Triple
T5176166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hancock County, West Virginia |
E116803
|
entity |
| Predicate | areaRankInStateByLandArea |
P62180
|
FINISHED |
| Object | smallest |
—
|
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: smallest | Statement: [Hancock County, West Virginia, areaRankInStateByLandArea, smallest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaRankInStateByLandArea Context triple: [Hancock County, West Virginia, areaRankInStateByLandArea, smallest]
-
A.
rankInWorldByArea
Indicates the position of an entity in a global ordering based on its total area size.
-
B.
largestStateByArea
Indicates that a state is the one with the greatest land area within a specified set or region.
-
C.
continentRankByArea
Indicates the relative position of a continent in an ordered list based on its total land area.
-
D.
landArea
Indicates the total surface area of a piece of land associated with an entity, typically measured in standardized units (e.g., square meters, hectares).
-
E.
areaRankingInContiguousUS
Indicates the relative position of an entity when U.S. states are ordered by area, considering only those in the contiguous United States.
- 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_69bd446140f08190becb93c61158f27f |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd797349008190b87ad9d0d3eb667f |
completed | March 20, 2026, 4:44 p.m. |
| PD | Predicate disambiguation | batch_69bd77b529948190b86671ebe43f4734 |
completed | March 20, 2026, 4:37 p.m. |
| PDg | Predicate description generation | batch_69bd79251b548190918a1eb930e24c22 |
completed | March 20, 2026, 4:43 p.m. |
Created at: March 20, 2026, 1:45 p.m.