Triple
T37251271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nightmute, Alaska |
E923995
|
entity |
| Predicate | fictionalPortrayalType |
P162583
|
FINISHED |
| Object | crime thriller setting |
—
|
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: crime thriller setting | Statement: [Nightmute, Alaska, fictionalPortrayalType, crime thriller setting]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalPortrayalType Context triple: [Nightmute, Alaska, fictionalPortrayalType, crime thriller setting]
-
A.
fictionalPortrayalOf
Indicates that one entity is a fictional representation, depiction, or dramatization of another entity.
-
B.
fictionalPortrayalSubject
Indicates that one entity is the subject or topic being portrayed, depicted, or represented in a fictional work by another entity.
-
C.
fictionalType
chosen
Indicates that one entity is a fictional or imaginary type or category of the other entity.
-
D.
portraysFictionalized
Indicates that one entity represents or depicts another entity in a fictionalized or altered manner, rather than as a strictly accurate portrayal.
-
E.
fictionalCharacterDepicted
Indicates that one entity is a fictional character and the other is a work or medium in which that character is visually or narratively depicted.
- 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_69f76eaabb4c819093b751b139dad551 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69ff53389a0481908b2baeb43c6294f0 |
completed | May 9, 2026, 3:31 p.m. |
| PD | Predicate disambiguation | batch_69ff52e2b4b88190b38d160d771fe14b |
completed | May 9, 2026, 3:29 p.m. |
Created at: May 3, 2026, 4:15 p.m.