Triple

T2094745
Position Surface form Disambiguated ID Type / Status
Subject Fejér County E32753 entity
Predicate containsTown P847 FINISHED
Object Gárdony E137985 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: Gárdony | Statement: [Fejér County, containsTown, Gárdony]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gárdony
Context triple: [Fejér County, containsTown, Gárdony]
  • A. Gárdony chosen
    Gárdony is a Hungarian town and popular resort area on the southern shore of Lake Velence, known for its beaches, thermal waters, and recreational tourism.
  • B. Sarolt
    Sarolt was a prominent 10th-century Hungarian noblewoman and duchess, influential in the Christianization and early state formation of Hungary as the wife of Grand Prince Géza and mother of King Stephen I.
  • C. Zamárdi
    Zamárdi is a popular Hungarian resort town on the southern shore of Lake Balaton, known for its beaches, lakeside recreation, and summer festivals.
  • D. Gauda
    Gauda was a historic region in eastern India, centered in present-day West Bengal and Bangladesh, that served as an important political and cultural center in early medieval times.
  • E. Serhedi
    Serhedi is a regional dialect of Kurmanji Kurdish spoken in parts of the Kurdish-inhabited areas of the Middle East.
  • 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_69a885eba0708190999696a45cbec816 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abba99ddc48190bb2097b56efb7aca completed March 7, 2026, 5:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae305cb77c819085c4f3eb2223f749 completed March 9, 2026, 2:28 a.m.
Created at: March 4, 2026, 7:43 p.m.