Triple

T745305
Position Surface form Disambiguated ID Type / Status
Subject Łódź E15327 entity
Predicate hasSportsClub P346 FINISHED
Object Widzew Łódź
Widzew Łódź is a Polish professional football club from the city of Łódź, historically known as one of the country’s most successful and popular teams.
 E90937 NE FINISHED

How this triple was built (4 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: Widzew Łódź | Statement: [Łódź, hasSportsClub, Widzew Łódź]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Widzew Łódź
Context triple: [Łódź, hasSportsClub, Widzew Łódź]
  • A. Ciechocinek
    Ciechocinek is a Polish spa town renowned for its historic saline graduation towers and therapeutic health resorts.
  • B. Łódź
    Łódź is one of Poland’s largest cities, historically known as a major industrial and textile manufacturing center.
  • C. Wadowice
    Wadowice is a historic town in southern Poland best known as the birthplace of Pope John Paul II.
  • D. Zamość
    Zamość is a Renaissance-planned city in southeastern Poland, renowned for its well-preserved Old Town and UNESCO World Heritage status.
  • E. Kazimierz
    Kazimierz is a historic district of Kraków known for its rich Jewish heritage, medieval architecture, and vibrant cultural life.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Widzew Łódź
Triple: [Łódź, hasSportsClub, Widzew Łódź]
Generated description
Widzew Łódź is a Polish professional football club from the city of Łódź, historically known as one of the country’s most successful and popular teams.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Widzew Łódź
Target entity description: Widzew Łódź is a Polish professional football club from the city of Łódź, historically known as one of the country’s most successful and popular teams.
  • A. Ciechocinek
    Ciechocinek is a Polish spa town renowned for its historic saline graduation towers and therapeutic health resorts.
  • B. Łódź
    Łódź is one of Poland’s largest cities, historically known as a major industrial and textile manufacturing center.
  • C. Wadowice
    Wadowice is a historic town in southern Poland best known as the birthplace of Pope John Paul II.
  • D. Zamość
    Zamość is a Renaissance-planned city in southeastern Poland, renowned for its well-preserved Old Town and UNESCO World Heritage status.
  • E. Kazimierz
    Kazimierz is a historic district of Kraków known for its rich Jewish heritage, medieval architecture, and vibrant cultural life.
  • F. None of above. chosen

Provenance (5 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_69a49358aa308190adbc9b5a0a2adcf9 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a61217b881908592096b1edacb8a completed March 1, 2026, 8:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69a66671ebd48190a785be0ae0d3588c completed March 3, 2026, 4:41 a.m.
NEDg Description generation batch_69a666ded0288190a43e8a13db4f6914 completed March 3, 2026, 4:43 a.m.
NED2 Entity disambiguation (via description) batch_69a667b8de2c819092f9a4c10abeeb56 completed March 3, 2026, 4:46 a.m.
Created at: March 1, 2026, 7:37 p.m.