Triple
T38196796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark Wynter |
E1005638
|
entity |
| Predicate | hasCareerShift |
P71077
|
FINISHED |
| Object | from pop singer to musical theatre actor |
—
|
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: from pop singer to musical theatre actor | Statement: [Mark Wynter, hasCareerShift, from pop singer to musical theatre actor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCareerShift Context triple: [Mark Wynter, hasCareerShift, from pop singer to musical theatre actor]
-
A.
hasPastOccupation
Indicates that an entity previously held a particular job, role, or occupation in the past.
-
B.
occupationalChange
chosen
Indicates a change in a person’s job, profession, or occupational status over time.
-
C.
hasCareerTrack
Indicates that an entity is associated with or follows a particular career path or professional progression.
-
D.
hasCareerService
Indicates that an entity provides or is associated with a career-related support or advisory service for another entity.
-
E.
hasCareerFunction
Indicates that an entity performs, is associated with, or is responsible for a specific career-related role or function.
- 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_69f76dbd22f48190940318cea061e8bb |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a0056174c908190be99c91a70393e47 |
completed | May 10, 2026, 9:55 a.m. |
| PD | Predicate disambiguation | batch_6a00538e7e08819091ecd4316cd641a1 |
completed | May 10, 2026, 9:44 a.m. |
Created at: May 3, 2026, 4:29 p.m.