Triple
T6681962
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | January term |
E152003
|
entity |
| Predicate | courseLoad |
P71720
|
FINISHED |
| Object | one primary course |
—
|
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: one primary course | Statement: [January term, courseLoad, one primary course]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: courseLoad Context triple: [January term, courseLoad, one primary course]
-
A.
coursePar
Indicates that two entities (such as paths, lines, or trajectories) run alongside each other in the same general direction without intersecting.
-
B.
course
Indicates that an entity is an academic class or unit of instruction offered within an educational program.
-
C.
courseStructure
Indicates how a course is organized into its constituent parts, such as modules, units, lessons, and their sequencing or hierarchy.
-
D.
courseSetting
Indicates the context or environment in which a course is delivered or conducted.
-
E.
courseName
Indicates the specific name or title assigned to a course in an educational 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_69c687f9977c819097e7f5ada4fe522e |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6c0aa8c5c8190a302b261f11b70cb |
completed | March 27, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c6ad0b6d00819086205b8ce30dd045 |
completed | March 27, 2026, 4:15 p.m. |
| PDg | Predicate description generation | batch_69c6c0a90a088190978061cb05dbe268 |
completed | March 27, 2026, 5:38 p.m. |
Created at: March 27, 2026, 2:04 p.m.