Triple
T3645196
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Danville, Virginia |
E77281
|
entity |
| Predicate | hasPopulationRankInVirginia |
P3907
|
FINISHED |
| Object | small to mid-sized city |
—
|
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: small to mid-sized city | Statement: [Danville, Virginia, hasPopulationRankInVirginia, small to mid-sized city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPopulationRankInVirginia Context triple: [Danville, Virginia, hasPopulationRankInVirginia, small to mid-sized city]
-
A.
populationRankInVirginia
chosen
Indicates the relative position of an entity in terms of population size compared to other entities within Virginia.
-
B.
populationRankInMaryland
Indicates the relative position of an entity in terms of its population size compared to other entities within the state of Maryland.
-
C.
populationDensityRankInUS
Indicates the relative position of a place in a ranking of U.S. locations ordered by population density.
-
D.
rankByPopulationInUnitedStates
Indicates the relative ordering of entities based on their population size within the United States.
-
E.
rankByPopulationInUS
Indicates the relative ordering of entities based on the size of their populations within the United States.
- 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_69ad85de1b988190a45f8dbfebc806fc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc35da84c81908950de92ba171fa3 |
completed | March 8, 2026, 6:43 p.m. |
| PD | Predicate disambiguation | batch_69adb8445b2c8190ab07f6ad4e010d0e |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:24 p.m.