Triple
T25976747
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bishopton Primary School |
E645961
|
entity |
| Predicate | hasPupilGenderAdmission |
P16158
|
FINISHED |
| Object | co-educational |
—
|
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: co-educational | Statement: [Bishopton Primary School, hasPupilGenderAdmission, co-educational]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPupilGenderAdmission Context triple: [Bishopton Primary School, hasPupilGenderAdmission, co-educational]
-
A.
hasPupilsGender
Indicates that an entity has pupils whose gender is specified or characterized in some way.
-
B.
admissionGender
chosen
Indicates the gender-based criteria or classification applied in the context of admission or entry decisions.
-
C.
formerGenderAdmission
Indicates that an institution previously admitted a particular gender but no longer does so.
-
D.
hasCoeducation
Indicates that an educational institution includes both male and female students together in its instructional programs.
-
E.
genderOfChild
Indicates the gender or sex assigned to a specified child in the relationship.
- 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_69e77e8768648190b27bb578f14bcb88 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f650c70d7c819093d9a0f005f7c8d5 |
completed | May 2, 2026, 7:30 p.m. |
| PD | Predicate disambiguation | batch_69f64cab1f648190a2a9460690d18a37 |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 22, 2026, 8:52 a.m.