Triple
T21365448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pennsylvania Route 28 |
E526896
|
entity |
| Predicate | hasRuralSection |
P143998
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Pennsylvania Route 28, hasRuralSection, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRuralSection Context triple: [Pennsylvania Route 28, hasRuralSection, yes]
-
A.
hasRuralArea
Indicates that an entity includes, is associated with, or contains a countryside or sparsely populated geographic area.
-
B.
hasLongRuralSectionsIn
Indicates that something (such as a route or infrastructure) contains extended stretches that pass through rural areas within a specified region or location.
-
C.
hasRuralLocality
Indicates that an entity possesses, includes, or is associated with a rural locality (such as a village, hamlet, or countryside settlement) within its scope or jurisdiction.
-
D.
hasRuralHinterland
Indicates that a place or urban area is associated with and served by a surrounding rural region that supports it economically, socially, or functionally.
-
E.
hasRuralAreaShare
Indicates the proportion of an entity’s total area or population that is classified as rural.
- F. None of above. chosen
Provenance (4 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_69e0b51d8a308190b09113b3b3f9bc15 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b06ec4208190b9ed3484afd85852 |
completed | April 22, 2026, 11:26 a.m. |
| PD | Predicate disambiguation | batch_69e6162bbfc88190a3e75859941b2638 |
completed | April 20, 2026, 12:03 p.m. |
| PDg | Predicate description generation | batch_69e61b3e47f881908fb2aac9bd2bfb58 |
completed | April 20, 2026, 12:25 p.m. |
Created at: April 16, 2026, 5:09 p.m.