Triple
T366487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faculty of Mathematics and Computer Science, University of Havana |
E7970
|
entity |
| Predicate | hasTypeOfStaff |
P2464
|
FINISHED |
| Object | academic staff |
—
|
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: academic staff | Statement: [Faculty of Mathematics and Computer Science, University of Havana, hasTypeOfStaff, academic staff]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfStaff Context triple: [Faculty of Mathematics and Computer Science, University of Havana, hasTypeOfStaff, academic staff]
-
A.
hasAffiliationType
Indicates that one entity is connected to another through a specified kind or category of affiliation or association.
-
B.
hasTypeOfOrganization
Indicates that an entity is classified as belonging to a particular type or category of organization.
-
C.
hasFacultyType
chosen
Indicates that a faculty member or academic unit is associated with a specific category or type of faculty (e.g., full-time, adjunct, visiting).
-
D.
typeOfRole
Indicates that one entity specifies the kind or category of role that another entity holds or performs.
-
E.
hasOfficeType
Indicates that an entity’s office is classified as a specific type or category of office.
- 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_69a2e7e880008190a6ad7e06e5d03007 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ebe7d4d0819083daeb7686ae1914 |
completed | Feb. 28, 2026, 1:21 p.m. |
| PD | Predicate disambiguation | batch_69a2e95dbb208190b277fc5352a4ee84 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.