Triple
T22379905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Order of Prince Edward Island |
E553242
|
entity |
| Predicate | maximumAnnualAppointments |
P147430
|
FINISHED |
| Object | three |
—
|
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: three | Statement: [Order of Prince Edward Island, maximumAnnualAppointments, three]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumAnnualAppointments Context triple: [Order of Prince Edward Island, maximumAnnualAppointments, three]
-
A.
minimumSessionsPerYear
Indicates the smallest number of sessions that must occur within a one-year period.
-
B.
numberOfAnnualPatientVisits
Indicates the total count of patient visits that occur over the course of one year.
-
C.
annualVisitation
Indicates a recurring visit or attendance that takes place once every year between the related entities.
-
D.
firstAppointmentsNumber
Indicates the number of first-time appointments scheduled or recorded for an entity within a given context.
-
E.
firstAppointmentsYear
Indicates the calendar year in which the first appointments or initial assignments to a role, position, or service took place.
- 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_69e11e4c03248190a26a5060ea6973ee |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1582afe6c819093940f9d817c64a8 |
completed | April 29, 2026, 1 a.m. |
| PD | Predicate disambiguation | batch_69e73011e6388190a05edf137f488441 |
completed | April 21, 2026, 8:06 a.m. |
| PDg | Predicate description generation | batch_69e7342e9a0081909257210a81c96b29 |
completed | April 21, 2026, 8:24 a.m. |
Created at: April 16, 2026, 8:45 p.m.