Triple
T2738959
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas's 33rd congressional district |
E60701
|
entity |
| Predicate | urbanRuralClassification |
P40854
|
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: [Texas's 33rd congressional district, urbanRuralClassification, predominantly urban]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: urbanRuralClassification Context triple: [Texas's 33rd congressional district, urbanRuralClassification, predominantly urban]
-
A.
urbanRuralSplit
Indicates a division or distinction between urban and rural areas, conditions, or populations.
-
B.
hasUrbanClassification
chosen
Indicates that an entity is assigned a specific urban status or category within a defined classification system.
-
C.
isRural
Indicates that something is located in, characteristic of, or associated with a countryside or non-urban area.
-
D.
isUrbanized
Indicates that a place or area has been developed with dense human settlement, infrastructure, and built environment characteristic of a city or town.
-
E.
isRuralSettlement
Indicates that a settlement is located in a rural area, typically characterized by low population density and limited urban infrastructure.
- 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_69ab4b77febc819095603eb012cd141b |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb147a588190829b74fe05b3a114 |
completed | March 7, 2026, 8 a.m. |
| PD | Predicate disambiguation | batch_69abd82859348190bce3be8f2e9d60ba |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:56 p.m.