Triple
T8430492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rose DeWitt Bukater |
E199102
|
entity |
| Predicate | ticketClassOnRMSTitanic |
P37972
|
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: [Rose DeWitt Bukater, ticketClassOnRMSTitanic, first class]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ticketClassOnRMSTitanic Context triple: [Rose DeWitt Bukater, ticketClassOnRMSTitanic, first class]
-
A.
ticketClass
chosen
Indicates the category or level of service assigned to a ticket within a ticketing or reservation system.
-
B.
ticketClassSystem
Indicates that an entity is classified within a particular ticketing or fare class system that defines categories or levels of tickets.
-
C.
seatClass
Indicates the travel or seating category assigned to a passenger or seat (e.g., economy, business, first class).
-
D.
ticketTypeExample
Indicates that an entity serves as an example or illustrative instance of a particular ticket type.
-
E.
classesOfSeats
Indicates the different categories or types of seats associated with something, such as a venue, vehicle, or event.
- 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_69ca8313c99081909a5c6d83b91de5b3 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe30fba4081908bfdef3faf5baceb |
completed | March 31, 2026, 3:06 p.m. |
| PD | Predicate disambiguation | batch_69cbd0ec200c8190b0299e2b0b4bdcc2 |
completed | March 31, 2026, 1:49 p.m. |
Created at: March 30, 2026, 6:07 p.m.