Triple
T3877536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Satō |
E92538
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Kei Satō
Kei Satō is a Japanese politician and member of the House of Councillors known for his involvement in contemporary national politics.
|
E673415
|
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: Kei Satō | Statement: [Satō, hasNotableBearer, Kei Satō]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kei Satō Context triple: [Satō, hasNotableBearer, Kei Satō]
-
A.
Takeru Satō
Takeru Satō is a Japanese actor best known internationally for starring as Himura Kenshin in the live-action Rurouni Kenshin film series.
-
B.
Katsuya Okada
Katsuya Okada is a Japanese politician who has served as leader of the Democratic Party of Japan and as Deputy Prime Minister.
-
C.
Naoki Tanaka
Naoki Tanaka is a Japanese politician and businessman known as the son of former Prime Minister Kakuei Tanaka and a member of the influential Tanaka political family.
-
D.
Daisuke Kato
Daisuke Kato is a former Japanese professional baseball player best known for his time with the Orix Buffaloes in Nippon Professional Baseball.
-
E.
Takahiro Fujii
Takahiro Fujii is a Japanese voice actor known for providing the voice for the iconic Nintendo character Donkey Kong.
- 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: Kei Satō Triple: [Satō, hasNotableBearer, Kei Satō]
Generated description
Kei Satō is a Japanese politician and member of the House of Councillors known for his involvement in contemporary national politics.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kei Satō Target entity description: Kei Satō is a Japanese politician and member of the House of Councillors known for his involvement in contemporary national politics.
-
A.
Takeru Satō
Takeru Satō is a Japanese actor best known internationally for starring as Himura Kenshin in the live-action Rurouni Kenshin film series.
-
B.
Katsuya Okada
Katsuya Okada is a Japanese politician who has served as leader of the Democratic Party of Japan and as Deputy Prime Minister.
-
C.
Naoki Tanaka
Naoki Tanaka is a Japanese politician and businessman known as the son of former Prime Minister Kakuei Tanaka and a member of the influential Tanaka political family.
-
D.
Daisuke Kato
Daisuke Kato is a former Japanese professional baseball player best known for his time with the Orix Buffaloes in Nippon Professional Baseball.
-
E.
Takahiro Fujii
Takahiro Fujii is a Japanese voice actor known for providing the voice for the iconic Nintendo character Donkey Kong.
- 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_69aed967448c819086c4b358d37b25aa |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec72fa7c81909c73b3cf90597e9a |
completed | March 9, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c85695cc608190aa6ed016bd3f8929 |
completed | March 28, 2026, 10:30 p.m. |
| NEDg | Description generation | batch_69c85765d1f48190b171ff87a15c5b74 |
completed | March 28, 2026, 10:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8580963748190b81bd7437259da28 |
completed | March 28, 2026, 10:36 p.m. |
Created at: March 9, 2026, 3:20 p.m.