Triple

T8056206
Position Surface form Disambiguated ID Type / Status
Subject Krka d. d. E188007 entity
Predicate hasSubsidiary P254 FINISHED
Object Krka Magyarország
Krka Magyarország is the Hungarian subsidiary of the international pharmaceutical company Krka, responsible for marketing and distributing its medicines and health products in Hungary.
 E707059 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: Krka Magyarország | Statement: [Krka d. d., hasSubsidiary, Krka Magyarország]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Krka Magyarország
Context triple: [Krka d. d., hasSubsidiary, Krka Magyarország]
  • 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. Crikvenica
    Crikvenica is a coastal town and popular tourist resort on the Adriatic Sea in western Croatia.
  • C. Hévíz
    Hévíz is a Hungarian spa town famous for its large natural thermal lake and wellness tourism.
  • D. Morača
    Morača is a major river in Montenegro that flows through the capital city of Podgorica before emptying into Lake Skadar.
  • E. Baška
    Baška is a popular coastal town and tourist resort on the island of Krk in Croatia, known for its long pebble beach and historic old town.
  • 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: Krka Magyarország
Triple: [Krka d. d., hasSubsidiary, Krka Magyarország]
Generated description
Krka Magyarország is the Hungarian subsidiary of the international pharmaceutical company Krka, responsible for marketing and distributing its medicines and health products in Hungary.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Krka Magyarország
Target entity description: Krka Magyarország is the Hungarian subsidiary of the international pharmaceutical company Krka, responsible for marketing and distributing its medicines and health products in Hungary.
  • 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. Crikvenica
    Crikvenica is a coastal town and popular tourist resort on the Adriatic Sea in western Croatia.
  • C. Hévíz
    Hévíz is a Hungarian spa town famous for its large natural thermal lake and wellness tourism.
  • D. Morača
    Morača is a major river in Montenegro that flows through the capital city of Podgorica before emptying into Lake Skadar.
  • E. Baška
    Baška is a popular coastal town and tourist resort on the island of Krk in Croatia, known for its long pebble beach and historic old town.
  • 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_69ca82b2f68881908c50560697e210da completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3fa26fb88190b6799bddeb68ed78 completed March 31, 2026, 3:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc5725c5308190b2eb565902124b10 completed March 31, 2026, 11:22 p.m.
NEDg Description generation batch_69cc58ae255c8190a6a2d2f335c6cc3f completed March 31, 2026, 11:28 p.m.
NED2 Entity disambiguation (via description) batch_69cc5cd22b188190b8a31e8e8ac8b98d completed March 31, 2026, 11:46 p.m.
Created at: March 30, 2026, 5:25 p.m.