Triple

T11902727
Position Surface form Disambiguated ID Type / Status
Subject Walter Lassally E283194 entity
Predicate placeOfBirth P1 FINISHED
Object Berlin, Germany E5567 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: Berlin, Germany | Statement: [Walter Lassally, placeOfBirth, Berlin, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Berlin, Germany
Context triple: [Walter Lassally, placeOfBirth, Berlin, Germany]
  • A. Berlin
    Berlin is a major Ethereum network upgrade that introduced various gas cost optimizations and transaction processing improvements to enhance the blockchain’s efficiency and performance.
  • B. Berlin
    Berlin is a borough in Camden County, New Jersey, known as a suburban community within the Philadelphia metropolitan area.
  • C. Berlin chosen
    Berlin is the capital and largest city of Germany, historically significant as a focal point of Cold War tensions and a major cultural, political, and economic center in Europe.
  • D. Berlin
    Berlin is a charismatic, calculating, and morally ambiguous mastermind and heist leader in the Spanish television series "Money Heist" (La Casa de Papel).
  • E. Berlin AB
    Berlin AB is the central fare zone of Berlin’s public transport network, covering the inner city and surrounding urban areas served by the Verkehrsverbund Berlin-Brandenburg (VBB).
  • 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_69d6ab2c07e88190ba13b0d21fd6cf33 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8dd1792648190853f15fbf217eebd completed April 10, 2026, 11:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69f4587d1e548190b741a11d2ef63595 completed May 1, 2026, 7:38 a.m.
Created at: April 8, 2026, 9:44 p.m.