Triple
T7084168
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cunsey Beck |
E165032
|
entity |
| Predicate | hasGeographicalSetting |
P3227
|
FINISHED |
| Object | rural area |
—
|
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: rural area | Statement: [Cunsey Beck, hasGeographicalSetting, rural area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGeographicalSetting Context triple: [Cunsey Beck, hasGeographicalSetting, rural area]
-
A.
geographicContext
chosen
Indicates that one entity is situated within, associated with, or characterized by the geographic setting or region defined by another entity.
-
B.
hasGeographyCharacteristic
Indicates that an entity possesses a specific geographical feature, property, or attribute.
-
C.
isGeographicalEntity
Indicates that something exists as a distinct geographic feature, area, or place within physical space.
-
D.
geographicalPractice
Indicates a relationship where an entity engages in or is associated with a practice, activity, or method that is specific to or characteristic of a particular geographic area or location.
-
E.
geographicalRegionType
Indicates the specific kind or category of geographical region that an entity belongs to (e.g., continent, country, province, or city).
- 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_69c6887d98408190912b9580666b0c1d |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e5102be08190bbde790bfa8fe9e2 |
completed | March 27, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69c6e1bfcb948190a5ada74fb8c054cb |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:40 p.m.