Triple
T15385121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ryu |
E367896
|
entity |
| Predicate | teacher |
P335
|
FINISHED |
| Object |
Gouken
Gouken is a legendary martial arts master from the Street Fighter series, best known for training Ryu and Ken in the Ansatsuken fighting style.
|
E1156376
|
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: Gouken | Statement: [Ryu, teacher, Gouken]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gouken Context triple: [Ryu, teacher, Gouken]
-
A.
Kenkichi
Kenkichi is a Japanese masculine given name that can be written with various kanji combinations and has been borne by numerous notable figures in fields such as sports, politics, and the arts.
-
B.
Kinsaku
Kinsaku is the birth name of Matsuo Bashō, the renowned 17th-century Japanese haiku poet and literary figure.
-
C.
Takaichi
Takaichi is a Japanese surname most prominently associated with conservative politician Sanae Takaichi.
-
D.
Yorii
Yorii is a town in Saitama Prefecture, Japan, known as a regional residential and commuter hub connected to the greater Tokyo area.
-
E.
Ryogo
Ryogo is a Japanese given name most notably borne by theoretical physicist Ryogo Kubo, known for his contributions to statistical mechanics and the fluctuation-dissipation theorem.
- 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: Gouken Triple: [Ryu, teacher, Gouken]
Generated description
Gouken is a legendary martial arts master from the Street Fighter series, best known for training Ryu and Ken in the Ansatsuken fighting style.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gouken Target entity description: Gouken is a legendary martial arts master from the Street Fighter series, best known for training Ryu and Ken in the Ansatsuken fighting style.
-
A.
Kenkichi
Kenkichi is a Japanese masculine given name that can be written with various kanji combinations and has been borne by numerous notable figures in fields such as sports, politics, and the arts.
-
B.
Kinsaku
Kinsaku is the birth name of Matsuo Bashō, the renowned 17th-century Japanese haiku poet and literary figure.
-
C.
Takaichi
Takaichi is a Japanese surname most prominently associated with conservative politician Sanae Takaichi.
-
D.
Yorii
Yorii is a town in Saitama Prefecture, Japan, known as a regional residential and commuter hub connected to the greater Tokyo area.
-
E.
Ryogo
Ryogo is a Japanese given name most notably borne by theoretical physicist Ryogo Kubo, known for his contributions to statistical mechanics and the fluctuation-dissipation theorem.
- 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_69d85a1551a08190ba2caea7cd51c639 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e74ff70819094c1a85f51d6e228 |
completed | April 16, 2026, 1:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff1a6b67c08190b0df6b9fd65ff28b |
completed | May 9, 2026, 11:28 a.m. |
| NEDg | Description generation | batch_69ff1b0e319881909af55b7c7360cc0e |
completed | May 9, 2026, 11:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff1bb6907c8190a3116d122303a1c6 |
completed | May 9, 2026, 11:34 a.m. |
Created at: April 10, 2026, 3:19 a.m.