Triple
T25393789
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Murrumbeena railway station |
E636235
|
entity |
| Predicate | hasMykiCardVendingMachines |
P165306
|
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: [Murrumbeena railway station, hasMykiCardVendingMachines, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMykiCardVendingMachines Context triple: [Murrumbeena railway station, hasMykiCardVendingMachines, yes]
-
A.
hasMykiCardVendingMachine
chosen
Indicates that a location or entity is equipped with a Myki card vending machine for purchasing or reloading Myki cards.
-
B.
hasMykiCardReaders
Indicates that an entity is equipped with or contains Myki card readers for processing Myki cards.
-
C.
hasSelfServiceTicketMachines
Indicates that an entity is equipped with self-service ticket machines available for use.
-
D.
hasOpalOrMykiGates
Indicates that a location or facility is equipped with Opal or Myki ticketing gates for access control or fare validation.
-
E.
hasOysterCardReaders
Indicates that the subject is equipped with or contains Oyster card readers for validating or processing Oyster card transactions.
- 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_69e75db263888190b77fff9e2827b9a2 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f65aa07c048190a5df30d53d8f0cf5 |
completed | May 2, 2026, 8:12 p.m. |
| PD | Predicate disambiguation | batch_69f659cc571c819097e51e531961d812 |
completed | May 2, 2026, 8:08 p.m. |
Created at: April 21, 2026, 1:49 p.m.