Triple
T27617566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belle Reeve |
E700479
|
entity |
| Predicate | fictionalGeographicContext |
P114636
|
FINISHED |
| Object | American South |
—
|
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: American South | Statement: [Belle Reeve, fictionalGeographicContext, American South]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalGeographicContext Context triple: [Belle Reeve, fictionalGeographicContext, American South]
-
A.
fictionalGeographicRegion
Indicates that a geographic region exists only in fiction or imagination rather than in the real world.
-
B.
fictionalSettingRegion
chosen
Indicates that a fictional setting is located within or associated with a specific geographic or administrative region.
-
C.
fictionalPlaceType
Indicates that a place is a fictional location and specifies what type or category of fictional place it is.
-
D.
fictionalCitySetting
Indicates that a narrative, event, or work is set in a city that is imaginary or does not exist in the real world.
-
E.
locatedInFictionalContext
Indicates that one entity exists or occurs within the setting or universe of a fictional work associated with another entity.
- 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_69ef6a4f1d9c8190b0705acda054368d |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f6f8565134819096aac0175f924a9f |
completed | May 3, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69f6f65fd1d08190b88e5e68ba268500 |
completed | May 3, 2026, 7:16 a.m. |
Created at: April 27, 2026, 2:13 p.m.