Triple
T36937261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shi No Numa |
E913652
|
entity |
| Predicate | hasPerkMachine |
P197217
|
FINISHED |
| Object | Juggernog |
—
|
NE NERFINISHED |
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: Juggernog | Statement: [Shi No Numa, hasPerkMachine, Juggernog]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPerkMachine Context triple: [Shi No Numa, hasPerkMachine, Juggernog]
-
A.
hasSlotMachines
Indicates that an entity contains, offers, or is equipped with one or more slot machines.
-
B.
hasSelfServiceTicketMachines
Indicates that an entity is equipped with self-service ticket machines available for use.
-
C.
hasOpalTopUpMachine
Indicates that a location or facility is equipped with an Opal card top-up machine for adding credit or purchasing travel value.
-
D.
hasTicketOfficeOrMachines
Indicates that a place provides access to ticket purchasing facilities, either through a staffed ticket office, ticket machines, or both.
-
E.
hasMykiCardVendingMachine
Indicates that a location or entity is equipped with a Myki card vending machine for purchasing or reloading Myki cards.
- F. None of above. chosen
Provenance (4 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_69f76e8a6a5c81909c1febf32bf3fe23 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fe7bfc94bc81909eeec946e8c1c450 |
completed | May 9, 2026, 12:12 a.m. |
| PD | Predicate disambiguation | batch_69fe7b74a1188190886f128e07f712da |
completed | May 9, 2026, 12:10 a.m. |
| PDg | Predicate description generation | batch_69fe7bfb71b08190bed5c33e4ab7afff |
completed | May 9, 2026, 12:12 a.m. |
Created at: May 3, 2026, 4:13 p.m.