Triple

T14148181
Position Surface form Disambiguated ID Type / Status
Subject 14th district of Budapest E350606 entity
Predicate alsoKnownAs P39 FINISHED
Object Zugló E349621 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: Zugló | Statement: [14th district of Budapest, alsoKnownAs, Zugló]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zugló
Context triple: [14th district of Budapest, alsoKnownAs, Zugló]
  • A. Zugló chosen
    Zugló is Budapest’s 14th district, a largely residential area known for its parks, historic villas, and major landmarks such as City Park and Heroes’ Square.
  • B. Zala
    Zala is a river in western Hungary that flows into Lake Balaton and lends its name to the surrounding Zala region.
  • C. Trencsén
    Trencsén is a historic town in present-day Slovakia, known for its medieval castle and its role as an important regional center in the former Upper Hungary.
  • D. Bochsa
    Bochsa is the surname of Nicolas-Charles Bochsa, a 19th-century French composer, harpist, and influential music teacher.
  • E. Oberá
    Oberá is a major inland city in northeastern Argentina known for its cultural diversity and role as an agricultural and commercial center in Misiones Province.
  • 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_69d827865f608190b311820428ae027b completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de61237ef481909374c1f68a2370b7 completed April 14, 2026, 3:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcdf205c788190920b5055f9fe63a8 completed May 7, 2026, 6:51 p.m.
Created at: April 10, 2026, 12:55 a.m.