Triple
T19514475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Interactive One |
E488241
|
entity |
| Predicate | hasAreaOfCoverage |
P136191
|
FINISHED |
| Object | United States urban markets |
—
|
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: United States urban markets | Statement: [Interactive One, hasAreaOfCoverage, United States urban markets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAreaOfCoverage Context triple: [Interactive One, hasAreaOfCoverage, United States urban markets]
-
A.
hasProtectedAreaCoverage
Indicates that a specified portion or extent of an area falls within officially designated protected areas.
-
B.
mayCoverArea
Indicates that one entity is permitted or able to extend over, include, or encompass a specified spatial area.
-
C.
hasCoverage
Indicates that one entity provides insurance or protection coverage for another entity or subject.
-
D.
providesCoverage
Indicates that one entity supplies protection, insurance, or service coverage to another entity or for a specified risk or scope.
-
E.
hasFrequencyCoverage
Indicates that one entity provides, supports, or is applicable across a specified range or set of frequencies associated with another entity.
- F. None of above. chosen
Provenance (4 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_69d8e8da8bec819081f400199491ccc3 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6359a7070819099d925447c80bf23 |
completed | April 20, 2026, 2:18 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7bd25881908caa04eaef1f6718 |
completed | April 19, 2026, 4:06 p.m. |
| PDg | Predicate description generation | batch_69e5004d3a708190a1c13c8f644f3926 |
completed | April 19, 2026, 4:18 p.m. |
Created at: April 10, 2026, 1:40 p.m.