Triple
T229727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faculty of Nursing, Cairo University |
E4383
|
entity |
| Predicate | regulatesProfession |
P9508
|
FINISHED |
| Object | nursing education standards |
—
|
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 education standards | Statement: [Faculty of Nursing, Cairo University, regulatesProfession, nursing education standards]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regulatesProfession Context triple: [Faculty of Nursing, Cairo University, regulatesProfession, nursing education standards]
-
A.
legalProfessionRole
Indicates that one entity holds or performs a specific professional role within the legal domain in relation to another entity or context.
-
B.
portraysProfession
Indicates that one entity depicts or represents another entity in a specific profession or occupational role.
-
C.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
D.
roleInvolves
Indicates that a particular role includes or requires participation in a specified activity, responsibility, or function.
-
E.
describesCareerOf
Indicates that one entity provides a description or characterization of the professional career of another entity.
- 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_69a257363ffc81909757bde7ab3404da |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25e0868708190ad551ca06cc57f4a |
completed | Feb. 28, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69a25b5a075081909b0e9b88c1492d5a |
completed | Feb. 28, 2026, 3:04 a.m. |
| PDg | Predicate description generation | batch_69a25e06dd7c8190a8cbb76cee3c6e4b |
completed | Feb. 28, 2026, 3:16 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.