Triple
T8342572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naoto Kan |
E195955
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Naoto
Naoto is a Japanese given name commonly used for males and borne by various notable figures in politics, entertainment, and sports.
|
E728430
|
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: Naoto | Statement: [Naoto Kan, givenName, Naoto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Naoto Context triple: [Naoto Kan, givenName, Naoto]
-
A.
Masato Otaka
Masato Otaka is a Japanese architect associated with the Metabolism movement, known for his contributions to postwar urban planning and visionary megastructure designs.
-
B.
Mako Komuro
Mako Komuro is a former Japanese imperial family member and niece of Emperor Naruhito who left royal status upon marrying commoner Kei Komuro.
-
C.
Toru Watanabe
Toru Watanabe is the introspective university student protagonist of Haruki Murakami’s novel "Norwegian Wood," whose coming-of-age story explores love, loss, and emotional turmoil in 1960s Tokyo.
-
D.
Makoto Uchida
Makoto Uchida is a Japanese automotive executive who serves as the chief executive officer of Nissan Motor Co.
-
E.
Takeharu
Takeharu is a Japanese given name commonly used for males.
- 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: Naoto Triple: [Naoto Kan, givenName, Naoto]
Generated description
Naoto is a Japanese given name commonly used for males and borne by various notable figures in politics, entertainment, and sports.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Naoto Target entity description: Naoto is a Japanese given name commonly used for males and borne by various notable figures in politics, entertainment, and sports.
-
A.
Masato Otaka
Masato Otaka is a Japanese architect associated with the Metabolism movement, known for his contributions to postwar urban planning and visionary megastructure designs.
-
B.
Mako Komuro
Mako Komuro is a former Japanese imperial family member and niece of Emperor Naruhito who left royal status upon marrying commoner Kei Komuro.
-
C.
Toru Watanabe
Toru Watanabe is the introspective university student protagonist of Haruki Murakami’s novel "Norwegian Wood," whose coming-of-age story explores love, loss, and emotional turmoil in 1960s Tokyo.
-
D.
Makoto Uchida
Makoto Uchida is a Japanese automotive executive who serves as the chief executive officer of Nissan Motor Co.
-
E.
Takeharu
Takeharu is a Japanese given name commonly used for males.
- 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_69ca82ecbdc481908a55cad8ca062d88 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7fe9efec81908e0c9ded3963bac5 |
completed | March 31, 2026, 8:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cdc72bc43c81909d95c7eb6aefc403 |
completed | April 2, 2026, 1:32 a.m. |
| NEDg | Description generation | batch_69cdcb90bec88190a2c19681405aa13e |
completed | April 2, 2026, 1:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cdcd0fc9488190a0a576c385b9bc1f |
completed | April 2, 2026, 1:57 a.m. |
Created at: March 30, 2026, 5:58 p.m.