Triple
T28332181
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Il barbiere di Siviglia |
E717564
|
entity |
| Predicate | mainFemaleRoleVoiceType |
P2000
|
FINISHED |
| Object | mezzo-soprano or soprano |
—
|
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: mezzo-soprano or soprano | Statement: [Il barbiere di Siviglia, mainFemaleRoleVoiceType, mezzo-soprano or soprano]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainFemaleRoleVoiceType Context triple: [Il barbiere di Siviglia, mainFemaleRoleVoiceType, mezzo-soprano or soprano]
-
A.
voiceType
chosen
Indicates the specific vocal style, quality, or role associated with an entity’s voice in a given context.
-
B.
voiceActorFemale
Indicates that the subject is a female voice actor who provides the voice for the specified character or role.
-
C.
voiceTypeOfValentine
Indicates that one entity is the type or style of voice associated with a Valentine-related expression or message of another entity.
-
D.
femaleRole
Indicates that the role, function, or position involved is associated with or designated as female.
-
E.
vocalGender
Indicates the perceived or assigned gender associated with an entity’s voice in a vocal performance or recording.
- 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_69eff6e9a57c8190a69c2c74b5d72119 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f64e37f7fc819083809149b6661e3c |
completed | May 2, 2026, 7:19 p.m. |
| PD | Predicate disambiguation | batch_69f64caede108190a35cc7cbfead866f |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 28, 2026, 12:33 a.m.