Triple
T3768108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bou Inania Madrasa |
E82728
|
entity |
| Predicate | hasStudentCells |
P50996
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Bou Inania Madrasa, hasStudentCells, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStudentCells Context triple: [Bou Inania Madrasa, hasStudentCells, yes]
-
A.
hasStudents
Indicates that an entity (such as a class, school, or teacher) is associated with one or more students.
-
B.
hasStudentSection
Indicates that an entity (such as a course or class) is associated with a specific student section or subgroup of enrolled students.
-
C.
hasStudentEnrollment
Indicates that a person or entity is enrolled as a student in a particular course, program, or educational institution.
-
D.
hasDayStudents
Indicates that an educational institution has students who attend during the day but do not reside on campus.
-
E.
hasStudentCategory
Indicates that an entity is classified under a specific category or type of student.
- 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_69ad8b207b0081909d2b48843fbd8795 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcc2bdf6c819088d3c6ace83ca5ea |
completed | March 8, 2026, 7:21 p.m. |
| PD | Predicate disambiguation | batch_69adc04ec36c8190bd5b944d4f4d32aa |
completed | March 8, 2026, 6:30 p.m. |
| PDg | Predicate description generation | batch_69adc133ef50819094c2b971f31f1615 |
completed | March 8, 2026, 6:34 p.m. |
Created at: March 8, 2026, 3:35 p.m.