Triple
T38448736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dimrill Gate |
E906708
|
entity |
| Predicate | inFictionalWorldRegion |
P152884
|
FINISHED |
| Object | Wilderland |
—
|
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: Wilderland | Statement: [Dimrill Gate, inFictionalWorldRegion, Wilderland]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inFictionalWorldRegion Context triple: [Dimrill Gate, inFictionalWorldRegion, Wilderland]
-
A.
fictionalSettingRegion
Indicates that a fictional setting is located within or associated with a specific geographic or administrative region.
-
B.
fictionalGeographicRegion
Indicates that a geographic region exists only in fiction or imagination rather than in the real world.
-
C.
associatedWithRegionInFiction
chosen
Indicates that, within a fictional context, an entity is linked or connected to a particular region or geographic area.
-
D.
setInFictionalizedRegionOf
Indicates that an event or narrative is located within a region that is a fictionalized or altered version of a real-world place.
-
E.
fictionalPlaceType
Indicates that a place is a fictional location and specifies what type or category of fictional place it is.
- 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_69f76e72878c8190a692836c8b01b58b |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fcce7afcd08190906cc3801152656a |
completed | May 7, 2026, 5:40 p.m. |
| PD | Predicate disambiguation | batch_69fcccf140ec8190862d53388a5f40d7 |
completed | May 7, 2026, 5:33 p.m. |
Created at: May 3, 2026, 4:31 p.m.