Triple
T23310476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nun danket alle Gott |
E590568
|
entity |
| Predicate | textAuthorOccupation |
P938
|
FINISHED |
| Object | Lutheran clergyman |
—
|
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: Lutheran clergyman | Statement: [Nun danket alle Gott, textAuthorOccupation, Lutheran clergyman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: textAuthorOccupation Context triple: [Nun danket alle Gott, textAuthorOccupation, Lutheran clergyman]
-
A.
authorOccupation
chosen
Indicates the professional role or job that an author holds or is associated with.
-
B.
hasAuthorOccupationOfAuthor
Indicates that an author has a specific occupation or professional role.
-
C.
creatorOccupation
Indicates the professional role or job that the creator of an entity holds or held.
-
D.
hasBiographicalSubjectOccupation
Indicates that the biographical subject is or was engaged in the specified occupation or profession.
-
E.
genreOfWorkHeWrites
Indicates that a person is an author who writes works belonging to a particular genre.
- 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_69e25d1d32188190948eb76909d1dcc3 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1972acad08190bb56541b822555cd |
completed | April 29, 2026, 5:29 a.m. |
| PD | Predicate disambiguation | batch_69effcf8ca2c8190887d4f4656617d21 |
completed | April 28, 2026, 12:19 a.m. |
Created at: April 17, 2026, 5:05 p.m.