Triple
T20253230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UK Bratislava |
E498612
|
entity |
| Predicate | hasApproximateStudentNumber |
P30984
|
FINISHED |
| Object | 20000–30000 |
—
|
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: 20000–30000 | Statement: [UK Bratislava, hasApproximateStudentNumber, 20000–30000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateStudentNumber Context triple: [UK Bratislava, hasApproximateStudentNumber, 20000–30000]
-
A.
hasApproximateStudents
chosen
Indicates that an entity is associated with an estimated or approximate number of students, rather than an exact count.
-
B.
hasFacultySizeApprox
Indicates that an institution has an approximate number of faculty members equal to the specified value.
-
C.
hasApproximateNumberOfPeople
Indicates that an entity is associated with an estimated or approximate count of people, rather than an exact number.
-
D.
hasStudents
Indicates that an entity (such as a class, school, or teacher) is associated with one or more students.
-
E.
typicalEnrollment
Indicates the usual or standard number of participants or members enrolled in something, such as a course, program, or institution.
- 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_69da6274c58c81909c646eabed6f4f30 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e673a986e08190b1ff2992ed5f8772 |
completed | April 20, 2026, 6:42 p.m. |
| PD | Predicate disambiguation | batch_69e55b1b23f88190bdcbe2f81dd226dd |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 11, 2026, 11:41 p.m.