Triple
T3283768
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Catania |
E68931
|
entity |
| Predicate | hasFacultyOf |
P141
|
FINISHED |
| Object | Medicine |
—
|
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: Medicine | Statement: [University of Catania, hasFacultyOf, Medicine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFacultyOf Context triple: [University of Catania, hasFacultyOf, Medicine]
-
A.
hasFaculty
chosen
Indicates that an institution or department possesses or is associated with one or more faculty members.
-
B.
hasFacultyType
Indicates that a faculty member or academic unit is associated with a specific category or type of faculty (e.g., full-time, adjunct, visiting).
-
C.
isPublicUniversityFaculty
Indicates that a person holds a faculty position at a public (state- or government-funded) university.
-
D.
isLargestFacultyOf
Indicates that one faculty is the largest (typically by size, number of members, or resources) among all faculties within a given institution or context.
-
E.
publicUniversityFaculty
Indicates that a person is a member of the faculty (e.g., professor, lecturer, instructor) at a public university.
- 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_69ad859c463481909ca4be267336c290 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb03621608190a2b7a6b38d6559da |
completed | March 8, 2026, 5:21 p.m. |
| PD | Predicate disambiguation | batch_69ada421fadc8190b7c7d3c8afd20061 |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:10 p.m.