Triple
T33683284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jonah Levin |
E862958
|
entity |
| Predicate | fictionalProfessionStatus |
P197369
|
FINISHED |
| Object | past commercial success |
—
|
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: past commercial success | Statement: [Jonah Levin, fictionalProfessionStatus, past commercial success]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalProfessionStatus Context triple: [Jonah Levin, fictionalProfessionStatus, past commercial success]
-
A.
fictionalCareerStatus
Indicates the relationship between an entity and a career or professional role that exists only in a fictional or imagined context, rather than in real life.
-
B.
fictionalOccupation
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
-
C.
hasFictionalProfessionLevel
Indicates that an entity holds a fictional or imagined profession at a specified level, rank, or degree of expertise.
-
D.
fictionalProfessionSpecialty
Indicates that a fictional character’s professional role is specialized in a particular subfield, focus area, or niche within that profession.
-
E.
fictionalStatus
Indicates that an entity exists only in imagination or narrative and does not correspond to a real-world counterpart.
- F. None of above. chosen
Provenance (4 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_69f3498662b48190904442c39df84fb7 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fe8ddf70e48190a917eb9e8f7b6966 |
completed | May 9, 2026, 1:29 a.m. |
| PD | Predicate disambiguation | batch_69fe87ef94dc81909bb00ec8d6de9bcd |
completed | May 9, 2026, 1:03 a.m. |
| PDg | Predicate description generation | batch_69fe8dde8d008190b03dc0f97618073c |
completed | May 9, 2026, 1:29 a.m. |
Created at: May 1, 2026, 1:43 a.m.