Triple

T1991264
Position Surface form Disambiguated ID Type / Status
Subject Zamoskvoretskaya Line E43255 entity
Predicate hasStation P35 FINISHED
Object Aeroport
Aeroport is a Moscow Metro station on the Zamoskvoretskaya Line, named after the nearby Khodynka Aerodrome area.
E222043 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: Aeroport | Statement: [Zamoskvoretskaya Line, hasStation, Aeroport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aeroport
Context triple: [Zamoskvoretskaya Line, hasStation, Aeroport]
  • A. Flughafen
    Flughafen is the Nuremberg U-Bahn station that serves Nuremberg Airport, providing direct metro access between the airport and the city.
  • B. Aeropuerto T2
    Aeropuerto T2 is a Metrobús station in Mexico City that serves Terminal 2 of the city’s international airport.
  • C. Cat Bi International Airport
    Cat Bi International Airport is a major commercial and military airport serving the coastal city of Hai Phong in northern Vietnam.
  • D. Coll Airport
    Coll Airport is a small regional airfield serving the island of Coll in Scotland’s Inner Hebrides, providing vital connections to the mainland.
  • E. Mitiga International Airport
    Mitiga International Airport is a major airport serving Tripoli, Libya, handling both domestic and international flights.
  • 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: Aeroport
Triple: [Zamoskvoretskaya Line, hasStation, Aeroport]
Generated description
Aeroport is a Moscow Metro station on the Zamoskvoretskaya Line, named after the nearby Khodynka Aerodrome area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Aeroport
Target entity description: Aeroport is a Moscow Metro station on the Zamoskvoretskaya Line, named after the nearby Khodynka Aerodrome area.
  • A. Flughafen
    Flughafen is the Nuremberg U-Bahn station that serves Nuremberg Airport, providing direct metro access between the airport and the city.
  • B. Aeropuerto T2
    Aeropuerto T2 is a Metrobús station in Mexico City that serves Terminal 2 of the city’s international airport.
  • C. Cat Bi International Airport
    Cat Bi International Airport is a major commercial and military airport serving the coastal city of Hai Phong in northern Vietnam.
  • D. Coll Airport
    Coll Airport is a small regional airfield serving the island of Coll in Scotland’s Inner Hebrides, providing vital connections to the mainland.
  • E. Mitiga International Airport
    Mitiga International Airport is a major airport serving Tripoli, Libya, handling both domestic and international flights.
  • 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_69a88714cf2c819081644be450b8356e completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb8451fe8819093531052f4533c36 completed March 7, 2026, 5:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae0338631c8190a8c7e5177ac473a8 completed March 8, 2026, 11:16 p.m.
NEDg Description generation batch_69ae03d3c3048190bf9c476bea8c4c1e completed March 8, 2026, 11:18 p.m.
NED2 Entity disambiguation (via description) batch_69ae0480bf9081909ee1ba5d5c805ab3 completed March 8, 2026, 11:21 p.m.
Created at: March 4, 2026, 7:37 p.m.