Triple

T16326764
Position Surface form Disambiguated ID Type / Status
Subject Raków Częstochowa E396439 entity
Predicate locationCity P3207 FINISHED
Object Częstochowa 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: Częstochowa | Statement: [Raków Częstochowa, locationCity, Częstochowa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Częstochowa
Context triple: [Raków Częstochowa, locationCity, Częstochowa]
  • A. Częstochowa chosen
    Częstochowa is a city in southern Poland best known as a major Catholic pilgrimage center, home to the Jasna Góra Monastery and the revered Black Madonna icon.
  • B. Kielce
    Kielce is a city in south-central Poland known as an important regional center for industry, education, and culture.
  • C. Kalisz
    Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
  • D. Tarnów
    Tarnów is a historic city in southern Poland known for its well-preserved Old Town, Renaissance architecture, and cultural heritage.
  • E. Lubawa
    Lubawa is a historic town in northern Poland known for its medieval heritage and location within the Warmian-Masurian region.
  • 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_69d87f255b788190a400eba031dd85d8 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e296bab8b48190b373b4efbd6f0d8c completed April 17, 2026, 8:23 p.m.
Created at: April 10, 2026, 5:06 a.m.