Triple
T1145829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 皇室 |
E23562
|
entity |
| Predicate | country |
P26
|
FINISHED |
| Object | 日本 |
E174
|
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: 日本 | Statement: [皇室, country, 日本]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 日本 Context triple: [皇室, country, 日本]
-
A.
Japan
chosen
Japan is an East Asian island nation in the Pacific Ocean known for its advanced technology, rich cultural heritage, and major cities such as Tokyo, Osaka, and Kyoto.
-
B.
China
China is a vast East Asian country known for its long continuous civilization, large population, and major global economic and political influence.
-
C.
Honshu
Honshu is the largest and most populous island of Japan, home to major cities such as Tokyo, Osaka, and Kyoto.
-
D.
Fujinomiya, Japan
Fujinomiya, Japan is a city in Shizuoka Prefecture known as a gateway to Mount Fuji and for its scenic views, shrines, and local cuisine.
-
E.
Oppama, Japan
Oppama, Japan is an industrial coastal district in Yokosuka, Kanagawa Prefecture, best known for its major Nissan automobile manufacturing plant.
- 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_69a493ef399c8190b04b9146d2314f59 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bc51244c8190bcd533f3e80c8f17 |
completed | March 1, 2026, 10:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac5eb1f7d08190ba722dcbbc8a6799 |
completed | March 7, 2026, 5:21 p.m. |
Created at: March 1, 2026, 7:44 p.m.