Triple

T12309506
Position Surface form Disambiguated ID Type / Status
Subject Šamorín E293439 entity
Predicate historicalRegion P915 FINISHED
Object Csallóköz E941838 NE FINISHED

How this triple was built (2 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: Csallóköz | Statement: [Šamorín, historicalRegion, Csallóköz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Csallóköz
Context triple: [Šamorín, historicalRegion, Csallóköz]
  • A. Csallóköz chosen
    Csallóköz is a large river island and historical region in southwestern Slovakia, situated between branches of the Danube and known for its fertile land and significant freshwater resources.
  • B. Nagykálló
    Nagykálló is a town in northeastern Hungary known for its historical architecture and traditional cultural heritage.
  • C. Nagykőrös
    Nagykőrös is a historic town in central Hungary known for its agricultural traditions and small-town character.
  • D. Csákvár
    Csákvár is a small town in central Hungary known for its rural character and location within the Transdanubian region.
  • 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.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6ab6a2b50819082f6aedd32ed608a completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f01ace8819087f245b9216f4dc8 completed April 10, 2026, 6:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69f79418e26c819088f3aa608d9d65d6 completed May 3, 2026, 6:29 p.m.
Created at: April 8, 2026, 9:53 p.m.