Triple
T26356258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Saw |
E663060
|
entity |
| Predicate | careerShift |
P71077
|
FINISHED |
| Object | transitioned from secular dancehall to gospel music |
—
|
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: transitioned from secular dancehall to gospel music | Statement: [Lady Saw, careerShift, transitioned from secular dancehall to gospel music]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerShift Context triple: [Lady Saw, careerShift, transitioned from secular dancehall to gospel music]
-
A.
occupationalChange
chosen
Indicates a change in a person’s job, profession, or occupational status over time.
-
B.
careerImpact
Indicates how one entity influences or changes another entity’s professional trajectory, opportunities, or outcomes.
-
C.
careerStart
Indicates the point in time when an entity begins its professional career or main occupational activity.
-
D.
careerAssists
Indicates the total number of assists a player has recorded over the entire span of their professional or competitive career.
-
E.
careerTackles
Indicates the total number of tackles a player has made over the course of their entire career.
- 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_69ee8130fc44819094e5ab1da201cd7b |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f60fef966c8190997cdcd6d8a5c380 |
completed | May 2, 2026, 2:53 p.m. |
| PD | Predicate disambiguation | batch_69f5f800fa9c8190aab0962669fde8ac |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 26, 2026, 10:48 p.m.