Triple
T928488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Order of Ontario |
E20038
|
entity |
| Predicate | maximumAppointmentsPerYear |
P21198
|
FINISHED |
| Object | 25 |
—
|
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: 25 | Statement: [Order of Ontario, maximumAppointmentsPerYear, 25]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumAppointmentsPerYear Context triple: [Order of Ontario, maximumAppointmentsPerYear, 25]
-
A.
canConfirmAppointments
Indicates that an entity has the ability or permission to confirm scheduled appointments.
-
B.
maximumNumberOfLaureatesPerYear
Indicates the highest allowable or observed count of laureates associated with a given year.
-
C.
typicalNumberOfMeetingsPerSeason
Indicates the usual or average count of meetings that occur within a single season.
-
D.
maximumService
Indicates that an entity provides the highest allowable or achievable level of service within a given context or system.
-
E.
hasMaximumNumberOfMembers
chosen
Indicates that there is an upper limit on how many members can be associated with a given 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_69a493af3dc48190adb7263e6e445ea1 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b34775ac8190aabbd047a36cec6b |
completed | March 1, 2026, 9:44 p.m. |
| PD | Predicate disambiguation | batch_69a4b29876348190a29f4ff9878074a5 |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.