Triple
T24135244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mir Yeshiva |
E598063
|
entity |
| Predicate | alumniInfluence |
P51
|
FINISHED |
| Object | many rabbis and educators worldwide |
—
|
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: many rabbis and educators worldwide | Statement: [Mir Yeshiva, alumniInfluence, many rabbis and educators worldwide]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: alumniInfluence Context triple: [Mir Yeshiva, alumniInfluence, many rabbis and educators worldwide]
-
A.
campusInfluence
Indicates the degree to which one entity affects, shapes, or contributes to the environment, activities, or dynamics within a campus setting.
-
B.
hasProfessionalAlumni
Indicates that an institution or organization has alumni who have gone on to work in a specified profession or professional field.
-
C.
alumnusRepresents
Indicates that an alumnus acts on behalf of, or serves as a representative of, a particular organization, group, or institution.
-
D.
hasAlumni
chosen
Indicates that an institution or organization is associated with individuals who formerly attended or graduated from it.
-
E.
alumniType
Indicates the specific category or classification of an alumnus/alumna in relation to an institution (e.g., graduate, former student, honorary).
- 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_69e288c92e448190ac57034fa0c863ce |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1df7c3ce08190bcbd9056a6630c2f |
completed | April 29, 2026, 10:37 a.m. |
| PD | Predicate disambiguation | batch_69f1765650fc8190a6bc1eb512b240bf |
completed | April 29, 2026, 3:09 a.m. |
Created at: April 17, 2026, 11:26 p.m.