Triple
T16841454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Borsetshire |
E409421
|
entity |
| Predicate | hasFictionalTopography |
P116836
|
FINISHED |
| Object | rolling countryside |
—
|
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: rolling countryside | Statement: [Borsetshire, hasFictionalTopography, rolling countryside]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalTopography Context triple: [Borsetshire, hasFictionalTopography, rolling countryside]
-
A.
hasFictionalLocation
Indicates that an entity is associated with, set in, or takes place within a location that exists only in fiction rather than in the real world.
-
B.
hasFictionalLandmark
Indicates that one entity includes, features, or is associated with a landmark that is fictional rather than real.
-
C.
hasBranchInFictionalLocation
Indicates that an organization maintains a branch, office, or presence within a fictional or imaginary location.
-
D.
hasFictionalSettingElement
chosen
Indicates that something includes or is associated with a specific element or component of a fictional setting.
-
E.
hasFictionalTownBasedOn
Indicates that a fictional town is modeled on, inspired by, or derived from a specific real-world town or location.
- 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_69d883952b048190887740a980b712ed |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b35167a48190b45a459023e3ab1b |
completed | April 18, 2026, 4:37 p.m. |
| PD | Predicate disambiguation | batch_69e32b87b4248190aaddb05e88452356 |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:24 a.m.