Triple

T6653665
Position Surface form Disambiguated ID Type / Status
Subject Antonia E150886 entity
Predicate hasVariant P455 FINISHED
Object Antónia
Antónia is a feminine given name commonly used in various European languages, often as a variant of Antonia.
 E609102 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: Antónia | Statement: [Antonia, hasVariant, Antónia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Antónia
Context triple: [Antonia, hasVariant, Antónia]
  • A. Antonina
    Antonina was a prominent Byzantine noblewoman and influential wife of the famed general Belisarius, noted for her political acumen and close association with Empress Theodora in the 6th century.
  • B. Mária
    Mária is the Hungarian and Slovak form of the given name Mary, commonly used in Central and Eastern Europe.
  • C. Tereza Vávrová
    Tereza Vávrová is a notable bearer of the Czech surname Vávrová, recognized enough to be specifically cited in reference to the name.
  • D. Helena Davidová
    Helena Davidová is a member of Franz Kafka’s extended family, known as the daughter of his sister Ottla Kafka.
  • E. Libuše
    Libuše is a Czech opera by Bedřich Smetana, centered on the legendary princess Libuše who prophesies the glory of Prague and the Czech nation.
  • 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: Antónia
Triple: [Antonia, hasVariant, Antónia]
Generated description
Antónia is a feminine given name commonly used in various European languages, often as a variant of Antonia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Antónia
Target entity description: Antónia is a feminine given name commonly used in various European languages, often as a variant of Antonia.
  • A. Antonina
    Antonina was a prominent Byzantine noblewoman and influential wife of the famed general Belisarius, noted for her political acumen and close association with Empress Theodora in the 6th century.
  • B. Mária
    Mária is the Hungarian and Slovak form of the given name Mary, commonly used in Central and Eastern Europe.
  • C. Tereza Vávrová
    Tereza Vávrová is a notable bearer of the Czech surname Vávrová, recognized enough to be specifically cited in reference to the name.
  • D. Helena Davidová
    Helena Davidová is a member of Franz Kafka’s extended family, known as the daughter of his sister Ottla Kafka.
  • E. Libuše
    Libuše is a Czech opera by Bedřich Smetana, centered on the legendary princess Libuše who prophesies the glory of Prague and the Czech nation.
  • 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_69c687f2c9508190a60b9aad31d3f358 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6b047eb688190bca86be98ac25e39 completed March 27, 2026, 4:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6eefd800c8190806cf3dff204ca01 completed March 27, 2026, 8:56 p.m.
NEDg Description generation batch_69c6f0a2a150819091ec6e6d2905abcb completed March 27, 2026, 9:03 p.m.
NED2 Entity disambiguation (via description) batch_69c6f1344dd4819096c1d3e216320c4d completed March 27, 2026, 9:05 p.m.
Created at: March 27, 2026, 2:01 p.m.