Triple
T3008926
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faculty of Law, Université de Montréal |
E81967
|
entity |
| Predicate | legalEducationSystem |
P340
|
FINISHED |
| Object | Canadian legal education system |
—
|
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: Canadian legal education system | Statement: [Faculty of Law, Université de Montréal, legalEducationSystem, Canadian legal education system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalEducationSystem Context triple: [Faculty of Law, Université de Montréal, legalEducationSystem, Canadian legal education system]
-
A.
educationSystem
chosen
Indicates the relationship in which an entity is part of, governed by, or operates within a particular system or structure of education.
-
B.
legalTraining
Indicates that one entity has provided or received education or instruction in law from another entity.
-
C.
lawSchoolName
Indicates the name of the law school with which an entity (such as a person or institution) is associated.
-
D.
studiedLawIn
Indicates that a person received legal education or training at a particular institution or location.
-
E.
educationRight
Indicates that an entity holds a right or entitlement to receive education or educational opportunities.
- 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_69ad8b1c4de88190a83b7cefaa1f2842 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a4ba6988190be29c00cd4266941 |
completed | March 8, 2026, 3:48 p.m. |
| PD | Predicate disambiguation | batch_69ad96180eb08190a524c5f458d41382 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 3 p.m.