Triple
T22098515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Peace University |
E546103
|
entity |
| Predicate | studentFacultyRatioCharacteristic |
P25336
|
FINISHED |
| Object | low student-to-faculty ratio |
—
|
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: low student-to-faculty ratio | Statement: [William Peace University, studentFacultyRatioCharacteristic, low student-to-faculty ratio]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: studentFacultyRatioCharacteristic Context triple: [William Peace University, studentFacultyRatioCharacteristic, low student-to-faculty ratio]
-
A.
studentFacultyRatio
chosen
Indicates the numerical relationship between the number of students and the number of faculty members in an institution.
-
B.
universityCharacteristic
Indicates that a specified characteristic, quality, or attribute is associated with a particular university.
-
C.
hasFacultySizeApprox
Indicates that an institution has an approximate number of faculty members equal to the specified value.
-
D.
numberOfFaculties
Indicates the total count of faculties associated with a given entity.
-
E.
memberInstitutionCharacteristic
Indicates that a specific characteristic or attribute is associated with a member institution within a larger organization or system.
- 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_69e11e36d03c8190a83a1ba802b7231b |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129131b4c8190b443bc820d9b5c61 |
completed | April 28, 2026, 9:39 p.m. |
| PD | Predicate disambiguation | batch_69e71b20ec50819096ac196c798f8e3c |
completed | April 21, 2026, 6:37 a.m. |
Created at: April 16, 2026, 8:30 p.m.