Triple
T1153291
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Arizona |
E23725
|
entity |
| Predicate | numberOfStudents |
P21454
|
FINISHED |
| Object | over 45000 |
—
|
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: over 45000 | Statement: [University of Arizona, numberOfStudents, over 45000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfStudents Context triple: [University of Arizona, numberOfStudents, over 45000]
-
A.
hasStudents
Indicates that an entity (such as a class, school, or teacher) is associated with one or more students.
-
B.
studentPopulationLevel
chosen
Indicates the relative size or magnitude of the student population associated with an entity.
-
C.
hasNumberOfStudentAthletes
Indicates the relationship that specifies how many student athletes are associated with a given entity.
-
D.
servesStudentPopulation
Indicates that an entity provides services, resources, or support to a defined group of students.
-
E.
hasDayStudents
Indicates that an educational institution has students who attend during the day but do not reside on campus.
- 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_69a493f0d32c8190ac74bad3c87f2641 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bc8e9cb481908a528a828b21d497 |
completed | March 1, 2026, 10:24 p.m. |
| PD | Predicate disambiguation | batch_69a4bb50d19c81908a98dbbb04a8906f |
completed | March 1, 2026, 10:18 p.m. |
Created at: March 1, 2026, 7:44 p.m.