Triple
T32411065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kenrick–Glennon Seminary |
E828219
|
entity |
| Predicate | formationModel |
P178644
|
FINISHED |
| Object | Program of Priestly Formation (United States) |
—
|
NE NERFINISHED |
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: Program of Priestly Formation (United States) | Statement: [Kenrick–Glennon Seminary, formationModel, Program of Priestly Formation (United States)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: formationModel Context triple: [Kenrick–Glennon Seminary, formationModel, Program of Priestly Formation (United States)]
-
A.
formationComponents
Indicates that one entity is composed of, or structurally includes, the other entities as its constituent parts or components.
-
B.
formationType
Indicates the specific structural or organizational configuration in which something is arranged, created, or formed.
-
C.
animationModel
Indicates that one entity serves as the animation model or reference rig used to drive or define the animated behavior of another entity.
-
D.
designModel
Indicates that one entity creates, specifies, or defines the structure or behavior of another entity as a model or blueprint.
-
E.
model
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
- 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_69f34919f300819092b541c6277cd68a |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f7117e55908190a67105e92bc4830f |
completed | May 3, 2026, 9:12 a.m. |
| PD | Predicate disambiguation | batch_69f70f380690819090cc34763ba460ed |
completed | May 3, 2026, 9:02 a.m. |
| PDg | Predicate description generation | batch_69f7117cf2188190b29e36fc1e342c60 |
completed | May 3, 2026, 9:12 a.m. |
Created at: May 1, 2026, 12:53 a.m.