Triple
T32380715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Violin Concerto in A minor, Op. 82 |
E827407
|
entity |
| Predicate | hasContrastingKeyAreas |
P21343
|
FINISHED |
| Object | A 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: A major | Statement: [Violin Concerto in A minor, Op. 82, hasContrastingKeyAreas, A major]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasContrastingKeyAreas Context triple: [Violin Concerto in A minor, Op. 82, hasContrastingKeyAreas, A major]
-
A.
hasMainContrast
chosen
Indicates a primary opposing or differing relationship between two elements, highlighting the main point of contrast between them.
-
B.
hasDistinctArea
Indicates that one entity possesses an area or region that is separate and distinguishable from that of another entity.
-
C.
hasContrastingCharacterType
Indicates that two characters are of distinctly different or opposing character types or archetypes.
-
D.
hasDensityContrast
Indicates that one entity differs from another in material density, highlighting a contrast in how compact or dense they are.
-
E.
providesContrastWith
Indicates that one entity is used to highlight differences or distinctions when compared with another entity.
- 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_69f349177ddc8190ab0583f05597056b |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a037c894b488190bcbec2eccaff4a01 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379edf2d88190b492fca86ed23cac |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 12:51 a.m.