Triple

T12084638
Position Surface form Disambiguated ID Type / Status
Subject Danuta Wałęsa E287774 entity
Predicate givenName P17 FINISHED
Object Danuta
Danuta is a feminine given name of Slavic origin, particularly common in Poland and other Central and Eastern European countries.
 E967473 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: Danuta | Statement: [Danuta Wałęsa, givenName, Danuta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Danuta
Context triple: [Danuta Wałęsa, givenName, Danuta]
  • A. Dagmara
    Dagmara is a feminine given name, primarily used in Slavic countries, that is a variant of the name Dagmar.
  • B. Danuta Stenka
    Danuta Stenka is a renowned Polish film, television, and theatre actress known for her powerful dramatic roles and extensive work in Polish cinema.
  • C. Martyna
    Martyna is a feminine given name used in various European countries, often considered a variant of names like Martina or Martine.
  • D. Beata
    Beata is a feminine given name of Latin origin, commonly used in various European countries and meaning "blessed" or "happy."
  • E. Michalina
    Michalina is a feminine given name of Slavic origin, commonly used in Polish-speaking countries.
  • 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: Danuta
Triple: [Danuta Wałęsa, givenName, Danuta]
Generated description
Danuta is a feminine given name of Slavic origin, particularly common in Poland and other Central and Eastern European countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Danuta
Target entity description: Danuta is a feminine given name of Slavic origin, particularly common in Poland and other Central and Eastern European countries.
  • A. Dagmara
    Dagmara is a feminine given name, primarily used in Slavic countries, that is a variant of the name Dagmar.
  • B. Danuta Stenka
    Danuta Stenka is a renowned Polish film, television, and theatre actress known for her powerful dramatic roles and extensive work in Polish cinema.
  • C. Martyna
    Martyna is a feminine given name used in various European countries, often considered a variant of names like Martina or Martine.
  • D. Beata
    Beata is a feminine given name of Latin origin, commonly used in various European countries and meaning "blessed" or "happy."
  • E. Michalina
    Michalina is a feminine given name of Slavic origin, commonly used in Polish-speaking countries.
  • 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_69d6ab4964708190850585628b287b0c completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d91513bbb0819084a8bb877e03060c completed April 10, 2026, 3:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f666bf1c819089de1235617e775b completed May 2, 2026, 1:04 p.m.
NEDg Description generation batch_69f60335285c819089f69472b2e48130 completed May 2, 2026, 1:59 p.m.
NED2 Entity disambiguation (via description) batch_69f60410ce0481908b2deb7522a3ec00 completed May 2, 2026, 2:02 p.m.
Created at: April 8, 2026, 9:48 p.m.