Triple
T21436506
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santa Catalina School |
E528825
|
entity |
| Predicate | hasReligiousOrderInfluence |
P53437
|
FINISHED |
| Object | Dominican tradition |
—
|
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: Dominican tradition | Statement: [Santa Catalina School, hasReligiousOrderInfluence, Dominican tradition]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReligiousOrderInfluence Context triple: [Santa Catalina School, hasReligiousOrderInfluence, Dominican tradition]
-
A.
religiousOrderInfluence
chosen
Indicates that one religious order exerts influence, guidance, or control over another entity, such as a person, group, or institution.
-
B.
hasEcclesiasticalInfluenceIn
Indicates that an entity holds religious or church-related authority, impact, or sway within a specified region, institution, or context.
-
C.
hasClergyOrder
Indicates that an entity is associated with, or belongs to, a specific religious or clerical order.
-
D.
hasReligiousInstitutionType
Indicates that an entity is associated with, or classified by, a specific type of religious institution.
-
E.
hasReligiousType
Indicates that an entity is associated with or classified under a particular religion or religious category.
- 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_69e0c4569fa081908101baa24f8745db |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b537f39081909220577618657805 |
completed | April 22, 2026, 11:47 a.m. |
| PD | Predicate disambiguation | batch_69e61639ee288190889ffd500d1260f6 |
completed | April 20, 2026, 12:04 p.m. |
Created at: April 16, 2026, 6:03 p.m.