 | Statement: [Katō Tomosaburō, nativeName, 加藤 友三郎]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 加藤 友三郎 Context triple: [Katō Tomosaburō, nativeName, 加藤 友三郎]
-
A.
山田 乙三
山田 乙三 was a Japanese general in the Imperial Japanese Army who played a significant role in military operations during the Second Sino-Japanese War and World War II.
-
B.
朝永振一郎
朝永振一郎 was a Japanese theoretical physicist and Nobel laureate renowned for his fundamental contributions to quantum electrodynamics.
-
C.
本間雅晴
本間雅晴は、第二次世界大戦期にフィリピン侵攻作戦を指揮し、戦後に戦争犯罪で裁かれた日本陸軍の軍人である。
-
D.
山本
山本 is a common Japanese surname borne by many notable figures across fields such as politics, sports, and the arts.
-
E.
松井 石根
松井石根 was a Japanese Imperial Army general during the Second Sino-Japanese War who was later convicted as a war criminal for his role in the Nanjing Massacre.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: 加藤 友三郎 Triple: [Katō Tomosaburō, nativeName, 加藤 友三郎]
Generated description
加藤友三郎 was a Japanese admiral and statesman who served as Prime Minister of Japan in the early 1920s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 加藤 友三郎 Target entity description: 加藤友三郎 was a Japanese admiral and statesman who served as Prime Minister of Japan in the early 1920s.
-
A.
山田 乙三
山田 乙三 was a Japanese general in the Imperial Japanese Army who played a significant role in military operations during the Second Sino-Japanese War and World War II.
-
B.
朝永振一郎
朝永振一郎 was a Japanese theoretical physicist and Nobel laureate renowned for his fundamental contributions to quantum electrodynamics.
-
C.
本間雅晴
本間雅晴は、第二次世界大戦期にフィリピン侵攻作戦を指揮し、戦後に戦争犯罪で裁かれた日本陸軍の軍人である。
-
D.
山本
山本 is a common Japanese surname borne by many notable figures across fields such as politics, sports, and the arts.
-
E.
松井 石根
松井石根 was a Japanese Imperial Army general during the Second Sino-Japanese War who was later convicted as a war criminal for his role in the Nanjing Massacre.
- F. None of above. chosen
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_69ad8588ba18819086a10951c32ecb80 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada6e7d0d081908b1c36bb909a58bf |
completed | March 8, 2026, 4:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b24b9a9bc88190b7090bda8fe6260c |
completed | March 12, 2026, 5:14 a.m. |
| NEDg | Description generation | batch_69b24d677ca8819094cb03360ac885da |
completed | March 12, 2026, 5:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b25178f3c08190be78bdbd0cdfc5f3 |
completed | March 12, 2026, 5:39 a.m. |
Created at: March 8, 2026, 3:07 p.m.