Triple
T33464436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bishop of Mainz (lower rank) |
E857006
|
entity |
| Predicate | wearsVestment |
P271
|
FINISHED |
| Object | mitre |
—
|
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: mitre | Statement: [Bishop of Mainz (lower rank), wearsVestment, mitre]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wearsVestment Context triple: [Bishop of Mainz (lower rank), wearsVestment, mitre]
-
A.
hasClericalVestments
Indicates that one entity possesses or is associated with the clerical vestments (religious garments) of another entity.
-
B.
requiresVestments
Indicates that performing the related action or role necessitates wearing specific ceremonial or official vestments.
-
C.
wears
chosen
Indicates that one entity is dressed in, or has on its body, a particular item such as clothing or accessories.
-
D.
oftenDepictedWearing
Indicates that an entity is frequently shown or represented as wearing a particular item or type of clothing in depictions or portrayals.
-
E.
usesDressing
Indicates that one entity applies or employs a particular dressing (such as a sauce, covering, or treatment) in relation to another entity or context.
- 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_69f34973461481909c701c98ebd75623 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f8164698819090c1b471f1caa4c6 |
completed | May 3, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69f6f6619404819084662aef1238261c |
completed | May 3, 2026, 7:16 a.m. |
Created at: May 1, 2026, 1:37 a.m.