Triple
T20272559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bavarian State Orchestra |
E502925
|
entity |
| Predicate | hasSubgenreSpecialization |
P127457
|
FINISHED |
| Object | opera orchestra repertoire |
—
|
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: opera orchestra repertoire | Statement: [Bavarian State Orchestra, hasSubgenreSpecialization, opera orchestra repertoire]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubgenreSpecialization Context triple: [Bavarian State Orchestra, hasSubgenreSpecialization, opera orchestra repertoire]
-
A.
isAssociatedWithSubgenre
chosen
Indicates that one entity has a connection or linkage to a specific subgenre of a broader category.
-
B.
hasNotableSubgenre
Indicates that one genre is recognized as a particularly significant or prominent subgenre of another genre.
-
C.
hasSubdiscipline
Indicates that one discipline includes another, more specialized field of study as a subordinate branch.
-
D.
hasSpecialCategory
Indicates that an entity is associated with a designated special or exceptional category distinct from its standard classifications.
-
E.
hasSpecialty
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
- 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_69e0b4b0e79c8190bd61f22ef1329fa8 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e675dff3c4819098ba45eba4e7b296 |
completed | April 20, 2026, 6:52 p.m. |
| PD | Predicate disambiguation | batch_69e55b1e5e1c8190ba8a5544b1db9e1d |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 16, 2026, 10:15 a.m.