Triple
T32328935
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Josefina Scaglione |
E825993
|
entity |
| Predicate | vocalOccupation |
P169071
|
FINISHED |
| Object | musical theatre singer |
—
|
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: musical theatre singer | Statement: [Josefina Scaglione, vocalOccupation, musical theatre singer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vocalOccupation Context triple: [Josefina Scaglione, vocalOccupation, musical theatre singer]
-
A.
vocalistIn
Indicates that a person serves as a vocalist (singer) in a particular musical group, band, or ensemble.
-
B.
singerVoiceActor
Indicates that the subject is both a singer and a voice actor for the object, or performs voice-acting roles in addition to singing in relation to the object.
-
C.
vocal
Indicates that an entity produces or is characterized by audible sounds, speech, or vocalizations.
-
D.
singerCharacterProfession
chosen
Indicates that the character’s profession or primary occupation is that of a singer.
-
E.
vocalOrganSpecialization
Indicates a relationship where an entity’s vocal organ is specially adapted or modified for a particular function or mode of sound production.
- 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_69f34912d0c48190bba75770660320e9 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f71422adac8190a5ceb32dcf820833 |
completed | May 3, 2026, 9:23 a.m. |
| PD | Predicate disambiguation | batch_69f712764d2c819081b64b27e5de4a13 |
completed | May 3, 2026, 9:16 a.m. |
Created at: May 1, 2026, 12:47 a.m.