Triple

T2986831
Position Surface form Disambiguated ID Type / Status
Subject Ankara Metro E80644 entity
Predicate servesDistrict P82 FINISHED
Object Kızılay
Kızılay is a central district and major commercial hub in Ankara, Turkey, known for its busy squares, offices, shops, and public transportation connections.
 E315533 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: Kızılay | Statement: [Ankara Metro, servesDistrict, Kızılay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kızılay
Context triple: [Ankara Metro, servesDistrict, Kızılay]
  • A. Kurskaya
    Kurskaya is a Moscow Metro station on the Koltsevaya (Circle) Line, serving as a major transfer hub in the city’s rapid transit network.
  • B. Tselinograd
    Tselinograd was the Soviet-era name of Kazakhstan’s capital city, now known as Astana.
  • C. La Russa
    La Russa is an Italian surname most prominently associated with Hall of Fame Major League Baseball manager Tony La Russa.
  • D. Krasnov
    Krasnov is a Russian surname borne by various notable figures in military, political, and cultural history.
  • E. Komsomolskaya
    Komsomolskaya is one of Moscow Metro’s most famous and ornate stations, renowned for its grand Baroque-style decor and elaborate mosaics.
  • 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: Kızılay
Triple: [Ankara Metro, servesDistrict, Kızılay]
Generated description
Kızılay is a central district and major commercial hub in Ankara, Turkey, known for its busy squares, offices, shops, and public transportation connections.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kızılay
Target entity description: Kızılay is a central district and major commercial hub in Ankara, Turkey, known for its busy squares, offices, shops, and public transportation connections.
  • A. Kurskaya
    Kurskaya is a Moscow Metro station on the Koltsevaya (Circle) Line, serving as a major transfer hub in the city’s rapid transit network.
  • B. Tselinograd
    Tselinograd was the Soviet-era name of Kazakhstan’s capital city, now known as Astana.
  • C. La Russa
    La Russa is an Italian surname most prominently associated with Hall of Fame Major League Baseball manager Tony La Russa.
  • D. Krasnov
    Krasnov is a Russian surname borne by various notable figures in military, political, and cultural history.
  • E. Komsomolskaya
    Komsomolskaya is one of Moscow Metro’s most famous and ornate stations, renowned for its grand Baroque-style decor and elaborate mosaics.
  • 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_69ad8b16c3488190b47b6aa7a59a335b completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad99c88f608190bf734e0b744bf3d1 completed March 8, 2026, 3:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69b108fb46b08190bcbd00e69cf06047 completed March 11, 2026, 6:17 a.m.
NEDg Description generation batch_69b1098429f0819086767078b9687674 completed March 11, 2026, 6:19 a.m.
NED2 Entity disambiguation (via description) batch_69b109ea926481909c85a9aa28027d41 completed March 11, 2026, 6:21 a.m.
Created at: March 8, 2026, 2:59 p.m.