Triple
T4772962
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wood County, West Virginia |
E105974
|
entity |
| Predicate | hasAreaLand |
P157
|
FINISHED |
| Object | approximately 366 square miles |
—
|
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: approximately 366 square miles | Statement: [Wood County, West Virginia, hasAreaLand, approximately 366 square miles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAreaLand Context triple: [Wood County, West Virginia, hasAreaLand, approximately 366 square miles]
-
A.
hasLandmarkArea
Indicates that a specified area is designated as the landmark area associated with a particular entity or location.
-
B.
landArea
chosen
Indicates the total surface area of a piece of land associated with an entity, typically measured in standardized units (e.g., square meters, hectares).
-
C.
hasLandStatus
Indicates that an entity possesses a particular legal or administrative status regarding land (such as ownership, tenure, protection, or use designation).
-
D.
hasNumberOfAcres
Indicates the specific quantity of land area, measured in acres, that is associated with an entity.
-
E.
hasAreaType
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_69bd43f226fc8190b867cc249c2a9042 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd655f98b0819088c05c5502ecf2cd |
completed | March 20, 2026, 3:18 p.m. |
| PD | Predicate disambiguation | batch_69bd6229d8448190a271719e5e30fd82 |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:21 p.m.