Triple
T13618830
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deep Springs College |
E325394
|
entity |
| Predicate | typicalEnrollment |
P110448
|
FINISHED |
| Object | approximately 25 to 30 students |
—
|
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: approximately 25 to 30 students | Statement: [Deep Springs College, typicalEnrollment, approximately 25 to 30 students]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalEnrollment Context triple: [Deep Springs College, typicalEnrollment, approximately 25 to 30 students]
-
A.
typicalClassSize
Indicates the usual or average number of students (or participants) that are present in a single class or course section.
-
B.
typicalStudentStatus
Indicates the usual or standard enrollment or academic standing that a student typically holds within an educational context.
-
C.
enrollmentType
Indicates the specific category or mode under which an entity is enrolled in a program, service, or system.
-
D.
typicalDegree
Indicates the usual or characteristic level, intensity, or extent to which something holds or applies in a given context.
-
E.
undergraduateEnrollment
Indicates the number of undergraduate students enrolled in an institution or program.
- 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_69d8076aae28819092cf636190ee5529 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbbb9ee3f081909056dc1a92c40b7a |
completed | April 12, 2026, 3:34 p.m. |
| PD | Predicate disambiguation | batch_69dbae1b3ee481909bd43ded6227a3e5 |
completed | April 12, 2026, 2:37 p.m. |
| PDg | Predicate description generation | batch_69dbbb8c77dc8190b7bd803b5e168d23 |
completed | April 12, 2026, 3:34 p.m. |
Created at: April 9, 2026, 9:50 p.m.