Triple

T13289512
Position Surface form Disambiguated ID Type / Status
Subject Mercedes-Benz CLA-Class E316530 entity
Predicate assemblyLocation P40 FINISHED
Object Kecskemét, Hungary E135511 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: Kecskemét, Hungary | Statement: [Mercedes-Benz CLA-Class, assemblyLocation, Kecskemét, Hungary]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kecskemét, Hungary
Context triple: [Mercedes-Benz CLA-Class, assemblyLocation, Kecskemét, Hungary]
  • A. Kecskemét chosen
    Kecskemét is a city in central Hungary known for its Art Nouveau architecture, cultural institutions, and role as an administrative and economic center of the region.
  • B. Kaposvár, Hungary
    Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
  • C. Kisvárda, Hungary
    Kisvárda is a small town in northeastern Hungary known for its historic castle, thermal baths, and role as a regional cultural and economic center.
  • D. Ricse, Hungary
    Ricse, Hungary is a small village in northeastern Hungary best known as the birthplace of film mogul Adolph Zukor, a founder of Paramount Pictures.
  • E. Budaörs, Hungary
    Budaörs is a suburban town just west of Budapest in Hungary, known for its rapid post-communist development, commercial centers, and role as a key transport hub near the capital.
  • 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_69d806b349908190a9a61dd9323bf153 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d99076aeec8190b0cb883ab60d3f6b completed April 11, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69f716d4a6f48190a2020e11e887be2e completed May 3, 2026, 9:35 a.m.
Created at: April 9, 2026, 9:27 p.m.