Triple
T20310447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faculty of Health Sciences |
E510222
|
entity |
| Predicate | mayIncludeDepartment |
P139616
|
FINISHED |
| Object | nursing department |
—
|
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: nursing department | Statement: [Faculty of Health Sciences, mayIncludeDepartment, nursing department]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mayIncludeDepartment Context triple: [Faculty of Health Sciences, mayIncludeDepartment, nursing department]
-
A.
supportsDepartment
Indicates that one entity provides assistance, resources, or backing to a specific department.
-
B.
offeredByDepartment
Indicates that something, such as a course or program, is provided or made available by a specific department.
-
C.
usedByDepartment
Indicates that a resource, tool, or item is utilized or operated by a specific department.
-
D.
canHoldAppointmentsInMultipleDepartments
Indicates that an entity is allowed to have appointments or roles in more than one department at the same time.
-
E.
basedInDepartment
Indicates that an entity operates or has its primary affiliation within a specific department.
- 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_69e0b4c7491c8190961113c4283b10b0 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e677436af88190a046c44fa45b68b4 |
completed | April 20, 2026, 6:58 p.m. |
| PD | Predicate disambiguation | batch_69e55b21b09081909e46691b6f45a07f |
completed | April 19, 2026, 10:45 p.m. |
| PDg | Predicate description generation | batch_69e56702ad04819099c1c08f28d16809 |
completed | April 19, 2026, 11:36 p.m. |
Created at: April 16, 2026, 11:19 a.m.