Triple
T1592390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokyo Station |
E34204
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object | Tōkyō-eki |
E34204
|
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: Tōkyō-eki | Statement: [Tokyo Station, hasAlternativeName, Tōkyō-eki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tōkyō-eki Context triple: [Tokyo Station, hasAlternativeName, Tōkyō-eki]
-
A.
Yokohama Station
Yokohama Station is one of Japan’s busiest railway hubs, serving numerous JR, private, and subway lines in central Yokohama.
-
B.
Nagoya Station
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.
-
C.
Tokyo Station
chosen
Tokyo Station is a major railway hub in central Tokyo, serving as a key terminal for Shinkansen bullet trains and numerous local and regional lines.
-
D.
Shinjuku Station
Shinjuku Station is one of the world’s busiest railway hubs, serving as a major commercial and transportation center in Tokyo, Japan.
-
E.
Suita Station
Suita Station is a railway station in Suita, Osaka Prefecture, Japan, serving passengers on the JR Kyoto Line (Tōkaidō Main Line).
- 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_69a885fdcb9c819081ce6f0b8cd477dd |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abb2c480008190bb472cfdab74c387 |
completed | March 7, 2026, 5:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd7f501e588190b666141f7e5ed6ae |
completed | March 20, 2026, 5:09 p.m. |
Created at: March 4, 2026, 7:27 p.m.