Triple
T6735479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tanaka |
E153742
|
entity |
| Predicate | hasNotablePerson |
P304
|
FINISHED |
| Object |
Tanaka Kei
Tanaka Kei is a Japanese actor known for his roles in television dramas and films.
|
E822567
|
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: Tanaka Kei | Statement: [Tanaka, hasNotablePerson, Tanaka Kei]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tanaka Kei Context triple: [Tanaka, hasNotablePerson, Tanaka Kei]
-
A.
Tanaka Koki
Tanaka Koki is a Japanese entertainer best known as a former member of the popular boy band KAT-TUN.
-
B.
Yosaki Takahashi
Yosaki Takahashi is a Japanese politician who serves as the mayor of Kawagoe in Saitama Prefecture.
-
C.
Tanaka Tatsuya
Tanaka Tatsuya is a Japanese artist and photographer best known for his imaginative miniature dioramas that recreate everyday scenes using tiny figurines and household objects.
-
D.
Okinori Kaya
Okinori Kaya was a Japanese bureaucrat and politician who served as Finance Minister before and during World War II and was later convicted as a Class A war criminal.
-
E.
Takuma Tono
Takuma Tono was a Japanese landscape architect known for bringing traditional Japanese garden design to international prominence, including in the United States.
- 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: Tanaka Kei Triple: [Tanaka, hasNotablePerson, Tanaka Kei]
Generated description
Tanaka Kei is a Japanese actor known for his roles in television dramas and films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tanaka Kei Target entity description: Tanaka Kei is a Japanese actor known for his roles in television dramas and films.
-
A.
Tanaka Koki
Tanaka Koki is a Japanese entertainer best known as a former member of the popular boy band KAT-TUN.
-
B.
Yosaki Takahashi
Yosaki Takahashi is a Japanese politician who serves as the mayor of Kawagoe in Saitama Prefecture.
-
C.
Tanaka Tatsuya
Tanaka Tatsuya is a Japanese artist and photographer best known for his imaginative miniature dioramas that recreate everyday scenes using tiny figurines and household objects.
-
D.
Okinori Kaya
Okinori Kaya was a Japanese bureaucrat and politician who served as Finance Minister before and during World War II and was later convicted as a Class A war criminal.
-
E.
Takuma Tono
Takuma Tono was a Japanese landscape architect known for bringing traditional Japanese garden design to international prominence, including in the United States.
- 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_69c6880bdd68819097de8b6099992682 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d16ecbe08190b019d547f631a725 |
completed | March 27, 2026, 6:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1cc2600208190b996bac5845bd385 |
completed | April 5, 2026, 2:42 a.m. |
| NEDg | Description generation | batch_69d1ccd04b60819085a5bde42605ecf5 |
completed | April 5, 2026, 2:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1cd70aa5481908b67afef279c38af |
completed | April 5, 2026, 2:48 a.m. |
Created at: March 27, 2026, 2:09 p.m.