Triple
T3166996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yokohama Museum of Art |
E66233
|
entity |
| Predicate | operator |
P179
|
FINISHED |
| Object | Yokohama City |
E10676
|
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: Yokohama City | Statement: [Yokohama Museum of Art, operator, Yokohama City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yokohama City Context triple: [Yokohama Museum of Art, operator, Yokohama City]
-
A.
Yokohama
chosen
Yokohama is Japan’s second-largest city and a major international port located just south of Tokyo.
-
B.
Nagoya
Nagoya is a major industrial and commercial city in central Japan, known as a manufacturing hub and the capital of Aichi Prefecture.
-
C.
Bunkyō City
Bunkyō City is a special ward in central Tokyo, Japan, known for its universities, historic temples, and quiet residential neighborhoods.
-
D.
Shibuya City
Shibuya City is a major commercial and entertainment district in central Tokyo, Japan, famous for its bustling scramble crossing, youth culture, and fashion scene.
-
E.
Kitakyushu
Kitakyushu is a major industrial and port city located in Fukuoka Prefecture on Japan’s Kyushu island.
- 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_69ad8585d7988190af37365331093ccd |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada643e3e481908f4526d66e36e150 |
completed | March 8, 2026, 4:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf32e44ed88190a7a6d9ea9097d8e6 |
completed | March 22, 2026, 12:08 a.m. |
Created at: March 8, 2026, 3:06 p.m.