Triple

T9813624
Position Surface form Disambiguated ID Type / Status
Subject Příbor E238340 entity
Predicate hasTwinTown P919 FINISHED
Object Orlová
Orlová is a town in the Moravian-Silesian Region of the Czech Republic, historically associated with coal mining and heavy industry.
 E823286 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: Orlová | Statement: [Příbor, hasTwinTown, Orlová]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Orlová
Context triple: [Příbor, hasTwinTown, Orlová]
  • A. Lopokova
    Lopokova is the surname of Lydia Lopokova, a renowned Russian ballerina associated with the Ballets Russes and later known for her marriage to economist John Maynard Keynes.
  • B. Rositsa
    Rositsa is a river in northern Bulgaria that serves as a significant tributary of the Yantra River.
  • C. Kamarinskaya
    Kamarinskaya is an 1848 orchestral work by Mikhail Glinka, often regarded as a pioneering piece in Russian symphonic music for its use of folk themes.
  • D. Saltykova
    Saltykova is a Russian surname most notoriously associated with Darya Saltykova, an 18th-century noblewoman and serial killer.
  • E. Govardeyskaya
    Govardeyskaya is a Moscow Metro station on the Kalininsko–Solntsevskaya line.
  • 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: Orlová
Triple: [Příbor, hasTwinTown, Orlová]
Generated description
Orlová is a town in the Moravian-Silesian Region of the Czech Republic, historically associated with coal mining and heavy industry.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Orlová
Target entity description: Orlová is a town in the Moravian-Silesian Region of the Czech Republic, historically associated with coal mining and heavy industry.
  • A. Lopokova
    Lopokova is the surname of Lydia Lopokova, a renowned Russian ballerina associated with the Ballets Russes and later known for her marriage to economist John Maynard Keynes.
  • B. Rositsa
    Rositsa is a river in northern Bulgaria that serves as a significant tributary of the Yantra River.
  • C. Kamarinskaya
    Kamarinskaya is an 1848 orchestral work by Mikhail Glinka, often regarded as a pioneering piece in Russian symphonic music for its use of folk themes.
  • D. Saltykova
    Saltykova is a Russian surname most notoriously associated with Darya Saltykova, an 18th-century noblewoman and serial killer.
  • E. Govardeyskaya
    Govardeyskaya is a Moscow Metro station on the Kalininsko–Solntsevskaya line.
  • 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_69ca84defac48190abc1148804f184c1 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb22410208190b82b81a4df800f80 completed April 2, 2026, 12:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1cc63c450819091e57030a48e7d88 completed April 5, 2026, 2:43 a.m.
NEDg Description generation batch_69d1cce3d9d481909eaf7278dfe20955 completed April 5, 2026, 2:45 a.m.
NED2 Entity disambiguation (via description) batch_69d1cd5d1670819085c58ff8889318af completed April 5, 2026, 2:47 a.m.
Created at: March 30, 2026, 8:30 p.m.