Triple
T36222093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeremy Caner |
E1047875
|
entity |
| Predicate | employerWithinFiction |
P109189
|
FINISHED |
| Object | Everlasting production |
—
|
NE NERFINISHED |
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: Everlasting production | Statement: [Jeremy Caner, employerWithinFiction, Everlasting production]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employerWithinFiction Context triple: [Jeremy Caner, employerWithinFiction, Everlasting production]
-
A.
employerInPlot
Indicates that one entity serves as the employer of another within the context of a specific plot or storyline.
-
B.
employerInUniverse
Indicates that one entity serves as the employer of another within a specified universe, context, or world.
-
C.
employerFictionalIndustry
Indicates that one entity is the employer of another within a fictional or imaginary industry or professional field.
-
D.
employerInReality
Indicates that one entity is the actual, real-world employer of another entity, as opposed to a nominal, legal, or assumed employer.
-
E.
worksForFictionalOrganization
chosen
Indicates that an entity is employed by or affiliated as a worker with a fictional organization.
- 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_69f76e42c878819095c8d19c0267fb87 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fbbc49da8c8190902bbb05d2477cab |
completed | May 6, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69fbb13f34b08190bbbb220ac1e6e666 |
completed | May 6, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:09 p.m.