Triple

T12492327
Position Surface form Disambiguated ID Type / Status
Subject Henryk E298594 entity
Predicate hasFeminineForm P1613 FINISHED
Object Henryka
Henryka is a Polish feminine given name derived from the male name Henryk.
 E991436 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: Henryka | Statement: [Henryk, hasFeminineForm, Henryka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Henryka
Context triple: [Henryk, hasFeminineForm, Henryka]
  • A. Józefina
    Józefina is the Polish form of the female given name Josephine, commonly used in Poland and among Polish-speaking communities.
  • 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. Magda
    Magda is a feminine given name, commonly used as a short form of Magdalena in various European languages.
  • 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: Henryka
Triple: [Henryk, hasFeminineForm, Henryka]
Generated description
Henryka is a Polish feminine given name derived from the male name Henryk.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Henryka
Target entity description: Henryka is a Polish feminine given name derived from the male name Henryk.
  • A. Józefina
    Józefina is the Polish form of the female given name Josephine, commonly used in Poland and among Polish-speaking communities.
  • 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. Magda
    Magda is a feminine given name, commonly used as a short form of Magdalena in various European languages.
  • 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_69d6ada377208190a36011199a4d8558 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94de3076c81909640c982d520ca6b completed April 10, 2026, 7:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6556e9180819084ddb984754b0b54 completed May 2, 2026, 7:50 p.m.
NEDg Description generation batch_69f656a6dafc81908acf59c0ba65189a completed May 2, 2026, 7:55 p.m.
NED2 Entity disambiguation (via description) batch_69f65b4d109c8190b48c71f664e7bb3f completed May 2, 2026, 8:15 p.m.
Created at: April 8, 2026, 9:56 p.m.