Triple
T8770013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Turku |
E208432
|
entity |
| Predicate | hasStaffApprox |
P17907
|
FINISHED |
| Object | 3500 |
—
|
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: 3500 | Statement: [University of Turku, hasStaffApprox, 3500]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStaffApprox Context triple: [University of Turku, hasStaffApprox, 3500]
-
A.
hasMedicalStaffApprox
Indicates that an entity is associated with an approximate or estimated number of medical staff.
-
B.
hasSupportStaff
Indicates that an entity is associated with one or more staff members who provide assistance or support services to it.
-
C.
hasGeneralStaff
Indicates that an entity has, is associated with, or is served by a general staff body responsible for overall strategic or administrative functions.
-
D.
hasApproximateStudents
Indicates that an entity is associated with an estimated or approximate number of students, rather than an exact count.
-
E.
employsApproximateNumberOfPeople
chosen
Indicates that an entity employs a roughly estimated or approximate number of people, rather than an exact headcount.
- 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_69ca835edb4481909b4aafb616dc5eb7 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5eedc7188190a67d959b9af53837 |
completed | March 31, 2026, 11:55 p.m. |
| PD | Predicate disambiguation | batch_69cc5c1aff3881908be6a9cbc9f50461 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:41 p.m.