Triple

T15324419
Position Surface form Disambiguated ID Type / Status
Subject Princess Kako of Akishino E366372 entity
Predicate familyName P18 FINISHED
Object Akišino
Akišino is the family name of a branch of the Japanese imperial family to which Princess Kako belongs.
 E1150226 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: Akišino | Statement: [Princess Kako of Akishino, familyName, Akišino]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Akišino
Context triple: [Princess Kako of Akishino, familyName, Akišino]
  • A. Ust-Luga
    Ust-Luga is a major Russian Baltic Sea port town that serves as a key cargo and energy export hub for the Leningrad Oblast region.
  • B. Shakhovskoye
    Shakhovskoye is a rural locality in Russia known primarily as the birthplace of Soviet politician Mikhail Suslov.
  • C. Severodvinsk
    Severodvinsk is a Russian port city on the White Sea, known as a major center for the construction and maintenance of nuclear submarines.
  • D. Setka
    Setka was an ancient Egyptian prince of the 4th Dynasty, likely a son of Pharaoh Djedefre, known from statues and inscriptions found at Abu Rawash.
  • E. Krasnoufimsk
    Krasnoufimsk is a small historic town in Russia’s Ural region, known for its traditional architecture and role as a local administrative and cultural center.
  • 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: Akišino
Triple: [Princess Kako of Akishino, familyName, Akišino]
Generated description
Akišino is the family name of a branch of the Japanese imperial family to which Princess Kako belongs.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Akišino
Target entity description: Akišino is the family name of a branch of the Japanese imperial family to which Princess Kako belongs.
  • A. Ust-Luga
    Ust-Luga is a major Russian Baltic Sea port town that serves as a key cargo and energy export hub for the Leningrad Oblast region.
  • B. Shakhovskoye
    Shakhovskoye is a rural locality in Russia known primarily as the birthplace of Soviet politician Mikhail Suslov.
  • C. Severodvinsk
    Severodvinsk is a Russian port city on the White Sea, known as a major center for the construction and maintenance of nuclear submarines.
  • D. Setka
    Setka was an ancient Egyptian prince of the 4th Dynasty, likely a son of Pharaoh Djedefre, known from statues and inscriptions found at Abu Rawash.
  • E. Krasnoufimsk
    Krasnoufimsk is a small historic town in Russia’s Ural region, known for its traditional architecture and role as a local administrative and cultural center.
  • 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_69d85a121520819093dcce999fdefe1a completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03dd5ce0c819093c9a14de549dff6 completed April 16, 2026, 1:39 a.m.
NED1 Entity disambiguation (via context triple) batch_69fef8aaef608190bd3ec9fdd215afbb completed May 9, 2026, 9:04 a.m.
NEDg Description generation batch_69fefa435efc81908c1e88267e745cdd completed May 9, 2026, 9:11 a.m.
NED2 Entity disambiguation (via description) batch_69fefb2ee9108190b3d8633cc9713c7b completed May 9, 2026, 9:15 a.m.
Created at: April 10, 2026, 3:16 a.m.