Triple
T4413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oregon State University |
E85
|
entity |
| Predicate | campusSetting |
P110
|
FINISHED |
| Object | college town |
—
|
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: college town | Statement: [Oregon State University, campusSetting, college town]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: campusSetting Context triple: [Oregon State University, campusSetting, college town]
-
A.
campus
Indicates that an entity is located on, associated with, or taking place within a particular campus.
-
B.
campusType
chosen
Indicates the classification or category of a campus based on its type (e.g., main, satellite, urban, rural).
-
C.
campusSize
Indicates the physical extent or scale of a campus, typically measured in area or capacity.
-
D.
hasAdditionalCampus
Indicates that an educational institution maintains one or more campuses in addition to its primary or main campus.
-
E.
hasMainCampus
Indicates that an educational institution is primarily based at or chiefly associated with a particular campus location.
- 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_69a238d6b47881909e68288aed2fd858 |
completed | Feb. 28, 2026, 12:37 a.m. |
| NER | Named-entity recognition | batch_69a23c24b3d08190a714126292fd5479 |
completed | Feb. 28, 2026, 12:51 a.m. |
| PD | Predicate disambiguation | batch_69a23998af288190855f0456740cbd51 |
completed | Feb. 28, 2026, 12:40 a.m. |
Created at: Feb. 28, 2026, 12:40 a.m.