Triple
T1086700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Illinois's 5th congressional district |
E24068
|
entity |
| Predicate | urbanRuralCharacter |
P17246
|
FINISHED |
| Object | predominantly 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: predominantly urban | Statement: [Illinois's 5th congressional district, urbanRuralCharacter, predominantly urban]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: urbanRuralCharacter Context triple: [Illinois's 5th congressional district, urbanRuralCharacter, predominantly urban]
-
A.
hasSuburbanCharacter
Indicates that something possesses qualities or features typically associated with suburban areas, such as lower density, residential focus, and car-oriented development.
-
B.
isRural
Indicates that something is located in, characteristic of, or associated with a countryside or non-urban area.
-
C.
hasRuralArea
Indicates that an entity includes, is associated with, or contains a countryside or sparsely populated geographic area.
-
D.
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.
-
E.
isRuralCounty
Indicates that a given county is classified as rural rather than urban based on demographic, geographic, or administrative criteria.
- 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_69a49404428c819092dcc9632f5f7b8b |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b963161081908a523c8d63871652 |
completed | March 1, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69a4b7407914819092ed933a7316b450 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.