Triple
T15630588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Titular Archbishop of Belcastro |
E375799
|
entity |
| Predicate | hasCanonicalCategory |
P119518
|
FINISHED |
| Object | titular archiepiscopal see |
—
|
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: titular archiepiscopal see | Statement: [Titular Archbishop of Belcastro, hasCanonicalCategory, titular archiepiscopal see]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCanonicalCategory Context triple: [Titular Archbishop of Belcastro, hasCanonicalCategory, titular archiepiscopal see]
-
A.
hasCanonicalTerm
Indicates that one term in a set is designated as the standard or authoritative form used to represent a concept or entity.
-
B.
hasCanonicalAspect
Indicates that an entity is associated with its standard or officially recognized aspect, form, or representation.
-
C.
hasCanonicalGenre
Indicates that an entity is associated with its primary or officially recognized genre classification.
-
D.
hasCategoryOn
Indicates that something is assigned to or associated with a specific category within a given context or scope.
-
E.
hasCanonicalContext
Indicates that something is associated with its primary, standard, or officially recognized contextual setting or framework.
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04eb536348190b93ed3c178d1ffb8 |
completed | April 16, 2026, 2:51 a.m. |
| PD | Predicate disambiguation | batch_69deda868d4481908f4bce1c64d2902a |
completed | April 15, 2026, 12:23 a.m. |
| PDg | Predicate description generation | batch_69dff7f3016c8190ac68d76e65e07af4 |
completed | April 15, 2026, 8:41 p.m. |
Created at: April 10, 2026, 4:14 a.m.