Triple

T4103956
Position Surface form Disambiguated ID Type / Status
Subject EIT Digital E88403 entity
Predicate hasCoLocationCentersIn P47071 FINISHED
Object Budapest E13406 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: Budapest | Statement: [EIT Digital, hasCoLocationCentersIn, Budapest]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Budapest
Context triple: [EIT Digital, hasCoLocationCentersIn, Budapest]
  • A. Budapest chosen
    Budapest is the capital and largest city of Hungary, renowned for its historic architecture, thermal baths, and prominent location along the Danube River.
  • B. Siófok
    Siófok is a popular resort town on the southern shore of Lake Balaton in Hungary, known for its beaches and vibrant summer tourism.
  • C. Győr
    Győr is a historic city in northwestern Hungary, known as an important regional cultural and economic center at the confluence of the Danube, Rába, and Rábca rivers.
  • D. Pozsony
    Pozsony is the historical Hungarian name for the city now known as Bratislava, the capital of Slovakia.
  • E. Kaposvár
    Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
  • 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_69aed9484fb881909146f4c772ad277c completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af019af25481909e9f1d171356f3e8 completed March 9, 2026, 5:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69b57f15f0088190b2ec453183f0ca7f completed March 14, 2026, 3:30 p.m.
Created at: March 9, 2026, 3:40 p.m.