Triple
T8401728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Little Neck station |
E198387
|
entity |
| Predicate | ticketVendingMachines |
P61741
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Little Neck station, ticketVendingMachines, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ticketVendingMachines Context triple: [Little Neck station, ticketVendingMachines, yes]
-
A.
ticketMachines
chosen
Indicates that there is a relationship involving ticket machines, typically denoting where they are located, available, or associated with a particular entity or place.
-
B.
hasTicketBooths
Indicates that one entity possesses or contains ticket booths used for selling or distributing tickets.
-
C.
ticketingLocation
Indicates the place or point where tickets are issued, sold, or otherwise processed for an event, service, or journey.
-
D.
ticketingProduct
Indicates a relationship where an entity is associated with, or offered as, a ticketing-related product (such as a service or item used for issuing, managing, or selling tickets).
-
E.
sellsTicketsUnder
Indicates that one entity sells tickets at a price lower than or under the pricing of another entity.
- 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_69ca8310df9c8190b25f16161cca3e41 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb824da3148190bfa3a1abfdfa02de |
completed | March 31, 2026, 8:14 a.m. |
| PD | Predicate disambiguation | batch_69cb70d473dc8190af8ea81ee5aa970d |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:04 p.m.