Triple
T35105158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grandet house in Saumur |
E1013130
|
entity |
| Predicate | setInRegionFictional |
P152884
|
FINISHED |
| Object | Loire Valley |
—
|
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: Loire Valley | Statement: [Grandet house in Saumur, setInRegionFictional, Loire Valley]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: setInRegionFictional Context triple: [Grandet house in Saumur, setInRegionFictional, Loire Valley]
-
A.
setInFictionalizedRegionOf
Indicates that an event or narrative is located within a region that is a fictionalized or altered version of a real-world place.
-
B.
belongsToFictionalRegion
Indicates that an entity is located within, associated with, or under the jurisdiction of a fictional or imaginary geographic region.
-
C.
associatedWithRegionInFiction
chosen
Indicates that, within a fictional context, an entity is linked or connected to a particular region or geographic area.
-
D.
fictionalSettingRegion
Indicates that a fictional setting is located within or associated with a specific geographic or administrative region.
-
E.
setInFictionalLocation
Indicates that an event, story, or narrative takes place within a fictional or imagined location rather than a real-world setting.
- 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_69f76dd556248190808b4c4f43debebb |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f791cc969c8190bf187d6031a030d5 |
completed | May 3, 2026, 6:19 p.m. |
| PD | Predicate disambiguation | batch_69f791033d288190b118029fe412b9c9 |
completed | May 3, 2026, 6:16 p.m. |
Created at: May 3, 2026, 4:01 p.m.