Triple
T34368024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sparta High School |
E882071
|
entity |
| Predicate | regionInFictionalSetting |
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: [Sparta High School, regionInFictionalSetting, American South]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionInFictionalSetting Context triple: [Sparta High School, regionInFictionalSetting, American South]
-
A.
fictionalSettingRegion
chosen
Indicates that a fictional setting is located within or associated with a specific geographic or administrative region.
-
B.
basedInFictionalSetting
Indicates that an entity’s primary location or setting exists within a fictional or imaginary world rather than the real world.
-
C.
stateOfFictionalLocation
Indicates that a fictional location is situated within or belongs to a particular state or state-like administrative region.
-
D.
fictionalGeographicRegion
Indicates that a geographic region exists only in fiction or imagination rather than in the real world.
-
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_69f349be5c9c81908dc726ae1f4c68f2 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff76ac40988190a34d858b5472ee2b |
completed | May 9, 2026, 6:02 p.m. |
| PD | Predicate disambiguation | batch_69ff760a90948190a12fcb80e6e3e14b |
completed | May 9, 2026, 5:59 p.m. |
Created at: May 1, 2026, 1:58 a.m.