Triple

T855112
Position Surface form Disambiguated ID Type / Status
Subject Głogów E18473 entity
Predicate hasRailConnectionTo P848 FINISHED
Object Zielona Góra
Zielona Góra is a city in western Poland known for its wine-making tradition and annual wine festival.
 E149641 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: Zielona Góra | Statement: [Głogów, hasRailConnectionTo, Zielona Góra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Zielona Góra
Context triple: [Głogów, hasRailConnectionTo, Zielona Góra]
  • A. Glogów
    Glogów is a historic town in western Poland on the Oder River, known for its medieval origins and reconstructed Old Town.
  • B. Kalisz
    Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
  • C. Opole
    Opole is a historic city in southwestern Poland, known as one of the country’s oldest urban centers and a regional cultural hub.
  • D. Łódź
    Łódź is one of Poland’s largest cities, historically known as a major industrial and textile manufacturing center.
  • E. Wrocław
    Wrocław is a major historic city in southwestern Poland, known for its picturesque Old Town, numerous bridges over the Oder River, and role as a cultural and academic center.
  • 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: Zielona Góra
Triple: [Głogów, hasRailConnectionTo, Zielona Góra]
Generated description
Zielona Góra is a city in western Poland known for its wine-making tradition and annual wine festival.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Zielona Góra
Target entity description: Zielona Góra is a city in western Poland known for its wine-making tradition and annual wine festival.
  • A. Glogów
    Glogów is a historic town in western Poland on the Oder River, known for its medieval origins and reconstructed Old Town.
  • B. Kalisz
    Kalisz is one of Poland’s oldest cities, located in the Greater Poland region and known for its historical architecture and cultural heritage.
  • C. Opole
    Opole is a historic city in southwestern Poland, known as one of the country’s oldest urban centers and a regional cultural hub.
  • D. Łódź
    Łódź is one of Poland’s largest cities, historically known as a major industrial and textile manufacturing center.
  • E. Wrocław
    Wrocław is a major historic city in southwestern Poland, known for its picturesque Old Town, numerous bridges over the Oder River, and role as a cultural and academic center.
  • 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_69a4938bdd3c8190a954a3c11844d9cf completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ac3a48c08190b4677d825fcbfaf9 completed March 1, 2026, 9:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69acb2ede6208190ba0857f4b0209fcf completed March 7, 2026, 11:21 p.m.
NEDg Description generation batch_69acb6f9ba608190ba1d9180567e31a8 completed March 7, 2026, 11:38 p.m.
NED2 Entity disambiguation (via description) batch_69acb767a8fc8190ba4aee2c8da39480 completed March 7, 2026, 11:40 p.m.
Created at: March 1, 2026, 7:39 p.m.