Triple
T5013035
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jerónimo de Loayza |
E112670
|
entity |
| Predicate | typeOfCleric |
P3092
|
FINISHED |
| Object | Roman Catholic prelate |
—
|
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: Roman Catholic prelate | Statement: [Jerónimo de Loayza, typeOfCleric, Roman Catholic prelate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfCleric Context triple: [Jerónimo de Loayza, typeOfCleric, Roman Catholic prelate]
-
A.
typeOfSaint
Indicates that one entity is classified as a specific kind or category of saint in relation to another entity.
-
B.
hasClericalDiscipline
Indicates that an entity is subject to, or governed by, a particular set of clerical or religious disciplinary rules or practices.
-
C.
hasClergyType
chosen
Indicates the specific category or role of clergy associated with an entity.
-
D.
notableClericPresent
Indicates that a distinguished or prominent cleric is present at a given event, location, or context.
-
E.
wasDiscipleOf
Indicates that one entity served as a disciple, student, or follower under the guidance or teaching of another entity.
- 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_69bd4434acb8819086679dbeccc2fe54 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd730f12a481908a27c15dc73987c6 |
completed | March 20, 2026, 4:17 p.m. |
| PD | Predicate disambiguation | batch_69bd714cbc448190aa53a8a83d768b64 |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:35 p.m.