Triple

T3097317
Position Surface form Disambiguated ID Type / Status
Subject Caterina E64628 entity
Predicate hasDiminutive P456 FINISHED
Object Rina
Rina is a feminine given name commonly used as a short or diminutive form of longer names such as Caterina.
 E329046 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: Rina | Statement: [Caterina, hasDiminutive, Rina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rina
Context triple: [Caterina, hasDiminutive, Rina]
  • A. Reona
    Reona is the Japanese given name of Nobel Prize–winning physicist Leo Esaki, known for his pioneering work on quantum tunneling and semiconductor devices.
  • B. Yuriko
    Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
  • C. Raisa
    Raisa Gorbacheva was the influential and highly visible wife of Soviet leader Mikhail Gorbachev, known for her intellectual background, public role, and charitable work.
  • D. Naoko
    Naoko is a central, emotionally fragile character in Haruki Murakami’s story "Norwegian Wood," whose complex relationship with the protagonist explores themes of love, loss, and mental illness.
  • E. 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.
  • 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: Rina
Triple: [Caterina, hasDiminutive, Rina]
Generated description
Rina is a feminine given name commonly used as a short or diminutive form of longer names such as Caterina.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rina
Target entity description: Rina is a feminine given name commonly used as a short or diminutive form of longer names such as Caterina.
  • A. Reona
    Reona is the Japanese given name of Nobel Prize–winning physicist Leo Esaki, known for his pioneering work on quantum tunneling and semiconductor devices.
  • B. Yuriko
    Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
  • C. Raisa
    Raisa Gorbacheva was the influential and highly visible wife of Soviet leader Mikhail Gorbachev, known for her intellectual background, public role, and charitable work.
  • D. Naoko
    Naoko is a central, emotionally fragile character in Haruki Murakami’s story "Norwegian Wood," whose complex relationship with the protagonist explores themes of love, loss, and mental illness.
  • E. 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.
  • 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_69ad857dc98481909e585dc3372e3ed5 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada23cbe3c8190b7ec5cfd464a1ca8 completed March 8, 2026, 4:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69b20f563524819084ae75c024b8291d completed March 12, 2026, 12:56 a.m.
NEDg Description generation batch_69b210ea15788190aa00dcfaabcca47a completed March 12, 2026, 1:03 a.m.
NED2 Entity disambiguation (via description) batch_69b2117703b08190acb25f3f96469a1e completed March 12, 2026, 1:05 a.m.
Created at: March 8, 2026, 3:03 p.m.