Triple

T10182055
Position Surface form Disambiguated ID Type / Status
Subject Tolna County E236810 entity
Predicate hasMajorSettlement P316 FINISHED
Object Tamási
Tamási is a small town in Hungary known for its thermal baths and location within the Tolna County region.
 E845765 NE FINISHED

How this triple was built (4 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: Tamási | Statement: [Tolna County, hasMajorSettlement, Tamási]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tamási
Context triple: [Tolna County, hasMajorSettlement, Tamási]
  • A. Tabán
    Tabán is a historic neighborhood in Budapest, Hungary, known for its former hillside streets, thermal baths, and multicultural past.
  • 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. 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. Vilmos
    Vilmos is a masculine given name of Hungarian origin, equivalent to William in English.
  • E. Béla
    Béla was a common medieval Hungarian royal given name borne by several kings, most notably Béla IV of Hungary.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Tamási
Triple: [Tolna County, hasMajorSettlement, Tamási]
Generated description
Tamási is a small town in Hungary known for its thermal baths and location within the Tolna County region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tamási
Target entity description: Tamási is a small town in Hungary known for its thermal baths and location within the Tolna County region.
  • A. Tabán
    Tabán is a historic neighborhood in Budapest, Hungary, known for its former hillside streets, thermal baths, and multicultural past.
  • 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. 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. Vilmos
    Vilmos is a masculine given name of Hungarian origin, equivalent to William in English.
  • E. Béla
    Béla was a common medieval Hungarian royal given name borne by several kings, most notably Béla IV of Hungary.
  • F. None of above. chosen

Provenance (5 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_69ca84d7260c8190bfbec36762943f37 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cded32b91c8190b01ad37b2456080a completed April 2, 2026, 4:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69d3013f0c54819092ee9c2c46fdf69e completed April 6, 2026, 12:41 a.m.
NEDg Description generation batch_69d3028a4384819094a7daef7287e54f completed April 6, 2026, 12:47 a.m.
NED2 Entity disambiguation (via description) batch_69d302e9d2688190bc292f8f2287c458 completed April 6, 2026, 12:48 a.m.
Created at: March 30, 2026, 9:12 p.m.