Triple
T8468902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Symphony No. 3 in E-flat major, Op. 97 |
E200231
|
entity |
| Predicate | hasKeySignature |
P83458
|
FINISHED |
| Object | three flats |
—
|
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: three flats | Statement: [Symphony No. 3 in E-flat major, Op. 97, hasKeySignature, three flats]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasKeySignature Context triple: [Symphony No. 3 in E-flat major, Op. 97, hasKeySignature, three flats]
-
A.
hasTimeSignature
Indicates that a musical work, passage, or segment is associated with a specific time signature defining its rhythmic meter.
-
B.
hasMelody
Indicates that one entity possesses, contains, or is characterized by a particular melody.
-
C.
hasSignatureSong
Indicates that an artist or performer is especially associated with a particular song that is widely recognized as their defining or most iconic work.
-
D.
hasFrequencyNote
Indicates that something is associated with a specific note describing how often it occurs or is repeated.
-
E.
hasKeyFigure
Indicates that an entity includes, involves, or is characterized by an important or central person relevant to it.
- 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_69cbe4d445f48190884df64bb1aebd41 |
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.