Triple
T2637426
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas's 30th congressional district |
E59779
|
entity |
| Predicate | urbanRuralComposition |
P17246
|
FINISHED |
| Object | overwhelmingly urban |
—
|
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: overwhelmingly urban | Statement: [Texas's 30th congressional district, urbanRuralComposition, overwhelmingly urban]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: urbanRuralComposition Context triple: [Texas's 30th congressional district, urbanRuralComposition, overwhelmingly urban]
-
A.
urbanRuralSplit
Indicates a division or distinction between urban and rural areas, conditions, or populations.
-
B.
hasUrbanRuralMix
Indicates that something exhibits a combination or blend of both urban and rural characteristics or components.
-
C.
urbanizationLevel
Indicates the degree to which an area or population is characterized by urban development, infrastructure, and density of human settlement.
-
D.
statusInUrbanAreas
Indicates the condition, prevalence, or situation of something specifically within urban areas.
-
E.
isUrbanized
chosen
Indicates that a place or area has been developed with dense human settlement, infrastructure, and built environment characteristic of a city or town.
- 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_69ab4ac8596c8190b34997e73d9e991c |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd8e3190081908ea828fe79569cc9 |
completed | March 7, 2026, 7:50 a.m. |
| PD | Predicate disambiguation | batch_69abd812849881908f956845a80e0205 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:50 p.m.