Triple

T2967237
Position Surface form Disambiguated ID Type / Status
Subject Istria E80193 entity
Predicate hasMountainRange P651 FINISHED
Object Učka
Učka is a mountain range in western Croatia known for its forested peaks, hiking trails, and panoramic views over the Istrian Peninsula and the Adriatic Sea.
 E320908 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: Učka | Statement: [Istria, hasMountainRange, Učka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Učka
Context triple: [Istria, hasMountainRange, Učka]
  • A. Dudinka
    Dudinka is a remote Arctic port town in northern Siberia, Russia, serving as a key shipping hub on the Yenisei River and gateway to the Norilsk industrial region.
  • B. Morača
    Morača is a major river in Montenegro that flows through the capital city of Podgorica before emptying into Lake Skadar.
  • C. Lučina
    Lučina is a river in the Moravian-Silesian Region of the Czech Republic that flows through the city of Ostrava.
  • D. Nidzica
    Nidzica is a historic town in northern Poland known for its well-preserved medieval Teutonic castle and picturesque surroundings.
  • E. Łužica
    Łužica is the Upper Sorbian name for Lusatia, a historical region in Central Europe traditionally inhabited by the West Slavic Sorbian people.
  • 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: Učka
Triple: [Istria, hasMountainRange, Učka]
Generated description
Učka is a mountain range in western Croatia known for its forested peaks, hiking trails, and panoramic views over the Istrian Peninsula and the Adriatic Sea.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Učka
Target entity description: Učka is a mountain range in western Croatia known for its forested peaks, hiking trails, and panoramic views over the Istrian Peninsula and the Adriatic Sea.
  • A. Dudinka
    Dudinka is a remote Arctic port town in northern Siberia, Russia, serving as a key shipping hub on the Yenisei River and gateway to the Norilsk industrial region.
  • B. Morača
    Morača is a major river in Montenegro that flows through the capital city of Podgorica before emptying into Lake Skadar.
  • C. Lučina
    Lučina is a river in the Moravian-Silesian Region of the Czech Republic that flows through the city of Ostrava.
  • D. Nidzica
    Nidzica is a historic town in northern Poland known for its well-preserved medieval Teutonic castle and picturesque surroundings.
  • E. Łužica
    Łužica is the Upper Sorbian name for Lusatia, a historical region in Central Europe traditionally inhabited by the West Slavic Sorbian people.
  • 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_69ad8b14ffe881908ffed62f9595c867 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad996e93788190ba9883714d4dfa0c completed March 8, 2026, 3:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1de9657648190a7f0309065f80728 completed March 11, 2026, 9:28 p.m.
NEDg Description generation batch_69b1dfa2fb28819089d7d76d9dc72e06 completed March 11, 2026, 9:33 p.m.
NED2 Entity disambiguation (via description) batch_69b1e0243a848190bce24d035a79fc0a completed March 11, 2026, 9:35 p.m.
Created at: March 8, 2026, 2:58 p.m.