Triple
T41806
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Toronto |
E826
|
entity |
| Predicate | studentsApprox |
P48
|
FINISHED |
| Object | 90000+ |
—
|
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: 90000+ | Statement: [University of Toronto, studentsApprox, 90000+]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: studentsApprox Context triple: [University of Toronto, studentsApprox, 90000+]
-
A.
hasStudents
chosen
Indicates that an entity (such as a class, school, or teacher) is associated with one or more students.
-
B.
hasNumberOfStudentAthletes
Indicates the relationship that specifies how many student athletes are associated with a given entity.
-
C.
campusSize
Indicates the physical extent or scale of a campus, typically measured in area or capacity.
-
D.
numberOfParticipants
Indicates the total count of entities involved in a particular event, activity, or relationship.
-
E.
passengersCountApproximate
Indicates that the number of passengers involved is given as an approximate or estimated count rather than an exact figure.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24db9527c8190816b6b25c88cb2f4 |
completed | Feb. 28, 2026, 2:06 a.m. |
| PD | Predicate disambiguation | batch_69a24ab8a8908190beec6da6694dd4c9 |
completed | Feb. 28, 2026, 1:54 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.