Triple
T34247050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faculty of Sciences (FAU) |
E878624
|
entity |
| Predicate | typicalCourseArea |
P205380
|
FINISHED |
| Object | calculus |
—
|
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: calculus | Statement: [Faculty of Sciences (FAU), typicalCourseArea, calculus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCourseArea Context triple: [Faculty of Sciences (FAU), typicalCourseArea, calculus]
-
A.
typicalCourse
Indicates that one entity is a standard or commonly taken course associated with another entity, such as a program, curriculum, or field of study.
-
B.
regionOfCourses
Indicates the geographical or administrative area in which the courses are offered or located.
-
C.
commonCourse
Indicates that two or more entities share at least one course in common.
-
D.
hasTypicalCourse
Indicates that there is a characteristic or commonly observed progression, sequence, or development pattern associated with the subject.
-
E.
courseType
Indicates the classification or category of a course based on its nature, level, or instructional format.
- 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_69f349b3618481909df955b063f305b2 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379fbe4a08190bfe65ebd141164e9 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c80ba448190853011097a151b7e |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 1:56 a.m.