Triple
T6681976
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | January term |
E152003
|
entity |
| Predicate | benefitForStudents |
P487
|
FINISHED |
| Object | flexibility in course planning |
—
|
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: flexibility in course planning | Statement: [January term, benefitForStudents, flexibility in course planning]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: benefitForStudents Context triple: [January term, benefitForStudents, flexibility in course planning]
-
A.
benefitsAre
Indicates that certain advantages, gains, or positive outcomes are possessed by or accrue to a particular entity or group.
-
B.
recitationBenefit
Indicates that one entity gains some advantage, improvement, or positive outcome as a result of a recitation performed by itself or another entity.
-
C.
benefits
chosen
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or action.
-
D.
educationalImpact
Indicates the effect or influence that one entity has on the learning, knowledge, or educational outcomes of another.
-
E.
educationUse
Indicates the use or application of something specifically for educational purposes or in an educational context.
- 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.