Triple

T1070296
Position Surface form Disambiguated ID Type / Status
Subject Alba Regia E23309 entity
Predicate modernEquivalent P21626 FINISHED
Object Székesfehérvár E3113 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: Székesfehérvár | Statement: [Alba Regia, modernEquivalent, Székesfehérvár]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Székesfehérvár
Context triple: [Alba Regia, modernEquivalent, Székesfehérvár]
  • A. Székesfehérvár chosen
    Székesfehérvár is a historic city in central Hungary that served as a medieval royal seat and coronation site for Hungarian kings.
  • B. Veszprém
    Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
  • C. Esztergom
    Esztergom is a historic Hungarian city on the Danube River that served as an early royal capital and remains a major religious and cultural center.
  • D. Siófok
    Siófok is a popular resort town on the southern shore of Lake Balaton in Hungary, known for its beaches and vibrant summer tourism.
  • E. Pécs
    Pécs is a historic cultural and university city in southwestern Hungary, renowned for its Roman and Ottoman heritage and its designation as a European Capital of Culture in 2010.
  • 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_69a493ee1f908190992b5f0d1b04459b completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b914b4908190886d6698294c6b5b completed March 1, 2026, 10:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69ada0b527dc819085f7b3ff85170e16 completed March 8, 2026, 4:15 p.m.
Created at: March 1, 2026, 7:42 p.m.