Triple
T15120937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mako |
E361169
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Komuro |
E364702
|
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: Komuro | Statement: [Mako, familyName, Komuro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Komuro Context triple: [Mako, familyName, Komuro]
-
A.
Komuro
chosen
Komuro is the married surname of Japan’s former Princess Mako, adopted after her marriage to commoner Kei Komuro.
-
B.
Koromo
Koromo was the former name of what is now Toyota City in Aichi Prefecture, Japan, historically known as a regional center before becoming synonymous with the Toyota automobile company.
-
C.
Takamikura
Takamikura is the ornate imperial throne used in Kyoto for the enthronement ceremonies of Japanese emperors.
-
D.
Mitoyo
Mitoyo is a coastal city in western Kagawa Prefecture on Japan’s Shikoku Island, known for its scenic Seto Inland Sea views and rural landscapes.
-
E.
Shimabukuro
Shimabukuro is a Japanese surname most notably associated with virtuoso ukulele player Jake Shimabukuro.
- 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_69d85a06450081909c5a14ea9851a15e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0059f69a881909929a037a0eef702 |
completed | April 15, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feb7f2e3408190ae095d16396e420d |
completed | May 9, 2026, 4:28 a.m. |
Created at: April 10, 2026, 3:06 a.m.