Triple

T9024033
Position Surface form Disambiguated ID Type / Status
Subject Ostankino Tower E215998 entity
Predicate nearbyAttraction P3449 FINISHED
Object VDNKh
VDNKh is a vast exhibition and amusement complex in Moscow known for its grand Soviet-era pavilions, monuments, and cultural attractions.
 E773225 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: VDNKh | Statement: [Ostankino Tower, nearbyAttraction, VDNKh]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: VDNKh
Context triple: [Ostankino Tower, nearbyAttraction, VDNKh]
  • A. VRN
    VRN is the public transport association serving Germany’s Rhine-Neckar metropolitan region, coordinating regional and local transit services across multiple states.
  • B. Vdm
    Vdm is the official station code for Van der Madeweg, a metro station in Amsterdam, Netherlands.
  • C. VDY
    VDY is the IATA airport code for Jindal Vijaynagar Airport in Karnataka, India.
  • D. VDV
    VDV is the elite airborne branch of Russia’s armed forces, known for rapid-deployment paratrooper and air-assault operations.
  • E. DKNVS
    DKNVS is the abbreviation for the Royal Norwegian Society of Sciences and Letters, one of Norway’s oldest and most prestigious learned societies dedicated to the advancement of science and scholarship.
  • 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: VDNKh
Triple: [Ostankino Tower, nearbyAttraction, VDNKh]
Generated description
VDNKh is a vast exhibition and amusement complex in Moscow known for its grand Soviet-era pavilions, monuments, and cultural attractions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: VDNKh
Target entity description: VDNKh is a vast exhibition and amusement complex in Moscow known for its grand Soviet-era pavilions, monuments, and cultural attractions.
  • A. VRN
    VRN is the public transport association serving Germany’s Rhine-Neckar metropolitan region, coordinating regional and local transit services across multiple states.
  • B. Vdm
    Vdm is the official station code for Van der Madeweg, a metro station in Amsterdam, Netherlands.
  • C. VDY
    VDY is the IATA airport code for Jindal Vijaynagar Airport in Karnataka, India.
  • D. VDV
    VDV is the elite airborne branch of Russia’s armed forces, known for rapid-deployment paratrooper and air-assault operations.
  • E. DKNVS
    DKNVS is the abbreviation for the Royal Norwegian Society of Sciences and Letters, one of Norway’s oldest and most prestigious learned societies dedicated to the advancement of science and scholarship.
  • 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_69ca83a5fa88819088144801b4dd7245 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6a7a770081908dfe3ce3374a04ba completed April 1, 2026, 12:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfdbbcaedc819084f8b057fbfcb0e7 completed April 3, 2026, 3:24 p.m.
NEDg Description generation batch_69cfdcae0e5c81909c50a0b53c1cf7cc completed April 3, 2026, 3:28 p.m.
NED2 Entity disambiguation (via description) batch_69cfdd6a2ba481908d66fed8f05a1297 completed April 3, 2026, 3:31 p.m.
Created at: March 30, 2026, 7:07 p.m.