Triple
T8396166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santa Rosa County School District |
E198055
|
entity |
| Predicate | schoolTypeOperated |
P58636
|
FINISHED |
| Object | traditional public schools |
—
|
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: traditional public schools | Statement: [Santa Rosa County School District, schoolTypeOperated, traditional public schools]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: schoolTypeOperated Context triple: [Santa Rosa County School District, schoolTypeOperated, traditional public schools]
-
A.
schoolSystemType
Indicates the classification or organizational model of a school system (e.g., public, private, charter, or other structural type).
-
B.
schoolSector
Indicates the educational sector or category (such as public, private, or charter) to which a school belongs.
-
C.
typeOfSchoolDistrict
chosen
Indicates the classification or category of a school district, specifying what kind of school district it is.
-
D.
schoolTypeAttended
Indicates the specific type or category of school that an entity has attended.
-
E.
operatesInEducationalSector
Indicates that an entity conducts its primary activities or provides services within the field of education or the educational industry.
- 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_69ca82f816bc8190ab321c07d72208c1 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb818893348190a6ea2ff6a2e3e491 |
completed | March 31, 2026, 8:10 a.m. |
| PD | Predicate disambiguation | batch_69cb70d24b248190a326aa6804f942b5 |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:04 p.m.