Triple
T2638613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diocese of Milwaukee |
E62807
|
entity |
| Predicate | hasChildrenPrograms |
P42417
|
FINISHED |
| Object | children’s Christian education |
—
|
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: children’s Christian education | Statement: [Diocese of Milwaukee, hasChildrenPrograms, children’s Christian education]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasChildrenPrograms Context triple: [Diocese of Milwaukee, hasChildrenPrograms, children’s Christian education]
-
A.
hasParentProgram
Indicates that a given program is hierarchically contained within or derived from another, higher-level program that serves as its parent.
-
B.
has child
Indicates that one entity is the parent of another entity, which is its child.
-
C.
hasChildrenSection
Indicates that an entity includes or is associated with a dedicated section that contains information about its children.
-
D.
hasChildrenWith
Indicates that two entities share one or more biological or adopted children together.
-
E.
subsidiaryProgram
Indicates that one program operates under, and is organizationally subordinate to, another primary or parent 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_69ab4c3f2dcc819082df80f5e032f690 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abd8e470ac8190bd0d6de6805afcd0 |
completed | March 7, 2026, 7:51 a.m. |
| PD | Predicate disambiguation | batch_69abd812849881908f956845a80e0205 |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abd8abcc348190bfa05c0abc4bcee7 |
completed | March 7, 2026, 7:50 a.m. |
Created at: March 6, 2026, 9:53 p.m.