Triple

T6632336
Position Surface form Disambiguated ID Type / Status
Subject Chernihiv E149955 entity
Predicate hasSisterCity P919 FINISHED
Object Tarnów E23368 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: Tarnów | Statement: [Chernihiv, hasSisterCity, Tarnów]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tarnów
Context triple: [Chernihiv, hasSisterCity, Tarnów]
  • A. Tarnów chosen
    Tarnów is a historic city in southern Poland known for its well-preserved Old Town, Renaissance architecture, and cultural heritage.
  • B. Przemyśl
    Przemyśl is a historic city in southeastern Poland near the Ukrainian border, known for its strategic location, multicultural heritage, and well-preserved fortifications.
  • C. Kielce
    Kielce is a city in south-central Poland known as an important regional center for industry, education, and culture.
  • D. Kalisz
    Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
  • E. Hrubieszów
    Hrubieszów is a historic town in eastern Poland near the Ukrainian border, known for its multicultural heritage and location in the Lublin region.
  • 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_69c687ee50048190aa151765bef16193 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6afc9138c81909d228ce4936d6b8b completed March 27, 2026, 4:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69d076fe8448819087e12ffe4d5bdf3c completed April 4, 2026, 2:27 a.m.
Created at: March 27, 2026, 1:59 p.m.