Triple

T14864894
Position Surface form Disambiguated ID Type / Status
Subject Tabán E349590 entity
Predicate nativeName P15 FINISHED
Object Tabán E349575 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: Tabán | Statement: [Tabán, nativeName, Tabán]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tabán
Context triple: [Tabán, nativeName, Tabán]
  • A. Tabán chosen
    Tabán is a historic neighborhood in Budapest, Hungary, known for its former hillside streets, thermal baths, and multicultural past.
  • B. Tamási
    Tamási is a small town in Hungary known for its thermal baths and location within the Tolna County region.
  • C. Teleki
    Teleki is a Hungarian noble family name most notably associated with Pál Teleki, a geographer and two-time prime minister of Hungary in the early 20th century.
  • D. Tomarza
    Tomarza is a district and town in central Turkey known for its agricultural activities and location within Kayseri Province in Central Anatolia.
  • E. Túr
    Túr is a river in Central Europe that flows through parts of Romania and Hungary before joining the Bodrog River.
  • 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_69d822ed7e1881909b90fca143ad7e34 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded5761c688190b4477cb081554b51 completed April 15, 2026, 12:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe6b4f224c8190bb2e06203c9b3a94 completed May 8, 2026, 11:01 p.m.
Created at: April 10, 2026, 1:54 a.m.