Triple
T4772961
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wood County, West Virginia |
E105974
|
entity |
| Predicate | hasAreaTotal |
P58710
|
FINISHED |
| Object | approximately 377 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 377 square miles | Statement: [Wood County, West Virginia, hasAreaTotal, approximately 377 square miles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAreaTotal Context triple: [Wood County, West Virginia, hasAreaTotal, approximately 377 square miles]
-
A.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
B.
hasAreaRange
Indicates that something’s area falls within a specified minimum-to-maximum range.
-
C.
hasBaseArea
Indicates that one entity has a base whose surface area is quantified or associated with another entity.
-
D.
hasCollectionArea
Indicates that an entity is associated with a specific geographic or spatial area from which items, specimens, or data are collected.
-
E.
hasFloorArea
Indicates that an entity possesses a specified amount of floor space as a measurable area.
- 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_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. |
| PDg | Predicate description generation | batch_69bd6508e218819086a36236cfa4a249 |
completed | March 20, 2026, 3:17 p.m. |
Created at: March 20, 2026, 1:21 p.m.