Triple

T18807531
Position Surface form Disambiguated ID Type / Status
Subject World Economic Forum E459917 entity
Predicate hasFormerName P65 FINISHED
Object EMF 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: EMF | Statement: [World Economic Forum, hasFormerName, EMF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: EMF
Context triple: [World Economic Forum, hasFormerName, EMF]
  • A. EMF chosen
    EMF is the former name of the World Economic Forum, an international organization that engages political, business, and other leaders to shape global, regional, and industry agendas.
  • B. EMI
    EMI was a major British music company and record label group known for signing and distributing many of the world’s leading recording artists.
  • C. EMI
    EMI was the European Monetary Institute, a precursor institution to the European Central Bank that helped prepare for the introduction of the euro and the coordination of European monetary policy.
  • D. EMM
    EMM is a peer-reviewed scientific journal focusing on translational and molecular medicine research.
  • E. EFM
    EFM is a major international film industry marketplace held annually alongside the Berlin International Film Festival, where producers, distributors, and buyers trade and promote films and audiovisual content.
  • 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_69d8d398c7d4819091cb2f7e48948aeb completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5a3d8ab9c819097834eac798ce810 completed April 20, 2026, 3:56 a.m.
Created at: April 10, 2026, 11:53 a.m.