Triple

T6984430
Position Surface form Disambiguated ID Type / Status
Subject Cassino E161926 entity
Predicate hasTwinTown P919 FINISHED
Object Czestochowa E128038 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: Czestochowa | Statement: [Cassino, hasTwinTown, Czestochowa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Czestochowa
Context triple: [Cassino, hasTwinTown, Czestochowa]
  • 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. Kalisz
    Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
  • C. Katowice
    Katowice is a major industrial and cultural city in southern Poland, known as the capital of the Silesian region.
  • D. Wrocław
    Wrocław is a major historic city in southwestern Poland, known for its picturesque Old Town, numerous bridges over the Oder River, and role as a cultural and academic center.
  • E. Kielce
    Kielce is a city in south-central Poland known as an important regional center for industry, education, and culture.
  • 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_69c68855dc0481909b4c7e9e9ed273db completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6db90e9108190a7aedeef1fb17eb4 completed March 27, 2026, 7:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1d583f75c819083cb0dcdf265d735 completed April 5, 2026, 3:22 a.m.
Created at: March 27, 2026, 2:31 p.m.