Triple

T17940922
Position Surface form Disambiguated ID Type / Status
Subject Castle of Eger E448583 entity
Predicate namedAfter P63 FINISHED
Object István Dobó
István Dobó was a 16th-century Hungarian military commander best known for leading the successful defense of Eger against the Ottoman siege in 1552.
E1297991 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: István Dobó | Statement: [Castle of Eger, namedAfter, István Dobó]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: István Dobó
Context triple: [Castle of Eger, namedAfter, István Dobó]
  • A. Gyula Jankovits
    Gyula Jankovits was a Hungarian sculptor best known for creating prominent public monuments in Budapest, including the Gellért Monument.
  • B. László Bárdossy
    László Bárdossy was a Hungarian politician who served as prime minister during World War II and played a key role in aligning Hungary with Nazi Germany.
  • C. József Vágó
    József Vágó was a Hungarian architect associated with early 20th-century modernist and Art Nouveau movements, known for his contributions to significant international projects.
  • D. Vilmos Gábor
    Vilmos Gábor was the father of Hungarian-American actress and socialite Zsa Zsa Gabor.
  • E. Lajos Koltai
    Lajos Koltai is a Hungarian cinematographer and film director renowned for his visually expressive work on both European and Hollywood films.
  • 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: István Dobó
Triple: [Castle of Eger, namedAfter, István Dobó]
Generated description
István Dobó was a 16th-century Hungarian military commander best known for leading the successful defense of Eger against the Ottoman siege in 1552.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: István Dobó
Target entity description: István Dobó was a 16th-century Hungarian military commander best known for leading the successful defense of Eger against the Ottoman siege in 1552.
  • A. Gyula Jankovits
    Gyula Jankovits was a Hungarian sculptor best known for creating prominent public monuments in Budapest, including the Gellért Monument.
  • B. László Bárdossy
    László Bárdossy was a Hungarian politician who served as prime minister during World War II and played a key role in aligning Hungary with Nazi Germany.
  • C. József Vágó
    József Vágó was a Hungarian architect associated with early 20th-century modernist and Art Nouveau movements, known for his contributions to significant international projects.
  • D. Vilmos Gábor
    Vilmos Gábor was the father of Hungarian-American actress and socialite Zsa Zsa Gabor.
  • E. Lajos Koltai
    Lajos Koltai is a Hungarian cinematographer and film director renowned for his visually expressive work on both European and Hollywood films.
  • 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_69d8b9f79d14819095540856928f0e25 completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e4ad95f4608190b1ebb45944218f07 completed April 19, 2026, 10:25 a.m.
NED1 Entity disambiguation (via context triple) batch_6a032914b7008190a7207d93f865ea8f completed May 12, 2026, 1:20 p.m.
NEDg Description generation batch_6a032b598bd881909f53df8f82799b6b completed May 12, 2026, 1:30 p.m.
NED2 Entity disambiguation (via description) batch_6a032c42d0a08190b2665adafaffdca7 completed May 12, 2026, 1:33 p.m.
Created at: April 10, 2026, 10:21 a.m.