Triple

T1017262
Position Surface form Disambiguated ID Type / Status
Subject Stephen I of Hungary E21958 entity
Predicate placeOfBirth P1 FINISHED
Object Esztergom
Esztergom is a historic Hungarian city on the Danube River that served as an early royal capital and remains a major religious and cultural center.
 E175407 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: Esztergom | Statement: [Stephen I of Hungary, placeOfBirth, Esztergom]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Esztergom
Context triple: [Stephen I of Hungary, placeOfBirth, Esztergom]
  • A. Székesfehérvár
    Székesfehérvár is a historic city in central Hungary that served as a medieval royal seat and coronation site for Hungarian kings.
  • B. Veszprém
    Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
  • C. Pozsony
    Pozsony is the historical Hungarian name for the city now known as Bratislava, the capital of Slovakia.
  • D. Siófok
    Siófok is a popular resort town on the southern shore of Lake Balaton in Hungary, known for its beaches and vibrant summer tourism.
  • E. Sopron
    Sopron is a historic city in western Hungary near the Austrian border, known for its well-preserved medieval old town and wine-making traditions.
  • 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: Esztergom
Triple: [Stephen I of Hungary, placeOfBirth, Esztergom]
Generated description
Esztergom is a historic Hungarian city on the Danube River that served as an early royal capital and remains a major religious and cultural center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Esztergom
Target entity description: Esztergom is a historic Hungarian city on the Danube River that served as an early royal capital and remains a major religious and cultural center.
  • A. Székesfehérvár
    Székesfehérvár is a historic city in central Hungary that served as a medieval royal seat and coronation site for Hungarian kings.
  • B. Veszprém
    Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
  • C. Pozsony
    Pozsony is the historical Hungarian name for the city now known as Bratislava, the capital of Slovakia.
  • D. Siófok
    Siófok is a popular resort town on the southern shore of Lake Balaton in Hungary, known for its beaches and vibrant summer tourism.
  • E. Sopron
    Sopron is a historic city in western Hungary near the Austrian border, known for its well-preserved medieval old town and wine-making traditions.
  • 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_69a493c68e24819080ed0ee8bcfd5ce0 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b7c4d488819081d8214ba0a22fe5 completed March 1, 2026, 10:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad307c46988190bb4ba823ad313a88 completed March 8, 2026, 8:17 a.m.
NEDg Description generation batch_69ad312e77ac8190b931a42317f2cefa completed March 8, 2026, 8:19 a.m.
NED2 Entity disambiguation (via description) batch_69ad3184853c8190953b288693ce6ea4 completed March 8, 2026, 8:21 a.m.
Created at: March 1, 2026, 7:41 p.m.