Triple
T3372558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Evergreen State College |
E70987
|
entity |
| Predicate | gradingSystem |
P48042
|
FINISHED |
| Object | primarily narrative evaluations instead of letter grades |
—
|
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: primarily narrative evaluations instead of letter grades | Statement: [Evergreen State College, gradingSystem, primarily narrative evaluations instead of letter grades]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: gradingSystem Context triple: [Evergreen State College, gradingSystem, primarily narrative evaluations instead of letter grades]
-
A.
gradeNumber
Indicates the numerical grade or level assigned to an entity within an ordered grading or classification system.
-
B.
gradeRank
Indicates the relative academic standing or position of an entity within a graded or ranked group based on performance or scores.
-
C.
gradeStructure
Indicates the hierarchical organization or breakdown of grades or scoring components within an evaluation system.
-
D.
grades
Indicates that one entity evaluates and assigns a score or level of performance to another entity.
-
E.
gradeCount
Indicates the number of grades associated with a given entity or 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_69ad85a729d48190afd789cd8417f289 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb2bdcf70819087fc7e00fbd61e0d |
completed | March 8, 2026, 5:32 p.m. |
| PD | Predicate disambiguation | batch_69ada433059881908e46f38cc5f40a32 |
completed | March 8, 2026, 4:30 p.m. |
| PDg | Predicate description generation | batch_69adaa518ac88190b64f949ace018ab7 |
completed | March 8, 2026, 4:56 p.m. |
Created at: March 8, 2026, 3:13 p.m.