Triple
T5271282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Camille Keaton |
E119262
|
entity |
| Predicate | hasCareerFocus |
P14530
|
FINISHED |
| Object | genre films |
—
|
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: genre films | Statement: [Camille Keaton, hasCareerFocus, genre films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCareerFocus Context triple: [Camille Keaton, hasCareerFocus, genre films]
-
A.
managedCareerOf
Indicates that one entity was responsible for overseeing, directing, or handling the professional career of another entity.
-
B.
hasCareerTrack
chosen
Indicates that an entity is associated with or follows a particular career path or professional progression.
-
C.
targetCareer
Indicates that one entity is the intended or pursued career or professional goal of another entity.
-
D.
studCareerBegan
Indicates that a student's professional or academic career started at a specified time or institution.
-
E.
hasProgrammingFocus
Indicates that something is centered on, specialized in, or primarily concerned with programming.
- 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_69bd446c38e081908cdaf113bdf86790 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7d5a23908190a24e79d1b29d6fcf |
completed | March 20, 2026, 5:01 p.m. |
| PD | Predicate disambiguation | batch_69bd77c71268819094f9f5203eed392d |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:51 p.m.