Triple
T6948797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North America’s Railroad |
E160867
|
entity |
| Predicate | impliesCoverage |
P74240
|
FINISHED |
| Object | Canada-wide rail network |
—
|
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: Canada-wide rail network | Statement: [North America’s Railroad, impliesCoverage, Canada-wide rail network]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impliesCoverage Context triple: [North America’s Railroad, impliesCoverage, Canada-wide rail network]
-
A.
isCoveredBy
Indicates that one entity is physically or conceptually overlaid, protected, or enclosed by another entity.
-
B.
hasCoverage
Indicates that one entity provides insurance or protection coverage for another entity or subject.
-
C.
isCoveredIn
Indicates that one entity has its surface or area overlaid, coated, or blanketed by another substance or material.
-
D.
providesCoverage
Indicates that one entity supplies protection, insurance, or service coverage to another entity or for a specified risk or scope.
-
E.
hasCoverageFocus
Indicates that one entity’s coverage, attention, or analysis is specifically focused on or directed toward 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_69c68850419081909fb426b8f5a304c7 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dacca12481908942ba793a104cc3 |
completed | March 27, 2026, 7:30 p.m. |
| PD | Predicate disambiguation | batch_69c6d7bf0a7c8190b5ed4aca22ba9b97 |
completed | March 27, 2026, 7:17 p.m. |
| PDg | Predicate description generation | batch_69c6dacb524c81909aade2282a4c0c01 |
completed | March 27, 2026, 7:30 p.m. |
Created at: March 27, 2026, 2:28 p.m.