Triple
T1232588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belmont Middle School |
E26475
|
entity |
| Predicate | hasCoeducationStatus |
P12428
|
FINISHED |
| Object | coeducational |
—
|
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: coeducational | Statement: [Belmont Middle School, hasCoeducationStatus, coeducational]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCoeducationStatus Context triple: [Belmont Middle School, hasCoeducationStatus, coeducational]
-
A.
hasCoeducation
chosen
Indicates that an educational institution includes both male and female students together in its instructional programs.
-
B.
containsEducationalInstitution
Indicates that one entity geographically or administratively includes or encompasses an educational institution within its boundaries or structure.
-
C.
hasSchoolCategory
Indicates that an entity (such as a school or educational institution) is associated with a particular category or type of school.
-
D.
admissionGender
Indicates the gender-based criteria or classification applied in the context of admission or entry decisions.
-
E.
alsoAttendedSchool
Indicates that two or more entities attended the same school in addition to any other schools they may have attended.
- 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_69a4948571c88190a9191e451e6035fd |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4be5b40208190b115a6a344402caf |
completed | March 1, 2026, 10:31 p.m. |
| PD | Predicate disambiguation | batch_69a4bb65d61c8190bf0424ea0019a98b |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:47 p.m.