Triple
T37034812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SS Empress of Britain (1989) |
E916601
|
entity |
| Predicate | hasPassengerCapacityCategory |
P39872
|
FINISHED |
| Object | large passenger ship |
—
|
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: large passenger ship | Statement: [SS Empress of Britain (1989), hasPassengerCapacityCategory, large passenger ship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerCapacityCategory Context triple: [SS Empress of Britain (1989), hasPassengerCapacityCategory, large passenger ship]
-
A.
passengerCapacityCategory
chosen
Indicates the classification of an entity based on the number of passengers it is designed or allowed to carry.
-
B.
hasLimitedPassengerCapacity
Indicates that an entity’s ability to carry passengers is restricted to a maximum number or range.
-
C.
maximumPassengerCapacity
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
-
D.
hasSeatingCapacityCategory
Indicates the classification of an entity based on the range or category of how many people it can seat.
-
E.
hasPassengerArea
Indicates that an object or vehicle includes a designated area intended for carrying passengers.
- 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_69f76e92c7648190bcfa277f64c71a21 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a00332eaa0c8190a69ea895576bb0ee |
completed | May 10, 2026, 7:26 a.m. |
| PD | Predicate disambiguation | batch_6a0032b2ea80819083b89ebb88165933 |
completed | May 10, 2026, 7:24 a.m. |
Created at: May 3, 2026, 4:14 p.m.