Triple
T10112591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northern Panhandle of West Virginia |
E218276
|
entity |
| Predicate | hasHigherPopulationDensityThan |
P92257
|
FINISHED |
| Object | many rural regions of West Virginia |
—
|
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: many rural regions of West Virginia | Statement: [Northern Panhandle of West Virginia, hasHigherPopulationDensityThan, many rural regions of West Virginia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHigherPopulationDensityThan Context triple: [Northern Panhandle of West Virginia, hasHigherPopulationDensityThan, many rural regions of West Virginia]
-
A.
hasPopulationDensity
Indicates the number of individuals (e.g., people, organisms) per unit area associated with a given entity or region.
-
B.
hasPopulationOver
Indicates that one entity has a population greater than a specified number or than another entity.
-
C.
isDenselyPopulated
Indicates that a place has a high concentration of inhabitants relative to its area.
-
D.
populationDensity
Indicates the number of individuals or entities occupying a unit area within a given region.
-
E.
hasHighestUrbanizationRateIn
Indicates that the subject has the greatest proportion of its population living in urban areas compared to all other entities within the specified object region or group.
- 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_69ca83da93fc8190b54e44bc2b34857c |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cdd15ed28081909a812e0634799ef8 |
completed | April 2, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69cd4b9ed7e48190aa132ef8a69b49f9 |
completed | April 1, 2026, 4:45 p.m. |
| PDg | Predicate description generation | batch_69cd4f8f869c8190a82ad040993e0244 |
completed | April 1, 2026, 5:02 p.m. |
Created at: March 30, 2026, 9:03 p.m.