Triple
T3365377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Queen station |
E70821
|
entity |
| Predicate | hasPrestoVendingMachines |
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: [Queen station, hasPrestoVendingMachines, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPrestoVendingMachines Context triple: [Queen station, hasPrestoVendingMachines, 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.
hasVIPTerminal
Indicates that one entity possesses or provides access to a VIP (very important person) terminal associated with another entity.
-
D.
hasProduct
Indicates that an entity possesses, offers, or is associated with a particular product.
-
E.
hasRetailPresenceIn
Indicates that an entity conducts retail operations or maintains a retail outlet, store, or sales presence within a specified location.
- 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_69ad85a729d48190afd789cd8417f289 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb28643f48190b78b0222f8323344 |
completed | March 8, 2026, 5:31 p.m. |
| PD | Predicate disambiguation | batch_69ada4317e288190ab7d0f66e9dba65f |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:13 p.m.