Triple
T31931185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wrong Turn |
E815253
|
entity |
| Predicate | fictionalSettingLocation |
P114636
|
FINISHED |
| Object | West Virginia wilderness |
—
|
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: West Virginia wilderness | Statement: [Wrong Turn, fictionalSettingLocation, West Virginia wilderness]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalSettingLocation Context triple: [Wrong Turn, fictionalSettingLocation, West Virginia wilderness]
-
A.
basedInFictionalSetting
Indicates that an entity’s primary location or setting exists within a fictional or imaginary world rather than the real world.
-
B.
townOfFictionalSetting
Indicates that a town serves as the fictional setting or primary location where the events of a narrative work take place.
-
C.
fictionalPlaceType
Indicates that a place is a fictional location and specifies what type or category of fictional place it is.
-
D.
fictionalSettingRegion
chosen
Indicates that a fictional setting is located within or associated with a specific geographic or administrative region.
-
E.
fictionalCitySetting
Indicates that a narrative, event, or work is set in a city that is imaginary or does not exist in the real world.
- 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_69f348f3035c81908558e2339955abb3 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fd8e5f7c4c8190ab8e2f2a7bb1bd79 |
completed | May 8, 2026, 7:18 a.m. |
| PD | Predicate disambiguation | batch_69fd8d8a16f08190b9e880901bfa44fe |
completed | May 8, 2026, 7:15 a.m. |
Created at: May 1, 2026, 12:04 a.m.