Triple

T1147710
Position Surface form Disambiguated ID Type / Status
Subject Cathedral Basilica of St. Stephen the King E23603 entity
Predicate locatedIn P40 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: [Cathedral Basilica of St. Stephen the King, locatedIn, Székesfehérvár]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Székesfehérvár
Context triple: [Cathedral Basilica of St. Stephen the King, locatedIn, 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. Gödöllő
    Gödöllő is a Hungarian town near Budapest best known for its historic Royal Palace, one of the largest Baroque palaces in Hungary.
  • E. Siófok
    Siófok is a popular resort town on the southern shore of Lake Balaton in Hungary, known for its beaches and vibrant summer tourism.
  • 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_69a493f0d32c8190ac74bad3c87f2641 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4bc7041248190893e4c655dbd0604 completed March 1, 2026, 10:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69addf1667908190bdcb6d200577418d completed March 8, 2026, 8:41 p.m.
Created at: March 1, 2026, 7:44 p.m.