Triple
T11415821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mirfield Free Grammar |
E270488
|
entity |
| Predicate | studentGenderIntake |
P12428
|
FINISHED |
| Object | mixed |
—
|
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: mixed | Statement: [Mirfield Free Grammar, studentGenderIntake, mixed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: studentGenderIntake Context triple: [Mirfield Free Grammar, studentGenderIntake, mixed]
-
A.
admissionGender
Indicates the gender-based criteria or classification applied in the context of admission or entry decisions.
-
B.
hasPupilsGender
Indicates that an entity has pupils whose gender is specified or characterized in some way.
-
C.
hasCoeducation
chosen
Indicates that an educational institution includes both male and female students together in its instructional programs.
-
D.
studentPopulationLevel
Indicates the relative size or magnitude of the student population associated with an entity.
-
E.
formerGenderAdmission
Indicates that an institution previously admitted a particular gender but no longer does so.
- 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_69d6aaddeaa8819088b30ef7b50598c9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d801ae47d0819098123505309c4a68 |
completed | April 9, 2026, 7:44 p.m. |
| PD | Predicate disambiguation | batch_69d7e70ffd708190b62a78ebcbce9f78 |
completed | April 9, 2026, 5:51 p.m. |
Created at: April 8, 2026, 9:34 p.m.