Triple

T15498464
Position Surface form Disambiguated ID Type / Status
Subject Galina E378883 entity
Predicate hasVariant P455 FINISHED
Object Halina
Halina is a feminine given name, commonly used in Slavic countries and considered a variant of the name Galina.
 E1160073 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: Halina | Statement: [Galina, hasVariant, Halina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Halina
Context triple: [Galina, hasVariant, Halina]
  • A. Hala Gąsienicowa
    Hala Gąsienicowa is a picturesque alpine meadow in the Polish Tatra Mountains, popular as a hiking hub and starting point for routes to surrounding peaks and lakes.
  • B. Zofia
    Zofia is a feminine given name of Slavic origin, particularly common in Poland and other Central and Eastern European countries.
  • C. Dagmara
    Dagmara is a feminine given name, primarily used in Slavic countries, that is a variant of the name Dagmar.
  • D. Michalina
    Michalina is a feminine given name of Slavic origin, commonly used in Polish-speaking countries.
  • E. Krystyna
    Krystyna is a central female character in Roman Polanski’s 1962 psychological drama film "Knife in the Water," whose interactions help drive the film’s tense, character-driven narrative.
  • 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: Halina
Triple: [Galina, hasVariant, Halina]
Generated description
Halina is a feminine given name, commonly used in Slavic countries and considered a variant of the name Galina.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Halina
Target entity description: Halina is a feminine given name, commonly used in Slavic countries and considered a variant of the name Galina.
  • A. Hala Gąsienicowa
    Hala Gąsienicowa is a picturesque alpine meadow in the Polish Tatra Mountains, popular as a hiking hub and starting point for routes to surrounding peaks and lakes.
  • B. Zofia
    Zofia is a feminine given name of Slavic origin, particularly common in Poland and other Central and Eastern European countries.
  • C. Dagmara
    Dagmara is a feminine given name, primarily used in Slavic countries, that is a variant of the name Dagmar.
  • D. Michalina
    Michalina is a feminine given name of Slavic origin, commonly used in Polish-speaking countries.
  • E. Krystyna
    Krystyna is a central female character in Roman Polanski’s 1962 psychological drama film "Knife in the Water," whose interactions help drive the film’s tense, character-driven narrative.
  • 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_69d85cd53a7c819080f5b9042c4c199e completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03fb0aee081909db1c54349ec8492 completed April 16, 2026, 1:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff3667a53c81908be789f99e580265 completed May 9, 2026, 1:28 p.m.
NEDg Description generation batch_69ff3744ba8c81909989864ba107b93b completed May 9, 2026, 1:31 p.m.
NED2 Entity disambiguation (via description) batch_69ff37ee94b081909309062b2d30ede5 completed May 9, 2026, 1:34 p.m.
Created at: April 10, 2026, 3:53 a.m.