Triple
T29528244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bagatelle in A minor, WoO 59 |
E749124
|
entity |
| Predicate | hasContrastingSectionKey |
P102537
|
FINISHED |
| Object | F major |
—
|
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: F major | Statement: [Bagatelle in A minor, WoO 59, hasContrastingSectionKey, F major]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasContrastingSectionKey Context triple: [Bagatelle in A minor, WoO 59, hasContrastingSectionKey, F major]
-
A.
contrastingSectionKey
chosen
Indicates that one musical section is in a key that contrasts harmonically with the key of another section.
-
B.
hasSectionColor
Indicates that an entity possesses a section (or part) characterized by a specific color.
-
C.
hasSectionInKey
Indicates that a key (such as a document, configuration, or data structure key) contains or is associated with a specific section within it.
-
D.
hasAlternateKeySections
Indicates that an entity is associated with one or more alternative key segments or components that can also uniquely identify it.
-
E.
hasSectionWith
Indicates that an entity contains or includes a specific section that satisfies certain conditions or characteristics.
- 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_69f0bd46d99c81908ba9d01cc1dbef7d |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_6a007338002081908cb6340d65ff86d5 |
completed | May 10, 2026, 11:59 a.m. |
| PD | Predicate disambiguation | batch_6a0072a137ac8190a7debeb28e738e03 |
completed | May 10, 2026, 11:57 a.m. |
Created at: April 28, 2026, 4:49 p.m.