Triple
T36699679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SS Empress of Canada (2036) |
E906189
|
entity |
| Predicate | hasPlannedPassengerService |
P175201
|
FINISHED |
| Object | long-distance voyages |
—
|
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: long-distance voyages | Statement: [SS Empress of Canada (2036), hasPlannedPassengerService, long-distance voyages]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPlannedPassengerService Context triple: [SS Empress of Canada (2036), hasPlannedPassengerService, long-distance voyages]
-
A.
hasPassengerServicesType
chosen
Indicates the type or category of passenger services that are provided or associated with an entity.
-
B.
hasPassengerServicesTo
Indicates that a transportation provider operates passenger services connecting one location or entity to another.
-
C.
hasPassengerAirlineService
Indicates that a location or facility is served by scheduled passenger airline flights.
-
D.
isInPassengerService
Indicates that an entity (such as a vehicle, vessel, or aircraft) is currently being used to carry passengers as part of regular service.
-
E.
hasPassengerServiceLevel
Indicates the level or quality of passenger service provided in a given transportation context.
- 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_69f76e7195c48190b5580c9cfb01e95f |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fe08d2b2e48190ac7be6d62d4a44a3 |
completed | May 8, 2026, 4:01 p.m. |
| PD | Predicate disambiguation | batch_69fe06cd3af08190ae25de0dc0cdd573 |
completed | May 8, 2026, 3:52 p.m. |
Created at: May 3, 2026, 4:12 p.m.