Triple

T21388547
Position Surface form Disambiguated ID Type / Status
Subject Anna Maria Franziska of Saxe-Lauenburg E527575 entity
Predicate deathPlace P21 FINISHED
Object Zákupy 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: Zákupy | Statement: [Anna Maria Franziska of Saxe-Lauenburg, deathPlace, Zákupy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zákupy
Context triple: [Anna Maria Franziska of Saxe-Lauenburg, deathPlace, Zákupy]
  • A. Zákupy chosen
    Zákupy is a small historic town in the Liberec Region of the Czech Republic, known for its Renaissance-Baroque chateau and its association with the Habsburg dynasty.
  • B. Pazarlar
    Pazarlar is a small town and district in western Turkey known for its rural character and location within Kütahya Province.
  • C. Cine Bazar
    Cine Bazar is a Japanese film production company known for its involvement in major genre films such as the 2016 kaiju movie "Shin Godzilla."
  • D. Votroci
    Votroci is the popular nickname of FC Hradec Králové, a Czech professional football club based in the city of Hradec Králové.
  • E. Trhové Sviny
    Trhové Sviny is a small historic town in the South Bohemian Region of the Czech Republic.
  • 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_69e0b51ff3748190935c0a513c62a12b completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e8b0f6ce4c81909da915139610a2a7 completed April 22, 2026, 11:28 a.m.
Created at: April 16, 2026, 5:12 p.m.