Triple
T8469426
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Variations on a Theme by Schumann, Op. 9 |
E200243
|
entity |
| Predicate | numberOfVariations |
P48909
|
FINISHED |
| Object | 16 |
—
|
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: 16 | Statement: [Variations on a Theme by Schumann, Op. 9, numberOfVariations, 16]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfVariations Context triple: [Variations on a Theme by Schumann, Op. 9, numberOfVariations, 16]
-
A.
variantCount
chosen
Indicates the number of distinct variants associated with a given entity or item.
-
B.
hasApproximateNumberOfVarieties
Indicates that an entity is associated with an estimated or non-exact count of different varieties or types.
-
C.
numberOfPrimaryVarieties
Indicates the count of distinct primary varieties associated with a given entity.
-
D.
colorVarietyCount
Indicates the number of distinct colors associated with or present in a given entity or set of entities.
-
E.
numberOfParametersOfLargestVariant
Indicates the total count of parameters in the variant that has the greatest number of parameters among all variants of an 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_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. |
Created at: March 30, 2026, 6:11 p.m.