Triple
T29179889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | II. Romanze: Andante non troppo con grazia |
E739716
|
entity |
| Predicate | characterIndication |
P193250
|
FINISHED |
| Object | con grazia |
—
|
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: con grazia | Statement: [II. Romanze: Andante non troppo con grazia, characterIndication, con grazia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterIndication Context triple: [II. Romanze: Andante non troppo con grazia, characterIndication, con grazia]
-
A.
character2
Indicates that a second character entity is involved in the relationship or context defined by the predicate.
-
B.
character3
Indicates a tertiary or additional character role associated with an entity, typically the third distinct character linked within a given context or work.
-
C.
characterRepresentation
Indicates a relationship where one entity serves as the symbolic, visual, or conceptual depiction of another entity’s character or identity.
-
D.
characterReception
Indicates how audiences, critics, or communities respond to or evaluate a particular character.
-
E.
characterAddressed
Indicates that one character directs speech, communication, or attention specifically toward another character.
- 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_69f07cb74c2c8190ad396487fcb4fde6 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69fd3d46d1f48190a1b20dd063224b7d |
completed | May 8, 2026, 1:32 a.m. |
| PD | Predicate disambiguation | batch_69fd3ae1510c81908fe1280efc17feee |
completed | May 8, 2026, 1:22 a.m. |
| PDg | Predicate description generation | batch_69fd3d45ccb8819082f15e60bd33afc9 |
completed | May 8, 2026, 1:32 a.m. |
Created at: April 28, 2026, 11:56 a.m.