Triple

T1547892
Position Surface form Disambiguated ID Type / Status
Subject Greater Poland E33019 entity
Predicate containsCity P294 FINISHED
Object Kalisz E133882 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: Kalisz | Statement: [Greater Poland, containsCity, Kalisz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kalisz
Context triple: [Greater Poland, containsCity, Kalisz]
  • A. Kalisz chosen
    Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
  • B. Kielce
    Kielce is a city in south-central Poland known as an important regional center for industry, education, and culture.
  • C. Bielsko-Biała
    Bielsko-Biała is a city in southern Poland at the foot of the Beskid Mountains, known as a regional industrial and cultural center formed from the historic towns of Bielsko and Biała.
  • D. Tarnów
    Tarnów is a historic city in southern Poland known for its well-preserved Old Town, Renaissance architecture, and cultural heritage.
  • E. Opole
    Opole is a historic city in southwestern Poland, known as one of the country’s oldest urban centers and a regional cultural hub.
  • 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_69a885ee6db8819099502bc5ce8af881 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abb20dd5a88190b3d6e6f0004fe9b4 completed March 7, 2026, 5:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69b2e7f3336c8190b0a9cd6f932bd3e0 completed March 12, 2026, 4:21 p.m.
Created at: March 4, 2026, 7:26 p.m.