Triple
T7718133
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ippolito Aldobrandini |
E174937
|
entity |
| Predicate | canonLawTraining |
P78325
|
FINISHED |
| Object | University-educated in law |
—
|
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: University-educated in law | Statement: [Ippolito Aldobrandini, canonLawTraining, University-educated in law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canonLawTraining Context triple: [Ippolito Aldobrandini, canonLawTraining, University-educated in law]
-
A.
canonLawSubject
Indicates that an entity is the subject or topic governed, regulated, or addressed by a particular canon law or set of canonical legal norms.
-
B.
canonLawContext
Indicates that something occurs within, is governed by, or is interpreted according to the norms and framework of canon law.
-
C.
canonLawAction
Indicates an action, decision, or procedure carried out under or in accordance with canon law.
-
D.
religiousCanon
Indicates that something is formally recognized as part of an established body of authoritative religious texts or doctrine.
-
E.
sacramentalDiscipline
Indicates the rules, practices, and regulations governing how religious sacraments are properly administered and received.
- 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_69c6995c463c8190a14458036249d419 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702ebb7448190ae8d47fe0cbb0907 |
completed | March 27, 2026, 10:21 p.m. |
| PD | Predicate disambiguation | batch_69c701683dec8190be9861e592aa8ce0 |
completed | March 27, 2026, 10:15 p.m. |
| PDg | Predicate description generation | batch_69c702e9a32081909a153190a62af426 |
completed | March 27, 2026, 10:21 p.m. |
Created at: March 27, 2026, 4:05 p.m.