Triple

T1201757
Position Surface form Disambiguated ID Type / Status
Subject Krasnodar Krai E25796 entity
Predicate hasCity P316 FINISHED
Object Anapa
Anapa is a resort city on Russia’s Black Sea coast, known for its sandy beaches, mild climate, and popularity as a family vacation destination.
 E137647 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: Anapa | Statement: [Krasnodar Krai, hasCity, Anapa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Anapa
Context triple: [Krasnodar Krai, hasCity, Anapa]
  • A. Wasilla
    Wasilla is a small city in south-central Alaska known as part of the Anchorage metropolitan area and for being the hometown of former governor Sarah Palin.
  • B. Solan
    Solan is a town in the Indian state of Himachal Pradesh known for its mushroom cultivation and as a growing commercial and educational hub in the region.
  • C. Lota
    Lota is a coastal city in southern Chile known historically for its coal mining industry and maritime heritage.
  • D. Neu-Anif
    Neu-Anif is a locality within the municipality of Anif in the Austrian state of Salzburg, known as a residential and suburban area near the city of Salzburg.
  • E. Tula
    Tula is a historic Russian city south of Moscow, known for its metalworking, samovar production, and as a cultural center near Leo Tolstoy’s estate at Yasnaya Polyana.
  • 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: Anapa
Triple: [Krasnodar Krai, hasCity, Anapa]
Generated description
Anapa is a resort city on Russia’s Black Sea coast, known for its sandy beaches, mild climate, and popularity as a family vacation destination.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Anapa
Target entity description: Anapa is a resort city on Russia’s Black Sea coast, known for its sandy beaches, mild climate, and popularity as a family vacation destination.
  • A. Wasilla
    Wasilla is a small city in south-central Alaska known as part of the Anchorage metropolitan area and for being the hometown of former governor Sarah Palin.
  • B. Solan
    Solan is a town in the Indian state of Himachal Pradesh known for its mushroom cultivation and as a growing commercial and educational hub in the region.
  • C. Lota
    Lota is a coastal city in southern Chile known historically for its coal mining industry and maritime heritage.
  • D. Neu-Anif
    Neu-Anif is a locality within the municipality of Anif in the Austrian state of Salzburg, known as a residential and suburban area near the city of Salzburg.
  • E. Tula
    Tula is a historic Russian city south of Moscow, known for its metalworking, samovar production, and as a cultural center near Leo Tolstoy’s estate at Yasnaya Polyana.
  • 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_69a49429f5ec8190a6a205eb0ae81e5e completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bd9fece4819089a6a2d61e61fa2e completed March 1, 2026, 10:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac7f3a48d48190ae5179312b52b3ee completed March 7, 2026, 7:40 p.m.
NEDg Description generation batch_69ac7fc59a488190adbdf156aaff8c03 completed March 7, 2026, 7:43 p.m.
NED2 Entity disambiguation (via description) batch_69ac806a6d748190acc5cdfa8fb90a64 completed March 7, 2026, 7:45 p.m.
Created at: March 1, 2026, 7:46 p.m.