Triple

T7967957
Position Surface form Disambiguated ID Type / Status
Subject CIEF E185252 entity
Predicate alsoKnownAs P39 FINISHED
Object Canton Fair E35220 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: Canton Fair | Statement: [CIEF, alsoKnownAs, Canton Fair]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Canton Fair
Context triple: [CIEF, alsoKnownAs, Canton Fair]
  • A. Canton Fair chosen
    The Canton Fair is China’s largest and oldest trade fair, held biannually in Guangzhou and serving as a major global platform for importing and exporting a wide range of goods.
  • B. China–ASEAN Expo
    The China–ASEAN Expo is a major annual trade and investment fair that promotes economic cooperation and integration between China and the member states of the Association of Southeast Asian Nations.
  • C. Hannover Messe
    Hannover Messe is one of the world’s largest and most influential industrial technology trade fairs, held annually in Hanover, Germany.
  • D. Berliner Messe
    Berliner Messe is a minimalist sacred choral composition by Estonian composer Arvo Pärt, written in his signature tintinnabuli style for the Latin Mass.
  • E. Messe Düsseldorf
    Messe Düsseldorf is a major international trade fair and exhibition center in Düsseldorf, Germany, hosting numerous global industry events and conventions.
  • 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_69ca8297699481909b75a405f01e03af completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3bd06ee081908c5080003fb7b8f7 completed March 31, 2026, 3:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc566afad88190b53f228d836619de completed March 31, 2026, 11:19 p.m.
Created at: March 30, 2026, 5:13 p.m.