Triple
T3877547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Satō |
E92538
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Shun Satō
Shun Satō is a Japanese figure skater known for competing internationally in men's singles events.
|
E684611
|
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: Shun Satō | Statement: [Satō, hasNotableBearer, Shun Satō]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shun Satō Context triple: [Satō, hasNotableBearer, Shun Satō]
-
A.
Kei Satō
Kei Satō is a Japanese politician and member of the House of Councillors known for his involvement in contemporary national politics.
-
B.
Ryo Satō
Ryo Satō is a Japanese personal name shared by multiple individuals, commonly appearing in contexts such as sports, entertainment, and other public professions in Japan.
-
C.
Kenta Satō
Kenta Satō is a Japanese individual notable enough to be recognized as a bearer of the surname Satō, though specific widely known achievements or roles are not clearly established.
-
D.
Kenji Satō
Kenji Satō is a Japanese individual notable enough to be specifically cited as a bearer of the common Japanese surname Satō.
-
E.
Takeru Satō
Takeru Satō is a Japanese actor best known internationally for starring as Himura Kenshin in the live-action Rurouni Kenshin film series.
- 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: Shun Satō Triple: [Satō, hasNotableBearer, Shun Satō]
Generated description
Shun Satō is a Japanese figure skater known for competing internationally in men's singles events.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shun Satō Target entity description: Shun Satō is a Japanese figure skater known for competing internationally in men's singles events.
-
A.
Kei Satō
Kei Satō is a Japanese politician and member of the House of Councillors known for his involvement in contemporary national politics.
-
B.
Ryo Satō
Ryo Satō is a Japanese personal name shared by multiple individuals, commonly appearing in contexts such as sports, entertainment, and other public professions in Japan.
-
C.
Kenta Satō
Kenta Satō is a Japanese individual notable enough to be recognized as a bearer of the surname Satō, though specific widely known achievements or roles are not clearly established.
-
D.
Kenji Satō
Kenji Satō is a Japanese individual notable enough to be specifically cited as a bearer of the common Japanese surname Satō.
-
E.
Takeru Satō
Takeru Satō is a Japanese actor best known internationally for starring as Himura Kenshin in the live-action Rurouni Kenshin film series.
- 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_69c8b4d712008190aa25340e1feb0804 |
completed | March 29, 2026, 5:12 a.m. |
| NEDg | Description generation | batch_69c8b5d3ed1c8190ad4e95229f91ca23 |
completed | March 29, 2026, 5:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8b63c93b48190bab5314723ed4c24 |
completed | March 29, 2026, 5:18 a.m. |
Created at: March 9, 2026, 3:20 p.m.