Triple

T3726821
Position Surface form Disambiguated ID Type / Status
Subject bj league E81768 entity
Predicate ticketRevenueModel P51269 FINISHED
Object club-based professional model 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: club-based professional model | Statement: [bj league, ticketRevenueModel, club-based professional model]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: ticketRevenueModel
Context triple: [bj league, ticketRevenueModel, club-based professional model]
  • A. 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).
  • B. ticket
    Indicates that an entity serves as or is associated with a ticket, typically representing authorization, access, or a record for an event, service, or transaction.
  • C. ticketingCode
    Indicates the specific fare or booking code associated with a ticket that defines its pricing, rules, and conditions of use.
  • D. ticketDemand
    Indicates that there is a level of desire or need among potential buyers for tickets to an event, service, or offering.
  • E. ticketingCompatibleWith
    Indicates that two systems, services, or components can interoperate or be used together within the same ticketing or reservation workflow without conflict.
  • 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_69ad8b1b7ef081908d2d381bbf54985a completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcaf7a6908190bd0c3bb5c55ab9ee completed March 8, 2026, 7:16 p.m.
PD Predicate disambiguation batch_69adc0452f5081909c79e114a86cce8c completed March 8, 2026, 6:30 p.m.
PDg Predicate description generation batch_69adc226fffc81909c679b44e611fee6 completed March 8, 2026, 6:38 p.m.
Created at: March 8, 2026, 3:34 p.m.