Triple
T4926832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint-Laurent |
E110596
|
entity |
| Predicate | hasSchools |
P113
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Saint-Laurent, hasSchools, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSchools Context triple: [Saint-Laurent, hasSchools, true]
-
A.
hasSchool
chosen
Indicates that an entity possesses, is associated with, or is served by a particular school.
-
B.
hasSchoolsAccess
Indicates that one entity has permission or the ability to access schools or school-related resources associated with another entity.
-
C.
hasSchoolCategory
Indicates that an entity (such as a school or educational institution) is associated with a particular category or type of school.
-
D.
schoolAttended
Indicates that one entity has attended, or been enrolled as a student at, the school represented by the other entity.
-
E.
hasSchoolYears
Indicates a relationship where an educational institution is associated with specific academic years or grade levels it offers or covers.
- 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_69bd4415190c8190817bee7ec9f9f944 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd70354bd081909291a43439f42ed3 |
completed | March 20, 2026, 4:05 p.m. |
| PD | Predicate disambiguation | batch_69bd6c3695c8819094e7ad2f6d4ba1ac |
completed | March 20, 2026, 3:48 p.m. |
Created at: March 20, 2026, 1:30 p.m.