Triple

T3639849
Position Surface form Disambiguated ID Type / Status
Subject Masako Owada E77159 entity
Predicate givenName P17 FINISHED
Object Masako
Masako is the Empress of Japan, a former diplomat and Harvard-educated member of the Imperial House known for her international background and public role.
 E381313 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: Masako | Statement: [Masako Owada, givenName, Masako]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Masako
Context triple: [Masako Owada, givenName, Masako]
  • A. Kazuko
    Kazuko is a Japanese feminine given name commonly borne by women, including members of the imperial family.
  • 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. 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.
  • D. Atsuko
    Atsuko is a Japanese feminine given name commonly borne by women and princesses in Japan, with meanings that vary depending on the kanji used.
  • E. Shigeko
    Shigeko is a Japanese feminine given name that has been borne by various notable women, including members of the imperial family.
  • 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: Masako
Triple: [Masako Owada, givenName, Masako]
Generated description
Masako is the Empress of Japan, a former diplomat and Harvard-educated member of the Imperial House known for her international background and public role.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Masako
Target entity description: Masako is the Empress of Japan, a former diplomat and Harvard-educated member of the Imperial House known for her international background and public role.
  • A. Kazuko
    Kazuko is a Japanese feminine given name commonly borne by women, including members of the imperial family.
  • 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. 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.
  • D. Atsuko
    Atsuko is a Japanese feminine given name commonly borne by women and princesses in Japan, with meanings that vary depending on the kanji used.
  • E. Shigeko
    Shigeko is a Japanese feminine given name that has been borne by various notable women, including members of the imperial family.
  • 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_69ad85dd0be48190b738990cb20c4731 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc32b83188190bfc0ed4dc8f66730 completed March 8, 2026, 6:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4cddcfeec8190920648ec66b2a53c completed March 14, 2026, 2:54 a.m.
NEDg Description generation batch_69b4ce60cf88819094616f7c2a604946 completed March 14, 2026, 2:56 a.m.
NED2 Entity disambiguation (via description) batch_69b4cea9c7008190a69e3fa4cdd90fd8 completed March 14, 2026, 2:57 a.m.
Created at: March 8, 2026, 3:24 p.m.