Triple
T13947649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | YCU |
E335431
|
entity |
| Predicate | operatedBy |
P86
|
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: [YCU, operatedBy, Yokohama City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yokohama City Context triple: [YCU, operatedBy, 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.
Kawasaki City
Kawasaki City is a major industrial and residential city in Kanagawa Prefecture, Japan, located between Tokyo and Yokohama along Tokyo Bay.
-
D.
Sagamihara
Sagamihara is a major city in Kanagawa Prefecture, Japan, known as a residential and industrial hub within the Greater Tokyo metropolitan area.
-
E.
Bunkyō City
Bunkyō City is a special ward in central Tokyo, Japan, known for its universities, historic temples, and quiet residential neighborhoods.
- 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_69d81c6081b88190b53e317c3370c8fe |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e12171c8190b95746bdd4845def |
completed | April 14, 2026, 12:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe967cea3c81909d9b600501897a41 |
completed | May 9, 2026, 2:05 a.m. |
Created at: April 9, 2026, 10:17 p.m.