Triple
T13575432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marion County, Texas |
E324268
|
entity |
| Predicate | isSmallPopulationCounty |
P98800
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Marion County, Texas, isSmallPopulationCounty, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isSmallPopulationCounty Context triple: [Marion County, Texas, isSmallPopulationCounty, true]
-
A.
isSmallCountyByPopulation
chosen
Indicates that a county has a relatively low number of residents compared to other counties, classifying it as small by population.
-
B.
isSmallCountyByArea
Indicates that a county is classified as small based on the size of its geographic area.
-
C.
hasVerySmallResidentPopulation
Indicates that the subject location has a resident population that is extremely small in size.
-
D.
isSmallDistrict
Indicates that a district is classified as small, typically based on its limited size, population, or administrative scope.
-
E.
mostPopulousCountyIn
Indicates that the subject is the county with the largest population within the specified object region or jurisdiction.
- 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_69d80769100c819099111274614f5ed2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb02b1f108190a12af382d1de70bb |
completed | April 12, 2026, 2:46 p.m. |
| PD | Predicate disambiguation | batch_69dbae161a0481909f9d3f40ca4e0ac5 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:48 p.m.