Triple
T2254834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SeatGeek Stadium |
E49696
|
entity |
| Predicate | sponsor |
P67
|
FINISHED |
| Object | SeatGeek |
E248994
|
NE 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: SeatGeek | Statement: [SeatGeek Stadium, sponsor, SeatGeek]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SeatGeek Context triple: [SeatGeek Stadium, sponsor, SeatGeek]
-
A.
SeatGeek
chosen
SeatGeek is a mobile-focused ticketing platform and marketplace for live sports, concerts, and entertainment events.
-
B.
StubHub (former)
StubHub (former) is an online ticket marketplace that was previously owned by eBay and used for buying and selling event tickets.
-
C.
CheapTickets
CheapTickets is an online travel agency brand offering discounted flights, hotels, and vacation packages, operated under the Expedia Group portfolio.
-
D.
MTA eTix
MTA eTix is a mobile ticketing application that lets riders purchase and display Long Island Rail Road and Metro-North Railroad tickets on their smartphones.
-
E.
CharlieTicket
CharlieTicket is a reusable paper smart card used for paying fares on Boston’s MBTA public transit system.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a88aaa9250819095e127d0d77e8a32 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc121af78819085b2e601d2f9bcdf |
completed | March 7, 2026, 6:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae71c487908190903e06bcb2393484 |
completed | March 9, 2026, 7:07 a.m. |
Created at: March 4, 2026, 7:47 p.m.