Triple
T6681975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | January term |
E152003
|
entity |
| Predicate | hasSchedulingPattern |
P63521
|
FINISHED |
| Object | meets more frequently than regular semester courses |
—
|
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: meets more frequently than regular semester courses | Statement: [January term, hasSchedulingPattern, meets more frequently than regular semester courses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSchedulingPattern Context triple: [January term, hasSchedulingPattern, meets more frequently than regular semester courses]
-
A.
hasPlanningPattern
Indicates that an entity follows or is associated with a particular planning pattern or structured approach to planning.
-
B.
hasScheduleType
chosen
Indicates that an entity is associated with a particular kind or category of schedule (e.g., recurring, one-time, or specific scheduling pattern).
-
C.
containsSchedule
Indicates that one entity includes or holds a schedule associated with another entity or activity.
-
D.
schedulingRule
Indicates a rule or constraint that governs when, how, or under what conditions an event or task may be scheduled.
-
E.
hasWorkSchedule
Indicates that an entity is assigned or follows a specific work schedule, defining when they are expected to work.
- F. None of above.
Provenance (3 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. |
Created at: March 27, 2026, 2:04 p.m.