Triple
T28469414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diocese of Naumburg-Zeitz |
E720390
|
entity |
| Predicate | hadChapterAt |
P164747
|
FINISHED |
| Object | Naumburg Cathedral |
—
|
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: Naumburg Cathedral | Statement: [Diocese of Naumburg-Zeitz, hadChapterAt, Naumburg Cathedral]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadChapterAt Context triple: [Diocese of Naumburg-Zeitz, hadChapterAt, Naumburg Cathedral]
-
A.
hadChapterOf
Indicates that an entity (such as a book or document) includes or contains a specific chapter as one of its parts.
-
B.
hasHad
Indicates that an entity previously experienced, possessed, or was involved in something at some point in the past.
-
C.
hasGeneralChapter
Indicates that an entity is associated with, or contains, a general chapter (a broad or overarching section) within a larger structured document or framework.
-
D.
hadEvent
Indicates that an entity experienced, hosted, or was associated with a specific event at some point in time.
-
E.
hadFort
Indicates that an entity possessed, controlled, or contained a fort at some time.
- 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_69f01a58a67c819097936d9e8da8d6e6 |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69f64ee0c2788190a94a04ad1902fd5e |
completed | May 2, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_69f64caede108190a35cc7cbfead866f |
completed | May 2, 2026, 7:12 p.m. |
| PDg | Predicate description generation | batch_69f64e36c57c8190af09470a8d35512b |
completed | May 2, 2026, 7:19 p.m. |
Created at: April 28, 2026, 2:47 a.m.