Triple

T11173069
Position Surface form Disambiguated ID Type / Status
Subject Sibiu E264332 entity
Predicate alternativeName P39 FINISHED
Object Nagyszeben
Nagyszeben is the Hungarian name for Sibiu, a historic city in central Romania known for its well-preserved medieval architecture and cultural significance in Transylvania.
 E1021327 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: Nagyszeben | Statement: [Sibiu, alternativeName, Nagyszeben]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nagyszeben
Context triple: [Sibiu, alternativeName, Nagyszeben]
  • A. Füzesabony
    Füzesabony is a small town in northeastern Hungary known as a regional railway junction and gateway to the Bükk and Mátra regions.
  • B. Nagyvázsony
    Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
  • C. Egerszalók
    Egerszalók is a Hungarian village famous for its thermal springs and striking terraced salt hill spa complex.
  • D. Zalaegerszeg
    Zalaegerszeg is a city in western Hungary that serves as the administrative center of Zala County and a regional economic and cultural hub.
  • E. Dunakeszi
    Dunakeszi is a town in Hungary located just north of Budapest, known as a rapidly growing suburban and commuter settlement along the Danube in Pest County.
  • 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: Nagyszeben
Triple: [Sibiu, alternativeName, Nagyszeben]
Generated description
Nagyszeben is the Hungarian name for Sibiu, a historic city in central Romania known for its well-preserved medieval architecture and cultural significance in Transylvania.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nagyszeben
Target entity description: Nagyszeben is the Hungarian name for Sibiu, a historic city in central Romania known for its well-preserved medieval architecture and cultural significance in Transylvania.
  • A. Füzesabony
    Füzesabony is a small town in northeastern Hungary known as a regional railway junction and gateway to the Bükk and Mátra regions.
  • B. Nagyvázsony
    Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
  • C. Egerszalók
    Egerszalók is a Hungarian village famous for its thermal springs and striking terraced salt hill spa complex.
  • D. Zalaegerszeg
    Zalaegerszeg is a city in western Hungary that serves as the administrative center of Zala County and a regional economic and cultural hub.
  • E. Dunakeszi
    Dunakeszi is a town in Hungary located just north of Budapest, known as a rapidly growing suburban and commuter settlement along the Danube in Pest County.
  • 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_69d6aa9dafac8190bd90d2c74f661aa7 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e89660208190b1d9e91529f5d246 completed April 9, 2026, 5:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6e2527570819092314ee0a678e53c completed May 3, 2026, 5:51 a.m.
NEDg Description generation batch_69f6e32bf5508190b4dc58971f8f64d0 completed May 3, 2026, 5:54 a.m.
NED2 Entity disambiguation (via description) batch_69f6e407dd988190b928b8931985a815 completed May 3, 2026, 5:58 a.m.
Created at: April 8, 2026, 9:29 p.m.