Triple

T2663699
Position Surface form Disambiguated ID Type / Status
Subject Plzeň Region E54782 entity
Predicate hasMunicipalityWithExtendedPowers P41272 FINISHED
Object Sušice
Sušice is a town in the Czech Republic known as a local administrative, cultural, and tourist center in the southwestern Plzeň Region.
E299068 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: Sušice | Statement: [Plzeň Region, hasMunicipalityWithExtendedPowers, Sušice]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sušice
Context triple: [Plzeň Region, hasMunicipalityWithExtendedPowers, Sušice]
  • A. Trutnov
    Trutnov is a town in the Czech Republic known as a gateway to the eastern Krkonoše (Giant Mountains) region and for its historical center and industrial heritage.
  • B. Říčany
    Říčany is a town in the Czech Republic, located just southeast of Prague and known as a popular residential and commuter suburb with historical roots.
  • C. Slaný
    Slaný is a historic town in the Czech Republic known for its medieval center and location northwest of Prague.
  • D. Harrachov
    Harrachov is a Czech mountain town in the Krkonoše range known as a major ski and winter sports resort near the Polish border.
  • E. Nymburk
    Nymburk is a historic town in the Czech Republic known for its medieval fortifications and location on the Elbe 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: Sušice
Triple: [Plzeň Region, hasMunicipalityWithExtendedPowers, Sušice]
Generated description
Sušice is a town in the Czech Republic known as a local administrative, cultural, and tourist center in the southwestern Plzeň Region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Sušice
Target entity description: Sušice is a town in the Czech Republic known as a local administrative, cultural, and tourist center in the southwestern Plzeň Region.
  • A. Trutnov
    Trutnov is a town in the Czech Republic known as a gateway to the eastern Krkonoše (Giant Mountains) region and for its historical center and industrial heritage.
  • B. Říčany
    Říčany is a town in the Czech Republic, located just southeast of Prague and known as a popular residential and commuter suburb with historical roots.
  • C. Slaný
    Slaný is a historic town in the Czech Republic known for its medieval center and location northwest of Prague.
  • D. Harrachov
    Harrachov is a Czech mountain town in the Krkonoše range known as a major ski and winter sports resort near the Polish border.
  • E. Nymburk
    Nymburk is a historic town in the Czech Republic known for its medieval fortifications and location on the Elbe 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_69ab49e028948190b97e01d73548b1d9 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abdd1e80dc819083e04e1427d187d0 completed March 7, 2026, 8:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc6382a2081909ea7216c19a65e7e completed March 10, 2026, 7:20 a.m.
NEDg Description generation batch_69afc6c6c620819098b76db174a6f98e completed March 10, 2026, 7:22 a.m.
NED2 Entity disambiguation (via description) batch_69afc72b2e3c8190aad78ac8924f07af completed March 10, 2026, 7:24 a.m.
Created at: March 6, 2026, 9:54 p.m.