Triple
T8469769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Schumann: Arabeske in C major, Op. 18 |
E200250
|
entity |
| Predicate | nationalSchool |
P83477
|
FINISHED |
| Object | German Romanticism |
—
|
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: German Romanticism | Statement: [Robert Schumann: Arabeske in C major, Op. 18, nationalSchool, German Romanticism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nationalSchool Context triple: [Robert Schumann: Arabeske in C major, Op. 18, nationalSchool, German Romanticism]
-
A.
majorSchool
Indicates that one entity is the primary or most significant school or educational institution associated with another entity.
-
B.
publicSchool
Indicates that an educational institution is operated and funded by a government or public authority rather than by private entities.
-
C.
hasNumberOfSchools
Indicates the quantity of schools associated with a given entity.
-
D.
educationFacility
Indicates that one entity functions as an institution or place where the other entity receives or provides education or training.
-
E.
schoolSector
Indicates the educational sector or category (such as public, private, or charter) to which a school belongs.
- 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_69ca831a4f348190bfdd09250e86ae35 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe4d5dd088190a79050417f527f14 |
completed | March 31, 2026, 3:14 p.m. |
| PD | Predicate disambiguation | batch_69cbd10072cc819084be1ed9ac7ebe9d |
completed | March 31, 2026, 1:49 p.m. |
| PDg | Predicate description generation | batch_69cbe30c2d088190b4cb89adb4e88273 |
completed | March 31, 2026, 3:06 p.m. |
Created at: March 30, 2026, 6:11 p.m.