Triple
T33767252
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pennsylvania Route 641 |
E865269
|
entity |
| Predicate | connectsToAreaType |
P6822
|
FINISHED |
| Object | rural areas |
—
|
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 areas | Statement: [Pennsylvania Route 641, connectsToAreaType, rural areas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsToAreaType Context triple: [Pennsylvania Route 641, connectsToAreaType, rural areas]
-
A.
connectsTypeOfAreas
Indicates a relationship where one entity serves as a link or connector between two different types of areas.
-
B.
connectsToAreaKnownFor
Indicates that something has a direct link or association to a specific area that is recognized or notable for certain characteristics or features.
-
C.
connectsArea
Indicates that one area serves as a link or passage between two other areas, enabling movement or interaction between them.
-
D.
hasAreaConnections
Indicates that an entity is linked to one or more surrounding or related areas, typically representing spatial or regional connections between them.
-
E.
hasAreaType
chosen
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
- 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_69f3498d3b748190aa3c4006c1f32f38 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff289541e0819096eeceb8e6332650 |
completed | May 9, 2026, 12:29 p.m. |
| PD | Predicate disambiguation | batch_69ff281ab1988190920f0443be9f10cc |
completed | May 9, 2026, 12:27 p.m. |
Created at: May 1, 2026, 1:45 a.m.