Triple

T12715662
Position Surface form Disambiguated ID Type / Status
Subject Asma al-Assad E303831 entity
Predicate employer P7 FINISHED
Object Deutsche Bank E373967 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: Deutsche Bank | Statement: [Asma al-Assad, employer, Deutsche Bank]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Deutsche Bank
Context triple: [Asma al-Assad, employer, Deutsche Bank]
  • A. Deutsche Bank chosen
    Deutsche Bank is a major global investment bank and financial services company headquartered in Frankfurt, Germany.
  • B. Commerzbank AG
    Commerzbank AG is a major German commercial bank headquartered in Frankfurt, known as one of the country’s leading financial institutions serving corporate, institutional, and retail clients.
  • C. Dresdner Bank
    Dresdner Bank was one of Germany’s major commercial banks, historically influential in the country’s financial and industrial development.
  • D. Deutsche Rentenbank
    Deutsche Rentenbank was the German financial institution established in 1923 to stabilize the currency and restore confidence during the hyperinflation crisis of the Weimar Republic.
  • E. Citigroup
    Citigroup is a major American multinational investment bank and financial services corporation headquartered in New York City.
  • 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_69d7bdf084148190ab9d513dc0735af4 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d9620bd6148190a2f50067a4c18c14 completed April 10, 2026, 8:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f69b8a79488190aaf95d4f2e20a7bc completed May 3, 2026, 12:49 a.m.
Created at: April 9, 2026, 5:23 p.m.