Triple
T980828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dordt University |
E21162
|
entity |
| Predicate | hasReligiousPerspective |
P6264
|
FINISHED |
| Object | Reformed Christian worldview |
—
|
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: Reformed Christian worldview | Statement: [Dordt University, hasReligiousPerspective, Reformed Christian worldview]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReligiousPerspective Context triple: [Dordt University, hasReligiousPerspective, Reformed Christian worldview]
-
A.
hadViewOnReligion
chosen
Indicates that an entity held a particular perspective, belief, or stance regarding religion.
-
B.
religiousCulturalContext
Indicates the religious or cultural setting, tradition, or framework within which an entity, practice, or event occurs or is interpreted.
-
C.
recognizesReligion
Indicates that one entity formally acknowledges or accepts another entity as a valid or legitimate religion.
-
D.
influencedReligion
Indicates that one entity has had a shaping or modifying effect on the religious beliefs, practices, or traditions of another entity.
-
E.
religiousElement
Indicates that something is a component, aspect, or feature associated with a religion or religious practice.
- 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_69a493c2b62c8190b616351789ec47f8 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b47b58ec81908d95f151b9af3dae |
completed | March 1, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69a4b2aa219081908a6b0ef786b4aa52 |
completed | March 1, 2026, 9:42 p.m. |
Created at: March 1, 2026, 7:40 p.m.