Triple
T9985170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UzK |
E196549
|
entity |
| Predicate | hasAcademicStaffApprox |
P26574
|
FINISHED |
| Object | 4000 |
—
|
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: 4000 | Statement: [UzK, hasAcademicStaffApprox, 4000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAcademicStaffApprox Context triple: [UzK, hasAcademicStaffApprox, 4000]
-
A.
hasAcademicStaff
Indicates that an institution or organization employs or is associated with one or more academic staff members.
-
B.
academicStaffCountApprox
chosen
Indicates an approximate number of academic staff associated with an institution or organizational unit.
-
C.
hasScientificStaff
Indicates that an entity employs or is associated with one or more individuals serving in scientific or research-related roles.
-
D.
hasFacultySizeApprox
Indicates that an institution has an approximate number of faculty members equal to the specified value.
-
E.
hasAcademicDepartment
Indicates that an institution or organization includes or is associated with a specific academic department.
- 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_69ca82efbce081908179b4b9c65096eb |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb8bf5adc81908c862b75053dd8f1 |
completed | April 2, 2026, 12:30 a.m. |
| PD | Predicate disambiguation | batch_69cd1da07db88190945bcdab3ca82e71 |
completed | April 1, 2026, 1:29 p.m. |
Created at: March 30, 2026, 8:49 p.m.