Triple

T710915
Position Surface form Disambiguated ID Type / Status
Subject Sinews of Peace E14203 entity
Predicate mentions P831 FINISHED
Object Berlin 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 | Statement: [Sinews of Peace, mentions, Berlin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Berlin
Context triple: [Sinews of Peace, mentions, Berlin]
  • A. 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.
  • B. West Berlin
    West Berlin was the Western-aligned, enclave-like portion of Berlin surrounded by East Germany during the Cold War, symbolizing resistance to Soviet pressure and the division of Germany.
  • C. East Berlin
    East Berlin was the Soviet-controlled eastern sector of Berlin that served as the capital of East Germany during the Cold War.
  • D. Leipzig
    Leipzig is a major city in eastern Germany known for its rich cultural heritage, vibrant music and arts scene, and important role in trade and commerce.
  • E. Frankfurt am Main
    Frankfurt am Main is a major German financial and transportation hub on the River Main, known for hosting the European Central Bank and one of Europe’s busiest airports.
  • 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_69a4934a36e081909e7abef98b898a4e completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a55c99fc8190941c5fd18551792a completed March 1, 2026, 8:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69a6732881488190a6f6eb556a42277c completed March 3, 2026, 5:35 a.m.
Created at: March 1, 2026, 7:36 p.m.