Triple

T663876
Position Surface form Disambiguated ID Type / Status
Subject Kraków E12815 entity
Predicate hasFootballClub P346 FINISHED
Object Wisła Kraków
Wisła Kraków is one of Poland’s oldest and most successful football clubs, based in the city of Kraków.
 E83027 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: Wisła Kraków | Statement: [Kraków, hasFootballClub, Wisła Kraków]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wisła Kraków
Context triple: [Kraków, hasFootballClub, Wisła Kraków]
  • A. Skawina
    Skawina is a town in southern Poland near Kraków, known for its industrial facilities and role as a local economic and transport hub.
  • B. Trzebinia
    Trzebinia is a town in southern Poland known for its industrial character and location between Kraków and Katowice.
  • C. Lech River
    The Lech River is a major Alpine river flowing through Austria and southern Germany, known for its scenic course, hydropower use, and role as a tributary of the Danube.
  • D. Dunajec River
    The Dunajec River is a picturesque river in southern Poland and northern Slovakia, renowned for its scenic gorge and popular rafting routes through the Pieniny Mountains.
  • E. Dolina Kościeliska
    Dolina Kościeliska is a popular scenic valley in the Polish Tatra Mountains, known for its dramatic limestone gorges, caves, and hiking trails.
  • 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: Wisła Kraków
Triple: [Kraków, hasFootballClub, Wisła Kraków]
Generated description
Wisła Kraków is one of Poland’s oldest and most successful football clubs, based in the city of Kraków.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wisła Kraków
Target entity description: Wisła Kraków is one of Poland’s oldest and most successful football clubs, based in the city of Kraków.
  • A. Skawina
    Skawina is a town in southern Poland near Kraków, known for its industrial facilities and role as a local economic and transport hub.
  • B. Trzebinia
    Trzebinia is a town in southern Poland known for its industrial character and location between Kraków and Katowice.
  • C. Lech River
    The Lech River is a major Alpine river flowing through Austria and southern Germany, known for its scenic course, hydropower use, and role as a tributary of the Danube.
  • D. Dunajec River
    The Dunajec River is a picturesque river in southern Poland and northern Slovakia, renowned for its scenic gorge and popular rafting routes through the Pieniny Mountains.
  • E. Dolina Kościeliska
    Dolina Kościeliska is a popular scenic valley in the Polish Tatra Mountains, known for its dramatic limestone gorges, caves, and hiking trails.
  • 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_69a493355dec819098d4244b2fa34885 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49fd1f0ec819087003d30bbab2fa6 completed March 1, 2026, 8:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5c398cc748190ab720263096064ef completed March 2, 2026, 5:06 p.m.
NEDg Description generation batch_69a5c4523e8081909464ca227b880e77 completed March 2, 2026, 5:09 p.m.
NED2 Entity disambiguation (via description) batch_69a5cdae65808190b7191f63d5f16d9f completed March 2, 2026, 5:49 p.m.
Created at: March 1, 2026, 7:36 p.m.