Triple

T7670807
Position Surface form Disambiguated ID Type / Status
Subject Upper Silesian metropolitan area E173741 entity
Predicate hasMajorCity P316 FINISHED
Object Rybnik E351435 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: Rybnik | Statement: [Upper Silesian metropolitan area, hasMajorCity, Rybnik]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rybnik
Context triple: [Upper Silesian metropolitan area, hasMajorCity, Rybnik]
  • A. Rybnik chosen
    Rybnik is a significant industrial and cultural city in the Silesian region of southern Poland, known for its coal mining heritage and regional economic importance.
  • B. Dąbie
    Dąbie is a small town in central Poland, located in the Łódź Voivodeship along the Ner River.
  • C. Skawina
    Skawina is a town in southern Poland near Kraków, known for its industrial facilities and role as a local economic and transport hub.
  • D. Bóbrka
    Bóbrka is a village in southeastern Poland historically significant as one of the birthplaces of the modern oil industry.
  • E. Rybi Potok
    Rybi Potok is a mountain stream in the Tatra Mountains of southern Poland that drains the waters of the popular alpine lake Morskie Oko.
  • 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_69c699562484819086752091e3164a27 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c701dd3c808190990e07ced94b3297 completed March 27, 2026, 10:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8a229dd348190b9a781b4d34d7b5b completed March 29, 2026, 3:53 a.m.
Created at: March 27, 2026, 4 p.m.