Triple
T1757560
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chūbu region |
E38582
|
entity |
| Predicate | railHub |
P523
|
FINISHED |
| Object | Nagoya Station |
E83103
|
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: Nagoya Station | Statement: [Chūbu region, railHub, Nagoya Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nagoya Station Context triple: [Chūbu region, railHub, Nagoya Station]
-
A.
Nagoya Station
chosen
Nagoya Station is one of Japan’s largest and busiest railway hubs, serving as a major Shinkansen and regional transit center in the city of Nagoya.
-
B.
Yokohama Station
Yokohama Station is one of Japan’s busiest railway hubs, serving numerous JR, private, and subway lines in central Yokohama.
-
C.
Osaka Station
Osaka Station is a major railway terminal and transportation hub in Osaka, Japan, serving numerous local and long-distance train lines and connecting key commercial districts.
-
D.
Suita Station
Suita Station is a railway station in Suita, Osaka Prefecture, Japan, serving passengers on the JR Kyoto Line (Tōkaidō Main Line).
-
E.
Kyoto Station
Kyoto Station is a major railway and transportation hub in Kyoto, Japan, known for its vast, modern architectural complex that integrates trains, buses, shopping, and cultural facilities.
- 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_69a8862bdb2081908aefe831c8aa8017 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa643ce88481909d2feef3c5fd849f |
completed | March 6, 2026, 5:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bde0234b6481908dcf37da32cf856b |
completed | March 21, 2026, 12:02 a.m. |
Created at: March 4, 2026, 7:31 p.m.