Triple
T38539131
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Finchley Catholic High School |
E924779
|
entity |
| Predicate | isCoeducationalFromAge |
P190880
|
FINISHED |
| Object | 16 |
—
|
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: 16 | Statement: [Finchley Catholic High School, isCoeducationalFromAge, 16]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isCoeducationalFromAge Context triple: [Finchley Catholic High School, isCoeducationalFromAge, 16]
-
A.
hasCoeducation
Indicates that an educational institution includes both male and female students together in its instructional programs.
-
B.
isNonCoeducational
Indicates that an educational institution serves only one gender and does not provide coeducational (mixed-gender) instruction.
-
C.
isSchoolFor
Indicates that one entity functions as an educational institution intended to serve, teach, or train the other entity.
-
D.
isSecondaryEducationInstitution
Indicates that an entity functions as an institution providing secondary-level education (typically between primary and higher/tertiary education).
-
E.
attendsSchoolIn
Indicates that a person is enrolled as a student at, and regularly goes to, a school located in a particular place.
- F. None of above. chosen
Provenance (4 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_69f76eadeac081909cdfdd0474cb6765 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcd34596288190b8e34a7a20b4c0db |
completed | May 7, 2026, 6 p.m. |
| PD | Predicate disambiguation | batch_69fcd1f81cbc8190b4fd3bfc3106c1f3 |
completed | May 7, 2026, 5:55 p.m. |
| PDg | Predicate description generation | batch_69fcd2e2f00081908b8c820fc073a63c |
completed | May 7, 2026, 5:58 p.m. |
Created at: May 3, 2026, 4:32 p.m.