Triple
T1794593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montserrat Caballé |
E39574
|
entity |
| Predicate | voiceDescription |
P2000
|
FINISHED |
| Object | powerful yet delicate voice |
—
|
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: powerful yet delicate voice | Statement: [Montserrat Caballé, voiceDescription, powerful yet delicate voice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: voiceDescription Context triple: [Montserrat Caballé, voiceDescription, powerful yet delicate voice]
-
A.
voiceType
chosen
Indicates the specific vocal style, quality, or role associated with an entity’s voice in a given context.
-
B.
voiceWork
Indicates that one entity performs or contributes voice acting or vocal performance work for another entity or creative production.
-
C.
speakerType
Indicates the role or category of a participant in a communicative act (e.g., narrator, quoted speaker, system voice) within a given context.
-
D.
speechType
Indicates the specific category or form of spoken or written communication that an utterance or speech act belongs to (e.g., question, statement, command).
-
E.
voiceActingChange
Indicates that the voice actor associated with an entity has been changed from one performer to another.
- 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_69a88631854081909723959921e45c2b |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab61b6ea188190aab9fb839bf1e367 |
completed | March 6, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69aa61d2f7a8819090301f92d3e358c7 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:32 p.m.