Triple
T27825635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oregon Transportation Plan |
E702945
|
entity |
| Predicate | hasModeCoverage |
P99632
|
FINISHED |
| Object | multimodal transportation |
—
|
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: multimodal transportation | Statement: [Oregon Transportation Plan, hasModeCoverage, multimodal transportation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasModeCoverage Context triple: [Oregon Transportation Plan, hasModeCoverage, multimodal transportation]
-
A.
hasCoverage
Indicates that one entity provides insurance or protection coverage for another entity or subject.
-
B.
hasModeCategory
chosen
Indicates that something is associated with or classified under a particular mode category (e.g., type or manner of operation or behavior).
-
C.
hasKeyCoverage
Indicates that one entity’s key or set of keys provides coverage, access, or applicability over another entity or domain.
-
D.
hasCoverageFocus
Indicates that one entity’s coverage, attention, or analysis is specifically focused on or directed toward another entity.
-
E.
hasModeSystem
Indicates that one entity operates under, or is associated with, a particular mode defined or managed by another system.
- 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_69ef840ad1e88190b5bff2d1ddec8700 |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_69f74c70fd248190a9d5543afcb08211 |
completed | May 3, 2026, 1:24 p.m. |
| PD | Predicate disambiguation | batch_69f7478e3b548190a51d5d436e2bb036 |
completed | May 3, 2026, 1:03 p.m. |
Created at: April 27, 2026, 5:51 p.m.