Triple

T12082919
Position Surface form Disambiguated ID Type / Status
Subject Prague 6 E287726 entity
Predicate contains P35 FINISHED
Object Veleslavín
Veleslavín is a residential district in the northwestern part of Prague known for its transport connections and proximity to green areas.
E968099 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: Veleslavín | Statement: [Prague 6, contains, Veleslavín]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Veleslavín
Context triple: [Prague 6, contains, Veleslavín]
  • A. Bregava
    Bregava is a river in Bosnia and Herzegovina known for flowing through the town of Stolac before joining the Neretva River.
  • B. Vasishka
    Vasishka was a Kushan emperor who ruled parts of northern India and Central Asia in the early 3rd century CE, known primarily from his inscriptions and coinage.
  • C. Lučina
    Lučina is a river in the Moravian-Silesian Region of the Czech Republic that flows through the city of Ostrava.
  • D. Slaná
    Slaná is a river in central Europe that flows through Slovakia and Hungary, where it is known as the Sajó.
  • E. Vratislavia
    Vratislavia is the historical Latin name of the city now known as Wrocław in southwestern Poland.
  • 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: Veleslavín
Triple: [Prague 6, contains, Veleslavín]
Generated description
Veleslavín is a residential district in the northwestern part of Prague known for its transport connections and proximity to green areas.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Veleslavín
Target entity description: Veleslavín is a residential district in the northwestern part of Prague known for its transport connections and proximity to green areas.
  • A. Bregava
    Bregava is a river in Bosnia and Herzegovina known for flowing through the town of Stolac before joining the Neretva River.
  • B. Vasishka
    Vasishka was a Kushan emperor who ruled parts of northern India and Central Asia in the early 3rd century CE, known primarily from his inscriptions and coinage.
  • C. Lučina
    Lučina is a river in the Moravian-Silesian Region of the Czech Republic that flows through the city of Ostrava.
  • D. Slaná
    Slaná is a river in central Europe that flows through Slovakia and Hungary, where it is known as the Sajó.
  • E. Vratislavia
    Vratislavia is the historical Latin name of the city now known as Wrocław in southwestern Poland.
  • 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_69d6ab4964708190850585628b287b0c completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d915124e4c8190b0264c2a09e3c2f3 completed April 10, 2026, 3:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f66509208190b7206e78df41c2fe completed May 2, 2026, 1:04 p.m.
NEDg Description generation batch_69f6022ecf38819080f0eb6a3a815c5b completed May 2, 2026, 1:54 p.m.
NED2 Entity disambiguation (via description) batch_69f606560934819092ba4d4fa162b799 completed May 2, 2026, 2:12 p.m.
Created at: April 8, 2026, 9:48 p.m.