Triple

T5328955
Position Surface form Disambiguated ID Type / Status
Subject Tarnów Voivodeship E123255 entity
Predicate containedCity P8465 FINISHED
Object Brzesko
Brzesko is a town in southern Poland known for its historical architecture and regional brewing traditions.
 E635966 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: Brzesko | Statement: [Tarnów Voivodeship, containedCity, Brzesko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Brzesko
Context triple: [Tarnów Voivodeship, containedCity, Brzesko]
  • A. Bolesławiec
    Bolesławiec is a historic town in southwestern Poland renowned for its traditional hand-decorated pottery.
  • B. Krotoszyn
    Krotoszyn is a historic town in west-central Poland known for its medieval origins and changing political affiliations, including periods under Prussian and German rule.
  • C. Bartoszyce
    Bartoszyce is a town in northern Poland known for its historical architecture and location near the border with Russia’s Kaliningrad Oblast.
  • D. Bielany
    Bielany is a northern district of Warsaw, Poland, known for its residential neighborhoods, green spaces, and connection to the city center via the Warsaw Metro.
  • E. Tarnobrzeg
    Tarnobrzeg is a city in southeastern Poland known historically for its sulfur mining industry and location along the Vistula River.
  • 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: Brzesko
Triple: [Tarnów Voivodeship, containedCity, Brzesko]
Generated description
Brzesko is a town in southern Poland known for its historical architecture and regional brewing traditions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Brzesko
Target entity description: Brzesko is a town in southern Poland known for its historical architecture and regional brewing traditions.
  • A. Bolesławiec
    Bolesławiec is a historic town in southwestern Poland renowned for its traditional hand-decorated pottery.
  • B. Krotoszyn
    Krotoszyn is a historic town in west-central Poland known for its medieval origins and changing political affiliations, including periods under Prussian and German rule.
  • C. Bartoszyce
    Bartoszyce is a town in northern Poland known for its historical architecture and location near the border with Russia’s Kaliningrad Oblast.
  • D. Bielany
    Bielany is a northern district of Warsaw, Poland, known for its residential neighborhoods, green spaces, and connection to the city center via the Warsaw Metro.
  • E. Tarnobrzeg
    Tarnobrzeg is a city in southeastern Poland known historically for its sulfur mining industry and location along the Vistula River.
  • 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_69bd46477f9081909d242a327d749466 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd8593bd6c8190b2054e548ddf2458 completed March 20, 2026, 5:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69c769cd39e081908624c8471b9131a1 completed March 28, 2026, 5:40 a.m.
NEDg Description generation batch_69c76da6e9e4819097c42d5a74efb3e7 completed March 28, 2026, 5:56 a.m.
NED2 Entity disambiguation (via description) batch_69c76e01a3fc8190b766188a0c243385 completed March 28, 2026, 5:58 a.m.
Created at: March 20, 2026, 2 p.m.