Triple
T10165185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bishop of Strängnäs |
E235187
|
entity |
| Predicate | hasLiturgicalVestments |
P42772
|
FINISHED |
| Object | bishop’s vestments |
—
|
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: bishop’s vestments | Statement: [Bishop of Strängnäs, hasLiturgicalVestments, bishop’s vestments]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLiturgicalVestments Context triple: [Bishop of Strängnäs, hasLiturgicalVestments, bishop’s vestments]
-
A.
hasClericalVestments
chosen
Indicates that one entity possesses or is associated with the clerical vestments (religious garments) of another entity.
-
B.
hasLiturgicalColor
Indicates that something is associated with a specific liturgical color used in religious rites or ceremonies.
-
C.
hasLiturgicalSymbol
Indicates that one entity serves as a liturgical symbol or emblem associated with another entity within a religious or worship context.
-
D.
hasLiturgicalPosture
Indicates that an entity is associated with a specific bodily posture or position used in a liturgical or religious ritual context.
-
E.
requiresVestments
Indicates that performing the related action or role necessitates wearing specific ceremonial or official vestments.
- 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_69ca84ceafd0819085828600e11bed6b |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdec6b96dc8190ae37d0d28e4c393b |
completed | April 2, 2026, 4:11 a.m. |
| PD | Predicate disambiguation | batch_69cd4ba795808190acc9124c98c6e40f |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 9:10 p.m.