Triple
T35951116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 42 Buenos Aires |
E1039727
|
entity |
| Predicate | teachingStaffModel |
P184469
|
FINISHED |
| Object | no traditional teachers |
—
|
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: no traditional teachers | Statement: [42 Buenos Aires, teachingStaffModel, no traditional teachers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teachingStaffModel Context triple: [42 Buenos Aires, teachingStaffModel, no traditional teachers]
-
A.
roleWithTeacher
Indicates a relationship where one entity holds a specific role or position in relation to a teacher.
-
B.
studentOrTeacherOf
Indicates that one entity is either a student of, or a teacher of, another entity within some educational or instructional context.
-
C.
hasTeachingRole
Indicates that one entity holds a position or responsibility involving teaching or instruction in relation to another entity.
-
D.
typeOfFaculty
Indicates the specific category or classification of faculty to which an academic staff member belongs (e.g., department, school, or faculty type).
-
E.
teacherInScene
Indicates that an entity is acting in the role of a teacher within a particular scene or context.
- 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_69f76e25ea488190b7cee970b3e70382 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7b35e32d481909ef0220e6f6ff4a8 |
completed | May 3, 2026, 8:43 p.m. |
| PD | Predicate disambiguation | batch_69f7b1bad2e88190963ab4ee5d4f2038 |
completed | May 3, 2026, 8:36 p.m. |
| PDg | Predicate description generation | batch_69f7b2c66054819083897e25edb65ba7 |
completed | May 3, 2026, 8:40 p.m. |
Created at: May 3, 2026, 4:07 p.m.