Triple

T40804
Position Surface form Disambiguated ID Type / Status
Subject Angela Merkel E805 entity
Predicate familyName P18 FINISHED
Object Merkel E805 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: Merkel | Statement: [Angela Merkel, familyName, Merkel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Merkel
Context triple: [Angela Merkel, familyName, Merkel]
  • A. Angela Merkel chosen
    Angela Merkel is a German politician who served as Chancellor of Germany from 2005 to 2021 and became one of the most influential leaders in Europe and the world.
  • B. Mark Rutte
    Mark Rutte is a Dutch liberal politician who served for many years as the Netherlands’ longest-tenured prime minister and leader of the People’s Party for Freedom and Democracy (VVD).
  • C. Margaret Thatcher
    Margaret Thatcher was the United Kingdom’s first female prime minister, known for her conservative economic policies, strong anti-communist stance, and transformative but divisive leadership during the 1980s.
  • D. Olaf Kölzig
    Olaf Kölzig is a former German-Canadian NHL goaltender best known for his long, standout career with the Washington Capitals, including winning the Vezina Trophy in 2000.
  • E. Theodor
    Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
  • 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_69a247a8f6c08190bac804906d62ed5a completed Feb. 28, 2026, 1:40 a.m.
NER Named-entity recognition batch_69a24ae0b7c4819092220568e8e52ad5 completed Feb. 28, 2026, 1:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69a255338b6c8190bb31101691ce689a completed Feb. 28, 2026, 2:38 a.m.
Created at: Feb. 28, 2026, 1:46 a.m.