Triple
T2684601
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Osgoode station |
E57453
|
entity |
| Predicate | fareSystem |
P395
|
FINISHED |
| Object | Presto card |
E31472
|
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: Presto card | Statement: [Osgoode station, fareSystem, Presto card]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Presto card Context triple: [Osgoode station, fareSystem, Presto card]
-
A.
Presto card
chosen
The Presto card is a reloadable smart card used for paying public transit fares across the Greater Toronto and Hamilton Area and other regions in Ontario, Canada.
-
B.
Clipper card
The Clipper card is a reloadable contactless smart card used to pay fares across multiple public transit systems in the San Francisco Bay Area.
-
C.
Breeze Card
The Breeze Card is a reusable smart fare card used for paying transit fares across the Metropolitan Atlanta Rapid Transit Authority (MARTA) system in Atlanta, Georgia.
-
D.
TAP card
The TAP card is a reusable contactless smart card used to pay fares across public transit systems in the Los Angeles County region.
-
E.
Bilhete Único smart card
The Bilhete Único smart card is São Paulo’s integrated public transport fare card, allowing seamless, discounted transfers across the city’s metro, bus, and train networks.
- 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_69ab4a5028388190a36f3baf1588309e |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd9edba5c8190b86d6cba0f1964e2 |
completed | March 7, 2026, 7:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afa0700d548190944544495a2d42f6 |
completed | March 10, 2026, 4:39 a.m. |
Created at: March 6, 2026, 9:54 p.m.