Triple
T35494243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SS Empress of Britain (2070) |
E1025811
|
entity |
| Predicate | passengerClassConfiguration |
P50616
|
FINISHED |
| Object | first class |
—
|
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: first class | Statement: [SS Empress of Britain (2070), passengerClassConfiguration, first class]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerClassConfiguration Context triple: [SS Empress of Britain (2070), passengerClassConfiguration, first class]
-
A.
passengerConfiguration
Indicates how passengers are arranged, seated, or distributed within a vehicle or transport setting.
-
B.
passengerLevel
Indicates the relative status, class, or priority assigned to a passenger within a transportation or service context.
-
C.
seatClass
chosen
Indicates the travel or seating category assigned to a passenger or seat (e.g., economy, business, first class).
-
D.
appliesToPassengerType
Indicates that a rule, condition, or attribute is relevant or restricted to a specific type or category of passenger.
-
E.
airlineClass
Indicates the specific travel class or service level assigned to a passenger or ticket on an airline flight.
- 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_69f76dfc9c60819089c4217d93922615 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a225a77c81908f8953ccfeb14336 |
completed | May 3, 2026, 7:29 p.m. |
| PD | Predicate disambiguation | batch_69f7a06d4f108190bae3ab9ae431d2c7 |
completed | May 3, 2026, 7:22 p.m. |
Created at: May 3, 2026, 4:04 p.m.