Triple
T6411172
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amtrak Acela |
E127707
|
entity |
| Predicate | ticketingClass |
P37972
|
FINISHED |
| Object | Business |
—
|
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: Business | Statement: [Amtrak Acela, ticketingClass, Business]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ticketingClass Context triple: [Amtrak Acela, ticketingClass, Business]
-
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.
ticketingCode
Indicates the specific fare or booking code associated with a ticket that defines its pricing, rules, and conditions of use.
-
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_69c0083723d88190b1e37b19df162c08 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c068d0f2b88190af06c208d8c01d07 |
completed | March 22, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69c060f40ecc8190b1df17b96767675c |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:41 p.m.