Triple
T5107529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yuriko Kikuchi |
E115133
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Shōta Sometani |
E586905
|
NE FINISHED |
How this triple was built (2 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: Shōta Sometani | Statement: [Yuriko Kikuchi, spouse, Shōta Sometani]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shōta Sometani Context triple: [Yuriko Kikuchi, spouse, Shōta Sometani]
-
A.
Shōta Sometani
chosen
Shōta Sometani is a Japanese actor known for his versatile performances in films and television dramas.
-
B.
Tomonori Kanemoto
Tomonori Kanemoto is a former Japanese professional baseball outfielder and manager, best known as an ironman of Nippon Professional Baseball for his consecutive games and innings played streaks.
-
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.
Kosuke Imai
Kosuke Imai is a political scientist and statistician known for his contributions to causal inference, experimental design, and statistical methods in the social sciences.
-
E.
Yoshitsugu Saito
Yoshitsugu Saitō was an Imperial Japanese Army lieutenant general best known for leading Japan’s doomed defense of Saipan during World War II and dying in the battle.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69bd4440b3348190be1251fd8b7951f1 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd75a8ee7881908876859402911e5a |
completed | March 20, 2026, 4:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62cd028808190a61ac9c12042611f |
completed | March 27, 2026, 7:08 a.m. |
Created at: March 20, 2026, 1:41 p.m.