Triple
T32794381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ferry laws on primary education |
E838716
|
entity |
| Predicate | typeOfSchoolTargeted |
P201552
|
FINISHED |
| Object | public primary 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: public primary schools | Statement: [Ferry laws on primary education, typeOfSchoolTargeted, public primary schools]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfSchoolTargeted Context triple: [Ferry laws on primary education, typeOfSchoolTargeted, public primary schools]
-
A.
schoolTypeAttended
Indicates the specific type or category of school that an entity has attended.
-
B.
educationType
Indicates the specific category or level of education associated with an entity, such as formal, informal, primary, secondary, or higher education.
-
C.
typeOfSchoolOperated
chosen
Indicates the specific category or kind of school that an entity is responsible for operating.
-
D.
parentSchoolType
Indicates the type or category of school to which a given school belongs as its parent institution.
-
E.
schoolOf
Indicates that an educational institution is the one where a person studied, worked, or is otherwise academically affiliated.
- 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_69f3493c7f6881908edf2aa13631d1e0 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a00776c4ebc8190899005fda34234d5 |
completed | May 10, 2026, 12:17 p.m. |
| PD | Predicate disambiguation | batch_6a0076f8a4c4819093ed577e67aa38f9 |
completed | May 10, 2026, 12:15 p.m. |
Created at: May 1, 2026, 1:14 a.m.