Triple
T28799702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Springfield, West Virginia |
E727196
|
entity |
| Predicate | hasNearbyLargerSettlement |
P42873
|
FINISHED |
| Object | Romney, West Virginia |
—
|
NE NERFINISHED |
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: Romney, West Virginia | Statement: [Springfield, West Virginia, hasNearbyLargerSettlement, Romney, West Virginia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyLargerSettlement Context triple: [Springfield, West Virginia, hasNearbyLargerSettlement, Romney, West Virginia]
-
A.
hasNearestLargerSettlement
chosen
Indicates that one settlement is associated with the geographically closest settlement that is larger in size or population.
-
B.
hasNearbyTown
Indicates that one location has a town situated close to it in geographic proximity.
-
C.
hasNearbySettlementDensity
Indicates that an entity is associated with a concentration of settlements located within a nearby surrounding area.
-
D.
hasNearbyTownType
Indicates that one entity has, in its vicinity, a town of a specified type or classification.
-
E.
hasMunicipalitySeatNearby
Indicates that the municipality’s administrative seat is located in close proximity to the referenced place or entity.
- 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_69f0319b7c44819085736bcc256185e6 |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_69fdb45537288190b6791078d4a6899f |
completed | May 8, 2026, 10 a.m. |
| PD | Predicate disambiguation | batch_69fdb39ad96481908376d7def9fafc13 |
completed | May 8, 2026, 9:57 a.m. |
Created at: April 28, 2026, 6:26 a.m.