Triple

T5360021
Position Surface form Disambiguated ID Type / Status
Subject Huta Stalowa Wola E102995 entity
Predicate namedAfter P63 FINISHED
Object Stalowa Wola E692490 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: Stalowa Wola | Statement: [Huta Stalowa Wola, namedAfter, Stalowa Wola]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Stalowa Wola
Context triple: [Huta Stalowa Wola, namedAfter, Stalowa Wola]
  • A. Stalowa Wola chosen
    Stalowa Wola is an industrial city in southeastern Poland, historically known as a major center of heavy industry and steel production.
  • B. Chorzów
    Chorzów is an industrial city in southern Poland’s Silesian region, known for its heavy industry heritage and the extensive Silesian Park.
  • C. Sosnowiec
    Sosnowiec is an industrial city in southern Poland, located in the Silesian Voivodeship and known as part of the Upper Silesian metropolitan area.
  • D. Tychy
    Tychy is a city in the Silesian region of southern Poland, known for its brewing industry and role as a planned industrial center.
  • E. Kociewie
    Kociewie is an ethnocultural region in northern Poland known for its distinct folk traditions, dialect, and rural landscapes.
  • 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_69bd43daa3e4819090b59d127db70e57 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd86330e4c8190b5452226886287b3 completed March 20, 2026, 5:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69ca53f2b0b8819090ca8ee3721a9867 completed March 30, 2026, 10:44 a.m.
Created at: March 20, 2026, 2:02 p.m.