Triple

T777238
Position Surface form Disambiguated ID Type / Status
Subject Imereti E16413 entity
Predicate traditionalWine P4038 FINISHED
Object Tsitska
Tsitska is a Georgian white grape variety from the Imereti region, known for producing fresh, high-acidity wines often used in both still and sparkling styles.
 E107355 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: Tsitska | Statement: [Imereti, traditionalWine, Tsitska]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tsitska
Context triple: [Imereti, traditionalWine, Tsitska]
  • A. Nikolassee
    Nikolassee is a residential locality in southwestern Berlin known for its lakeside setting, green spaces, and villa-style neighborhoods.
  • B. Shubskaya
    Shubskaya is a Russian surname most notably associated with Anastasia Shubskaya, a film producer and the wife of hockey star Alexander Ovechkin.
  • C. Novoslobodskaya
    Novoslobodskaya is a Moscow Metro station famed for its distinctive stained-glass panels and ornate, cathedral-like interior design.
  • D. Mytishchi
    Mytishchi is a city in western Russia that serves as a major suburban and industrial center just northeast of Moscow.
  • E. Yunaska
    Yunaska is the maiden surname of Lara Trump, who is married to Eric Trump, son of former U.S. President Donald Trump.
  • 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: Tsitska
Triple: [Imereti, traditionalWine, Tsitska]
Generated description
Tsitska is a Georgian white grape variety from the Imereti region, known for producing fresh, high-acidity wines often used in both still and sparkling styles.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tsitska
Target entity description: Tsitska is a Georgian white grape variety from the Imereti region, known for producing fresh, high-acidity wines often used in both still and sparkling styles.
  • A. Nikolassee
    Nikolassee is a residential locality in southwestern Berlin known for its lakeside setting, green spaces, and villa-style neighborhoods.
  • B. Shubskaya
    Shubskaya is a Russian surname most notably associated with Anastasia Shubskaya, a film producer and the wife of hockey star Alexander Ovechkin.
  • C. Novoslobodskaya
    Novoslobodskaya is a Moscow Metro station famed for its distinctive stained-glass panels and ornate, cathedral-like interior design.
  • D. Mytishchi
    Mytishchi is a city in western Russia that serves as a major suburban and industrial center just northeast of Moscow.
  • E. Yunaska
    Yunaska is the maiden surname of Lara Trump, who is married to Eric Trump, son of former U.S. President Donald Trump.
  • 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_69a4936ad1fc81908f190208059ccf78 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4aa9cecd08190a23c9f65080a4ac7 completed March 1, 2026, 9:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7c70e6eb48190b019759cd656e629 completed March 4, 2026, 5:45 a.m.
NEDg Description generation batch_69a7c896d1c481909493a1bc4e6266e3 completed March 4, 2026, 5:52 a.m.
NED2 Entity disambiguation (via description) batch_69a7c918ebbc81908ad58bb8045543e6 completed March 4, 2026, 5:54 a.m.
Created at: March 1, 2026, 7:37 p.m.