Triple

T4211347
Position Surface form Disambiguated ID Type / Status
Subject Hana Benešová E93908 entity
Predicate givenName P17 FINISHED
Object Hana
Hana is a common female given name of Hebrew origin, often associated with meanings like "grace" or "favor."
 E425528 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: Hana | Statement: [Hana Benešová, givenName, Hana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hana
Context triple: [Hana Benešová, givenName, Hana]
  • A. Hana
    Hana is a small, remote town on the eastern coast of Maui, Hawaii, known for its lush landscapes, waterfalls, and the scenic Road to Hana.
  • B. Hana
    Hana is a person known primarily as the romantic partner of Kip.
  • C. Hana
    Hana is a compassionate Canadian army nurse in Michael Ondaatje's novel "The English Patient," who cares for a badly burned man in an abandoned Italian villa during World War II.
  • D. Haruko
    Haruko, better known as Empress Shōken, was the consort of Emperor Meiji and a prominent Japanese empress noted for her support of modernization and social welfare.
  • E. Takako
    Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
  • 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: Hana
Triple: [Hana Benešová, givenName, Hana]
Generated description
Hana is a common female given name of Hebrew origin, often associated with meanings like "grace" or "favor."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hana
Target entity description: Hana is a common female given name of Hebrew origin, often associated with meanings like "grace" or "favor."
  • A. Hana
    Hana is a compassionate Canadian army nurse in Michael Ondaatje's novel "The English Patient," who cares for a badly burned man in an abandoned Italian villa during World War II.
  • B. Hana
    Hana is a small, remote town on the eastern coast of Maui, Hawaii, known for its lush landscapes, waterfalls, and the scenic Road to Hana.
  • C. Hana
    Hana is a person known primarily as the romantic partner of Kip.
  • D. Haruko
    Haruko, better known as Empress Shōken, was the consort of Emperor Meiji and a prominent Japanese empress noted for her support of modernization and social welfare.
  • E. Takako
    Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
  • 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_69b3451743608190808f41d17ccf2650 completed March 12, 2026, 10:58 p.m.
NER Named-entity recognition batch_69b3481219a08190b17bf3b414bd7d4a completed March 12, 2026, 11:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5b76f07b4819097b59868af43b611 completed March 14, 2026, 7:30 p.m.
NEDg Description generation batch_69b5b80e6b6c8190a6c10c62f733f4e5 completed March 14, 2026, 7:33 p.m.
NED2 Entity disambiguation (via description) batch_69b5b881c80081909af084ff4b43b01e completed March 14, 2026, 7:35 p.m.
Created at: March 12, 2026, 11:04 p.m.