Triple
T28292858
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gilpin County, Colorado, United States |
E713473
|
entity |
| Predicate | hasAreaRankInColorado |
P150581
|
FINISHED |
| Object | second smallest by area |
—
|
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: second smallest by area | Statement: [Gilpin County, Colorado, United States, hasAreaRankInColorado, second smallest by area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAreaRankInColorado Context triple: [Gilpin County, Colorado, United States, hasAreaRankInColorado, second smallest by area]
-
A.
rankByPopulationInColorado
Indicates the relative ordering of entities based on their population size within the state of Colorado.
-
B.
hasRankByArea
chosen
Indicates that something is assigned a position or ranking based on its area size.
-
C.
areaRankingInContiguousUS
Indicates the relative position of an entity when U.S. states are ordered by area, considering only those in the contiguous United States.
-
D.
areaRankInUS
Indicates the relative position of an entity in a ranking of areas within the United States, based on its size.
-
E.
countyArea
Indicates the total geographic area covered by a county, typically measured in standard area units.
- 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_69efb52371d88190a1381c4e58a3b731 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f7308a096081909d66a56f3c926806 |
completed | May 3, 2026, 11:24 a.m. |
| PD | Predicate disambiguation | batch_69f72a00c5f081908b6539d15baf4e12 |
completed | May 3, 2026, 10:57 a.m. |
Created at: April 27, 2026, 11:30 p.m.