Triple
T11919440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LoanMart Field |
E283615
|
entity |
| Predicate | hasOnSiteTicketOffice |
P41426
|
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: [LoanMart Field, hasOnSiteTicketOffice, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOnSiteTicketOffice Context triple: [LoanMart Field, hasOnSiteTicketOffice, yes]
-
A.
hasTicketBooths
Indicates that one entity possesses or contains ticket booths used for selling or distributing tickets.
-
B.
hasBookingOffice
Indicates that one entity maintains or is associated with a booking office where reservations or ticketing services are handled for it.
-
C.
hasTicketHall
Indicates that a place or facility includes or is equipped with a designated ticket hall area for purchasing or validating tickets.
-
D.
ticketingLocation
Indicates the place or point where tickets are issued, sold, or otherwise processed for an event, service, or journey.
-
E.
hasStaffedTicketOffice
chosen
Indicates that a location or facility has a ticket office that is staffed by personnel.
- 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_69d6ab2c07e88190ba13b0d21fd6cf33 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8e8dff77481908cacf6ad03df34ac |
completed | April 10, 2026, 12:11 p.m. |
| PD | Predicate disambiguation | batch_69d8bb3632ac8190b13e53c2b5db7125 |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:44 p.m.