Triple
T7784085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Takeo Miki |
E187196
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Miki Motoko
Miki Motoko was the wife of former Japanese Prime Minister Takeo Miki and served as a political spouse active in social and public life.
|
E698290
|
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: Miki Motoko | Statement: [Takeo Miki, spouse, Miki Motoko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miki Motoko Context triple: [Takeo Miki, spouse, Miki Motoko]
-
A.
Nijō Motoko
Nijō Motoko was a Japanese noblewoman of the Nijō family and the mother of Empress Teimei, consort of Emperor Taishō.
-
B.
Takako
Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
-
C.
Chiaki Mukai
Chiaki Mukai is a Japanese physician and astronaut who became the first Japanese woman to fly in space and a veteran of two NASA Space Shuttle missions.
-
D.
Maki Horikita
Maki Horikita is a Japanese actress known for her leading roles in popular television dramas and films during the 2000s and early 2010s.
-
E.
Yuriko
Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
- 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: Miki Motoko Triple: [Takeo Miki, spouse, Miki Motoko]
Generated description
Miki Motoko was the wife of former Japanese Prime Minister Takeo Miki and served as a political spouse active in social and public life.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Miki Motoko Target entity description: Miki Motoko was the wife of former Japanese Prime Minister Takeo Miki and served as a political spouse active in social and public life.
-
A.
Nijō Motoko
Nijō Motoko was a Japanese noblewoman of the Nijō family and the mother of Empress Teimei, consort of Emperor Taishō.
-
B.
Takako
Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
-
C.
Chiaki Mukai
Chiaki Mukai is a Japanese physician and astronaut who became the first Japanese woman to fly in space and a veteran of two NASA Space Shuttle missions.
-
D.
Maki Horikita
Maki Horikita is a Japanese actress known for her leading roles in popular television dramas and films during the 2000s and early 2010s.
-
E.
Yuriko
Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
- 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_69ca82af2d2c8190963861f5e0b8bf21 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cadf210f508190b215a0ab95192689 |
completed | March 30, 2026, 8:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb59e159a08190b0e16b7477f78051 |
completed | March 31, 2026, 5:21 a.m. |
| NEDg | Description generation | batch_69cb5f1afe0c8190916c7a9b2eab9270 |
completed | March 31, 2026, 5:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cb764973f88190964f91ee7e3fdc06 |
completed | March 31, 2026, 7:22 a.m. |
Created at: March 30, 2026, 4:22 p.m.