Triple
T13497260
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dayton Dragons |
E320793
|
entity |
| Predicate | ticketSalesReputation |
P110660
|
FINISHED |
| Object | strong attendance and ticket demand |
—
|
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: strong attendance and ticket demand | Statement: [Dayton Dragons, ticketSalesReputation, strong attendance and ticket demand]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ticketSalesReputation Context triple: [Dayton Dragons, ticketSalesReputation, strong attendance and ticket demand]
-
A.
ticketTypeSold
Indicates that a specific type of ticket has been sold in a given transaction or context.
-
B.
ticketsSold
Indicates that a certain number of tickets have been purchased or distributed for a particular event or offering.
-
C.
ticketRevenueModel
Indicates the method or structure by which revenue is generated from ticket sales.
-
D.
ticketingScope
Indicates the range or domain within which ticketing actions (such as creation, assignment, or management of tickets) are valid or applicable.
-
E.
ticketTypeStored
Indicates that a particular type of ticket has been recorded and saved in a storage or system.
- F. None of above. chosen
Provenance (4 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_69d807629d6c8190998f1b9bb12d2ed0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf4e9ca4819083116890a65389f9 |
completed | April 12, 2026, 2:42 p.m. |
| PD | Predicate disambiguation | batch_69dbae06061881909a6a6032e0507587 |
completed | April 12, 2026, 2:36 p.m. |
| PDg | Predicate description generation | batch_69dbaecc98cc8190829f5be759c4f1e3 |
completed | April 12, 2026, 2:40 p.m. |
Created at: April 9, 2026, 9:43 p.m.