Triple

T21746266
Position Surface form Disambiguated ID Type / Status
Subject Aleksandrovsky Sad E536795 entity
Predicate formerName P65 FINISHED
Object Ulitsa Kominterna
Ulitsa Kominterna was a former name of Moscow’s Aleksandrovsky Sad metro station, located near the Kremlin and the Alexander Garden.
 E1500150 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: Ulitsa Kominterna | Statement: [Aleksandrovsky Sad, formerName, Ulitsa Kominterna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ulitsa Kominterna
Context triple: [Aleksandrovsky Sad, formerName, Ulitsa Kominterna]
  • A. Anastas
    Anastas is a masculine given name most notably borne by Soviet statesman Anastas Mikoyan.
  • B. Kremlings
    Kremlings are a recurring race of crocodilian villains in the Donkey Kong video game series, often serving as the primary antagonists led by King K. Rool.
  • C. Pocius
    Pocius is a Lithuanian surname most notably associated with former professional basketball player Martynas Pocius.
  • D. Comasina
    Comasina is a station on Milan's Metro system that serves as the northern endpoint of Line 3 in the Comasina district of the city.
  • E. Prinkipos
    Prinkipos is the former name of Büyükada, the largest and most famous of Istanbul’s Princes’ Islands in the Sea of Marmara.
  • 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: Ulitsa Kominterna
Triple: [Aleksandrovsky Sad, formerName, Ulitsa Kominterna]
Generated description
Ulitsa Kominterna was a former name of Moscow’s Aleksandrovsky Sad metro station, located near the Kremlin and the Alexander Garden.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ulitsa Kominterna
Target entity description: Ulitsa Kominterna was a former name of Moscow’s Aleksandrovsky Sad metro station, located near the Kremlin and the Alexander Garden.
  • A. Anastas
    Anastas is a masculine given name most notably borne by Soviet statesman Anastas Mikoyan.
  • B. Kremlings
    Kremlings are a recurring race of crocodilian villains in the Donkey Kong video game series, often serving as the primary antagonists led by King K. Rool.
  • C. Pocius
    Pocius is a Lithuanian surname most notably associated with former professional basketball player Martynas Pocius.
  • D. Comasina
    Comasina is a station on Milan's Metro system that serves as the northern endpoint of Line 3 in the Comasina district of the city.
  • E. Prinkipos
    Prinkipos is the former name of Büyükada, the largest and most famous of Istanbul’s Princes’ Islands in the Sea of Marmara.
  • 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_69e0c46df5448190b4322127ffc4c690 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69f01a76540c8190b91a67f4a70869fb completed April 28, 2026, 2:24 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0a2eddcabc8190a97320733d2b44ca completed May 17, 2026, 9:10 p.m.
NEDg Description generation batch_6a0a3069272c8190a6c90ca0c8b777d5 completed May 17, 2026, 9:17 p.m.
NED2 Entity disambiguation (via description) batch_6a0a3114ce6c8190aeb972462837bb25 completed May 17, 2026, 9:20 p.m.
Created at: April 16, 2026, 6:49 p.m.