Triple
T7448247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Senkawa Station |
E171937
|
entity |
| Predicate | fareCardAccepted |
P9955
|
FINISHED |
| Object | Suica |
E131332
|
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: Suica | Statement: [Senkawa Station, fareCardAccepted, Suica]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suica Context triple: [Senkawa Station, fareCardAccepted, Suica]
-
A.
Suica
chosen
Suica is a rechargeable contactless smart card issued by JR East that is widely used for train fares and electronic payments across Japan.
-
B.
Kitaca
Kitaca is a rechargeable contactless smart card used primarily for public transportation and electronic payments in Japan’s Hokkaido region.
-
C.
PASMO
PASMO is a rechargeable contactless smart card widely used for public transportation and electronic payments across the Tokyo metropolitan area.
-
D.
ICOCA
ICOCA is a rechargeable contactless smart card used for fare payment on public transportation systems in the Kansai region of Japan.
-
E.
Myki
Myki is Melbourne’s contactless smartcard public transport ticketing system used across trains, trams, and buses in Victoria, Australia.
- 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_69c68a65402881908f7869368eb746fb |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f38808fc8190b27a2b455155cb5b |
completed | March 27, 2026, 9:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c83451fcfc8190b32840c3c3448962 |
completed | March 28, 2026, 8:04 p.m. |
Created at: March 27, 2026, 3:14 p.m.