Triple
T1658534
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Tehran |
E35852
|
entity |
| Predicate | hasApproximateStudents |
P30984
|
FINISHED |
| Object | tens of thousands of students |
—
|
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: tens of thousands of students | Statement: [University of Tehran, hasApproximateStudents, tens of thousands of students]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateStudents Context triple: [University of Tehran, hasApproximateStudents, tens of thousands of students]
-
A.
hasStudents
Indicates that an entity (such as a class, school, or teacher) is associated with one or more students.
-
B.
hasStudentEnrollment
Indicates that a person or entity is enrolled as a student in a particular course, program, or educational institution.
-
C.
hasDayStudents
Indicates that an educational institution has students who attend during the day but do not reside on campus.
-
D.
acceptsStudents
Indicates that an institution or program allows and takes in students as participants or members.
-
E.
servesStudentPopulation
Indicates that an entity provides services, resources, or support to a defined group of students.
- 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_69a88606aa808190aa0b421b4271f220 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aaf3359ce48190803b322db8ad6027 |
completed | March 6, 2026, 3:31 p.m. |
| PD | Predicate disambiguation | batch_69a907cff53c8190b424f088478d3e2c |
completed | March 5, 2026, 4:34 a.m. |
| PDg | Predicate description generation | batch_69a99a4c3810819089d2dd0e23c8e46b |
completed | March 5, 2026, 2:59 p.m. |
Created at: March 4, 2026, 7:29 p.m.