Triple
T7683859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | High Park subway station |
E174062
|
entity |
| Predicate | hasPrestoVendingMachine |
P42029
|
FINISHED |
| Object | yes |
—
|
LITERAL 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: yes | Statement: [High Park subway station, hasPrestoVendingMachine, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPrestoVendingMachine Context triple: [High Park subway station, hasPrestoVendingMachine, yes]
-
A.
hasPrestoMachines
chosen
Indicates that an entity possesses or is equipped with one or more Presto machines.
-
B.
hasPrestoCardSupport
Indicates that an entity supports or is compatible with the use of Presto cards for payment or access.
-
C.
hasPrestoFares
Indicates that an entity offers or supports fare payment using the Presto card system.
-
D.
hasVIPTerminal
Indicates that one entity possesses or provides access to a VIP (very important person) terminal associated with another entity.
-
E.
hasBeverageProgram
Indicates that an entity offers, manages, or participates in an organized beverage-related offering or initiative.
- F. None of above.
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_69c6995840408190a19de6c51090f46f |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7048b0b448190889bd40e0a38e51a |
completed | March 27, 2026, 10:28 p.m. |
| PD | Predicate disambiguation | batch_69c701618d3481908be84b76f36ac5a1 |
completed | March 27, 2026, 10:14 p.m. |
Created at: March 27, 2026, 4:01 p.m.