Triple
T20014768
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sou de Qualquer Lugar |
E494685
|
entity |
| Predicate | performsRoleInCareer |
P7222
|
FINISHED |
| Object | showcases Daniela Mercury's energetic style |
—
|
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: showcases Daniela Mercury's energetic style | Statement: [Sou de Qualquer Lugar, performsRoleInCareer, showcases Daniela Mercury's energetic style]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: performsRoleInCareer Context triple: [Sou de Qualquer Lugar, performsRoleInCareer, showcases Daniela Mercury's energetic style]
-
A.
roleDuringOccupation
Indicates the specific role or position an entity held during a particular occupation or period of control.
-
B.
employedRole
Indicates that an entity holds or performs a specific role or position within an employment or work context.
-
C.
roleInIndustry
Indicates the specific function, position, or capacity an entity holds within a particular industry or sector.
-
D.
describesCareerOf
chosen
Indicates that one entity provides a description or characterization of the professional career of another entity.
-
E.
roleInExperience
Indicates the specific function, position, or part an entity plays within a particular experience or event.
- 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_69da626bfd288190aa5d65098b6433ae |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6623bba1881908440c92f08729ec1 |
completed | April 20, 2026, 5:28 p.m. |
| PD | Predicate disambiguation | batch_69e54cdddbd48190becc8b2aa5ab4ef9 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 3:34 p.m.