Triple

T21223272
Position Surface form Disambiguated ID Type / Status
Subject TIF E523022 entity
Predicate organizer P123 FINISHED
Object TIF-Helexpo NE NERFINISHED

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: TIF-Helexpo | Statement: [TIF, organizer, TIF-Helexpo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: TIF-Helexpo
Context triple: [TIF, organizer, TIF-Helexpo]
  • A. TIF HELEXPO
    TIF HELEXPO is Greece’s national exhibition and conference organization, best known for staging major trade fairs and events in Thessaloniki and across the country.
  • B. Helexpo chosen
    Helexpo is Greece’s national exhibition and conference organizer, best known for staging major trade fairs and events such as the Thessaloniki International Fair.
  • C. Palexpo
    Palexpo is a large convention and exhibition center in Geneva, Switzerland, known for hosting major international events such as the Geneva International Motor Show.
  • D. The Expo
    The Expo is a well-known multipurpose event and exhibition venue in Portland, Oregon, hosting trade shows, conventions, and community events.
  • E. Expo
    Expo is an open-source platform and toolchain for building, deploying, and iterating on React Native applications.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b512ad94819087942b2ed925185f completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e734a846d08190a5470298a3ddd0f8 completed April 21, 2026, 8:26 a.m.
Created at: April 16, 2026, 3:44 p.m.