Triple
T7805032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sancte et Sapienter |
E180526
|
entity |
| Predicate | hasTraditionalTranslation |
P79114
|
FINISHED |
| Object | With Holiness and Wisdom |
—
|
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: With Holiness and Wisdom | Statement: [Sancte et Sapienter, hasTraditionalTranslation, With Holiness and Wisdom]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTraditionalTranslation Context triple: [Sancte et Sapienter, hasTraditionalTranslation, With Holiness and Wisdom]
-
A.
hasTranslation
Indicates that one entity is a translation or translated version of another entity in a different language.
-
B.
hasTraditionalName
Indicates that an entity is associated with a name traditionally used or recognized for it, often rooted in long-standing cultural or historical practice.
-
C.
hasTraditionalInterpretation
Indicates that something is associated with or understood according to a long-established or customary interpretation.
-
D.
hasTraditionalDialect
Indicates that an entity possesses or is associated with a traditional form or variety of a language or dialect.
-
E.
hasTranslated
Indicates that one entity has rendered the content of another entity from one language into a different language.
- 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_69ca827e50cc8190a92a733577184938 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf78a6d88819093f83528fe88b182 |
completed | March 30, 2026, 10:22 p.m. |
| PD | Predicate disambiguation | batch_69cae9111b2481909684a2d4aa4831c2 |
completed | March 30, 2026, 9:20 p.m. |
| PDg | Predicate description generation | batch_69caf7855a3c81908b9318f7186fc0c0 |
completed | March 30, 2026, 10:21 p.m. |
Created at: March 30, 2026, 4:35 p.m.