Triple

T14486140
Position Surface form Disambiguated ID Type / Status
Subject Božena Němcová E359232 entity
Predicate givenName P17 FINISHED
Object Božena
Božena is a Czech feminine given name most famously borne by the 19th-century writer Božena Němcová, a key figure in Czech literature and national revival.
 E1100503 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: Božena | Statement: [Božena Němcová, givenName, Božena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Božena
Context triple: [Božena Němcová, givenName, Božena]
  • A. 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.
  • B. Mária
    Mária is the Hungarian and Slovak form of the given name Mary, commonly used in Central and Eastern Europe.
  • C. Zátopková
    Zátopková is the surname of Dana Zátopková, a renowned Czech javelin thrower and Olympic champion.
  • D. Zora Vesecká
    Zora Vesecká is a Czech individual whose given name is Zora, a common female name in Slavic countries.
  • E. Antónia
    Antónia is a feminine given name commonly used in various European languages, often as a variant of Antonia.
  • 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: Božena
Triple: [Božena Němcová, givenName, Božena]
Generated description
Božena is a Czech feminine given name most famously borne by the 19th-century writer Božena Němcová, a key figure in Czech literature and national revival.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Božena
Target entity description: Božena is a Czech feminine given name most famously borne by the 19th-century writer Božena Němcová, a key figure in Czech literature and national revival.
  • A. 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.
  • B. Mária
    Mária is the Hungarian and Slovak form of the given name Mary, commonly used in Central and Eastern Europe.
  • C. Zátopková
    Zátopková is the surname of Dana Zátopková, a renowned Czech javelin thrower and Olympic champion.
  • D. Zora Vesecká
    Zora Vesecká is a Czech individual whose given name is Zora, a common female name in Slavic countries.
  • E. Antónia
    Antónia is a feminine given name commonly used in various European languages, often as a variant of Antonia.
  • 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_69d8279740308190af9df93a3af8592e completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de924ee0f08190baf68318b41fa64d completed April 14, 2026, 7:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd64a925148190992101984895a20b completed May 8, 2026, 4:20 a.m.
NEDg Description generation batch_69fd65ae2ef0819091c7576b9cfe5fe2 completed May 8, 2026, 4:25 a.m.
NED2 Entity disambiguation (via description) batch_69fd66476ab88190b2d410ced33ce34b completed May 8, 2026, 4:27 a.m.
Created at: April 10, 2026, 1:20 a.m.