Triple

T5328904
Position Surface form Disambiguated ID Type / Status
Subject Satu Mare E123254 entity
Predicate hasAlternativeName P39 FINISHED
Object Szatmár
Szatmár is the Hungarian name for Satu Mare, a historic city in northwestern Romania near the Hungarian border.
 E512580 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: Szatmár | Statement: [Satu Mare, hasAlternativeName, Szatmár]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Szatmár
Context triple: [Satu Mare, hasAlternativeName, Szatmár]
  • A. Sárvár
    Sárvár is a historic town in western Hungary known for its medieval Nádasdy Castle and thermal spa culture.
  • B. Tiszaújváros
    Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
  • C. Mátraháza
    Mátraháza is a small mountain resort village in northern Hungary, known for its scenic location in the Mátra range and its hiking and wellness tourism.
  • D. Sátoraljaújhely
    Sátoraljaújhely is a historic town in northeastern Hungary near the Slovak border, known for its wine region, cultural heritage, and scenic Zemplén Mountains setting.
  • E. Kőszeg
    Kőszeg is a historic Hungarian town near the Austrian border, renowned for its well-preserved medieval architecture and role in defending against Ottoman sieges.
  • 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: Szatmár
Triple: [Satu Mare, hasAlternativeName, Szatmár]
Generated description
Szatmár is the Hungarian name for Satu Mare, a historic city in northwestern Romania near the Hungarian border.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Szatmár
Target entity description: Szatmár is the Hungarian name for Satu Mare, a historic city in northwestern Romania near the Hungarian border.
  • A. Sárvár
    Sárvár is a historic town in western Hungary known for its medieval Nádasdy Castle and thermal spa culture.
  • B. Tiszaújváros
    Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
  • C. Mátraháza
    Mátraháza is a small mountain resort village in northern Hungary, known for its scenic location in the Mátra range and its hiking and wellness tourism.
  • D. Sátoraljaújhely
    Sátoraljaújhely is a historic town in northeastern Hungary near the Slovak border, known for its wine region, cultural heritage, and scenic Zemplén Mountains setting.
  • E. Kőszeg
    Kőszeg is a historic Hungarian town near the Austrian border, renowned for its well-preserved medieval architecture and role in defending against Ottoman sieges.
  • 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_69bd46477f9081909d242a327d749466 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd8593bd6c8190b2054e548ddf2458 completed March 20, 2026, 5:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf18b396c08190be60bcb9ac933b5e completed March 21, 2026, 10:16 p.m.
NEDg Description generation batch_69bf1b23cdbc8190bec3b7bc7f70770c completed March 21, 2026, 10:26 p.m.
NED2 Entity disambiguation (via description) batch_69bf1ba465948190aac6bfc406bae806 completed March 21, 2026, 10:28 p.m.
Created at: March 20, 2026, 2 p.m.