Triple
T37969286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prince-Bishop of Regensburg |
E947233
|
entity |
| Predicate | usedTitleInLatin |
P9999
|
FINISHED |
| Object | Princeps-Episcopus Ratisbonensis |
—
|
NE NERFINISHED |
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: Princeps-Episcopus Ratisbonensis | Statement: [Prince-Bishop of Regensburg, usedTitleInLatin, Princeps-Episcopus Ratisbonensis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedTitleInLatin Context triple: [Prince-Bishop of Regensburg, usedTitleInLatin, Princeps-Episcopus Ratisbonensis]
-
A.
hasLatinTitle
chosen
Indicates that an entity possesses a title or name expressed in Latin.
-
B.
titleInLatinScript
Indicates that the title of an entity is written or represented using a Latin-based writing system.
-
C.
hasLatinTitleOf
Indicates that one entity has, uses, or is associated with the Latin-language title corresponding to another entity.
-
D.
usedTitleIn
Indicates that one entity employed or referenced another entity as a title in some context.
-
E.
usesTitleIn
Indicates that an entity is referred to using a particular title within a specified context or medium.
- 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_69f76ef7db908190bba6086673a32300 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fc4748843c8190931432653be4890c |
completed | May 7, 2026, 8:03 a.m. |
| PD | Predicate disambiguation | batch_69fc45646ce481908caf292ff9f06e15 |
completed | May 7, 2026, 7:55 a.m. |
Created at: May 3, 2026, 4:20 p.m.