Triple
T5989800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kōkyo |
E133315
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Tokyo Station |
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: Tokyo Station | Statement: [Kōkyo, near, Tokyo Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tokyo Station Context triple: [Kōkyo, near, Tokyo Station]
-
A.
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.
-
B.
Yokohama Station
Yokohama Station is one of Japan’s busiest railway hubs, serving numerous JR, private, and subway lines in central Yokohama.
-
C.
Shibuya Station
Shibuya Station is one of Tokyo’s busiest and most important railway hubs, serving multiple train and subway lines and anchoring the famous Shibuya shopping and entertainment district.
-
D.
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.
-
E.
Shinjuku Station
Shinjuku Station is one of the world’s busiest railway hubs, serving as a major commercial and transportation center in Tokyo, Japan.
- 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_69c0087010d081908bb8142342d63330 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04dc76fd481908cc3f327e532a1a6 |
completed | March 22, 2026, 8:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c9493bf8088190bc59dd0e36d16a20 |
completed | March 29, 2026, 3:46 p.m. |
Created at: March 22, 2026, 4:05 p.m.