Triple

T5319917
Position Surface form Disambiguated ID Type / Status
Subject Géza, Grand Prince of the Hungarians E121646 entity
Predicate givenName P17 FINISHED
Object Géza
Géza was a 10th-century Grand Prince of the Hungarians who played a key role in consolidating the Hungarian state and paving the way for its Christianization under his son Stephen I.
 E513662 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: Géza | Statement: [Géza, Grand Prince of the Hungarians, givenName, Géza]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Géza
Context triple: [Géza, Grand Prince of the Hungarians, givenName, Géza]
  • A. György
    György is a Hungarian given name commonly used for men, equivalent to the English name George.
  • B. Béla
    Béla was a common medieval Hungarian royal given name borne by several kings, most notably Béla IV of Hungary.
  • C. Jenő
    Jenő is the Hungarian given name of the renowned theoretical physicist and mathematician Wigner Jenő Pál, known in English as Eugene Wigner.
  • D. András
    András is the Hungarian given name of Andrew S. Grove, the influential former CEO and co-founder of Intel.
  • E. István
    István is the Hungarian given name of Stephen I of Hungary, the first Christian king and founder of the medieval Hungarian state.
  • 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: Géza
Triple: [Géza, Grand Prince of the Hungarians, givenName, Géza]
Generated description
Géza was a 10th-century Grand Prince of the Hungarians who played a key role in consolidating the Hungarian state and paving the way for its Christianization under his son Stephen I.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Géza
Target entity description: Géza was a 10th-century Grand Prince of the Hungarians who played a key role in consolidating the Hungarian state and paving the way for its Christianization under his son Stephen I.
  • A. György
    György is a Hungarian given name commonly used for men, equivalent to the English name George.
  • B. Béla
    Béla was a common medieval Hungarian royal given name borne by several kings, most notably Béla IV of Hungary.
  • C. Jenő
    Jenő is the Hungarian given name of the renowned theoretical physicist and mathematician Wigner Jenő Pál, known in English as Eugene Wigner.
  • D. András
    András is the Hungarian given name of Andrew S. Grove, the influential former CEO and co-founder of Intel.
  • E. István
    István is the Hungarian given name of Stephen I of Hungary, the first Christian king and founder of the medieval Hungarian state.
  • 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_69bd463d956c819088105c3db802c017 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd8574c4b48190a0513ce59eedb564 completed March 20, 2026, 5:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf21b8692881908f04d8cfd7d3d10d completed March 21, 2026, 10:54 p.m.
NEDg Description generation batch_69bf232d7a888190878f7a3ce769dd83 completed March 21, 2026, 11:01 p.m.
NED2 Entity disambiguation (via description) batch_69bf23c3ad9c8190b84a31a4b8fe8fca completed March 21, 2026, 11:03 p.m.
Created at: March 20, 2026, 1:59 p.m.