Triple

T13294482
Position Surface form Disambiguated ID Type / Status
Subject Łabuńka River E316644 entity
Predicate hasNameInLanguage P15 FINISHED
Object Łabuńka
Łabuńka is a river in southeastern Poland that flows through the Lublin Voivodeship, including the area around the city of Zamość.
 E1032778 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: Łabuńka | Statement: [Łabuńka River, hasNameInLanguage, Łabuńka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Łabuńka
Context triple: [Łabuńka River, hasNameInLanguage, Łabuńka]
  • A. Gubałówka
    Gubałówka is a popular hill and tourist destination in the Polish Tatra region, known for its panoramic views of Zakopane and the surrounding mountains.
  • B. Zbyszko
    Zbyszko is a Polish given name, traditionally used as a diminutive or variant of Zbigniew and known from medieval and literary contexts.
  • C. Bumar-Łabędy
    Bumar-Łabędy is a Polish defense manufacturer best known for producing armored vehicles and modernized main battle tanks.
  • D. Lubień
    Lubień is a village in southern Poland, known here as the place where prominent historian and politician Bronisław Geremek died.
  • E. Sokołówka
    Sokołówka is a small river in Poland known for flowing through the city of Łódź and its surrounding areas.
  • 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: Łabuńka
Triple: [Łabuńka River, hasNameInLanguage, Łabuńka]
Generated description
Łabuńka is a river in southeastern Poland that flows through the Lublin Voivodeship, including the area around the city of Zamość.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Łabuńka
Target entity description: Łabuńka is a river in southeastern Poland that flows through the Lublin Voivodeship, including the area around the city of Zamość.
  • A. Gubałówka
    Gubałówka is a popular hill and tourist destination in the Polish Tatra region, known for its panoramic views of Zakopane and the surrounding mountains.
  • B. Zbyszko
    Zbyszko is a Polish given name, traditionally used as a diminutive or variant of Zbigniew and known from medieval and literary contexts.
  • C. Bumar-Łabędy
    Bumar-Łabędy is a Polish defense manufacturer best known for producing armored vehicles and modernized main battle tanks.
  • D. Lubień
    Lubień is a village in southern Poland, known here as the place where prominent historian and politician Bronisław Geremek died.
  • E. Sokołówka
    Sokołówka is a small river in Poland known for flowing through the city of Łódź and its surrounding areas.
  • 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_69d806b349908190a9a61dd9323bf153 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d99079c8508190b6208db9affcbc0e completed April 11, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69f716d8ee2081908428339216c43b47 completed May 3, 2026, 9:35 a.m.
NEDg Description generation batch_69f717b7b6dc8190ab323c1926dd9adb completed May 3, 2026, 9:39 a.m.
NED2 Entity disambiguation (via description) batch_69f718705a44819084b97d35a10ee50d completed May 3, 2026, 9:42 a.m.
Created at: April 9, 2026, 9:28 p.m.