Triple
T12353243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint Peter's University |
E294543
|
entity |
| Predicate | hasReligiousOrderType |
P3105
|
FINISHED |
| Object | Jesuit |
—
|
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: Jesuit | Statement: [Saint Peter's University, hasReligiousOrderType, Jesuit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReligiousOrderType Context triple: [Saint Peter's University, hasReligiousOrderType, Jesuit]
-
A.
religiousOrderSupported
Indicates that one entity provides support—such as resources, endorsement, or maintenance—to a particular religious order.
-
B.
hasClergyOrder
chosen
Indicates that an entity is associated with, or belongs to, a specific religious or clerical order.
-
C.
hasReligiousInstitutionType
Indicates that an entity is associated with, or classified by, a specific type of religious institution.
-
D.
hasClergyType
Indicates the specific category or role of clergy associated with an entity.
-
E.
approvedReligiousOrder
Indicates that an authority has formally recognized and authorized a particular religious order.
- 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_69d6ab6ccbec8190b09e2d357aa80064 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f8aa33c8190b22b7dff9559b8ed |
completed | April 10, 2026, 6:20 p.m. |
| PD | Predicate disambiguation | batch_69d93ecb5efc819086a3530282278bb1 |
completed | April 10, 2026, 6:17 p.m. |
Created at: April 8, 2026, 9:54 p.m.