Triple
T16618232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fawkner railway station |
E403749
|
entity |
| Predicate | hasMykiVendingMachine |
P49831
|
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: [Fawkner railway station, hasMykiVendingMachine, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMykiVendingMachine Context triple: [Fawkner railway station, hasMykiVendingMachine, yes]
-
A.
hasSelfServiceTicketMachines
chosen
Indicates that an entity is equipped with self-service ticket machines available for use.
-
B.
hasOpalTopUpMachine
Indicates that a location or facility is equipped with an Opal card top-up machine for adding credit or purchasing travel value.
-
C.
hasVIPTerminal
Indicates that one entity possesses or provides access to a VIP (very important person) terminal associated with another entity.
-
D.
hasNearbyQuarter
Indicates that one entity is located within a short distance of a specific quarter or district associated with another entity.
-
E.
hasRetailKiosks
Indicates that one entity operates or maintains retail kiosks associated with or located within another entity.
- 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_69d883897eb481909eaaa088ba9918d9 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3754bc4cc8190a586732fc6507b40 |
completed | April 18, 2026, 12:12 p.m. |
| PD | Predicate disambiguation | batch_69e296aabc508190b3836a91b49113ad |
completed | April 17, 2026, 8:23 p.m. |
Created at: April 10, 2026, 5:17 a.m.