Triple
T12326573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Il trittico |
E293843
|
entity |
| Predicate | subjectMatterGianniSchicchi |
P66010
|
FINISHED |
| Object | greed and comic deception |
—
|
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: greed and comic deception | Statement: [Il trittico, subjectMatterGianniSchicchi, greed and comic deception]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectMatterGianniSchicchi Context triple: [Il trittico, subjectMatterGianniSchicchi, greed and comic deception]
-
A.
associatedOpera
Indicates that there is a relationship linking an entity to an opera with which it is connected or related (e.g., as subject, inspiration, or context).
-
B.
theatricalSubject
chosen
Indicates that an entity serves as the main focus, topic, or subject within a theatrical work, performance, or dramatic context.
-
C.
inOperaRepertoire
Indicates that a musical work is included as part of the standard or active repertoire performed in opera productions.
-
D.
operaAct
Indicates that an entity performs in or takes part in an act (segment) of an opera performance.
-
E.
subjectOfFilm
Indicates that a person, character, or topic is the main focus or central topic depicted in a particular film.
- 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_69d6ab6ae0dc8190b1522a9c1c55c114 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f621570819091ee1db2609233ea |
completed | April 10, 2026, 6:20 p.m. |
| PD | Predicate disambiguation | batch_69d93ec5be788190b82d2edc6a0f1095 |
completed | April 10, 2026, 6:17 p.m. |
Created at: April 8, 2026, 9:53 p.m.