Triple

T9001620
Position Surface form Disambiguated ID Type / Status
Subject Kaguya-sama: Love Is War E215050 entity
Predicate creator P184 FINISHED
Object Aka Akasaka
Aka Akasaka is a Japanese manga artist and writer best known for creating the hit romantic comedy series "Kaguya-sama: Love Is War."
 E835328 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: Aka Akasaka | Statement: [Kaguya-sama: Love Is War, creator, Aka Akasaka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Aka Akasaka
Context triple: [Kaguya-sama: Love Is War, creator, Aka Akasaka]
  • A. Akasaka
    Akasaka is a central Tokyo district known for its business centers, upscale hotels, and vibrant nightlife.
  • B. Roppongi
    Roppongi is a central Tokyo district famous for its vibrant nightlife, international community, and major art and entertainment complexes.
  • C. Otemachi
    Otemachi is a major business district in central Tokyo known for its concentration of corporate headquarters, financial institutions, and proximity to the Imperial Palace.
  • D. Shibuya
    Shibuya is a major commercial and entertainment district in Tokyo, Japan, famous for its bustling streets, youth culture, and iconic landmarks.
  • E. Nagatacho
    Nagatacho is a central district in Tokyo, Japan, known as the political heart of the country and home to key government institutions such as the National Diet Building.
  • 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: Aka Akasaka
Triple: [Kaguya-sama: Love Is War, creator, Aka Akasaka]
Generated description
Aka Akasaka is a Japanese manga artist and writer best known for creating the hit romantic comedy series "Kaguya-sama: Love Is War."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Aka Akasaka
Target entity description: Aka Akasaka is a Japanese manga artist and writer best known for creating the hit romantic comedy series "Kaguya-sama: Love Is War."
  • A. Akasaka
    Akasaka is a central Tokyo district known for its business centers, upscale hotels, and vibrant nightlife.
  • B. Roppongi
    Roppongi is a central Tokyo district famous for its vibrant nightlife, international community, and major art and entertainment complexes.
  • C. Otemachi
    Otemachi is a major business district in central Tokyo known for its concentration of corporate headquarters, financial institutions, and proximity to the Imperial Palace.
  • D. Shibuya
    Shibuya is a major commercial and entertainment district in Tokyo, Japan, famous for its bustling streets, youth culture, and iconic landmarks.
  • E. Nagatacho
    Nagatacho is a central district in Tokyo, Japan, known as the political heart of the country and home to key government institutions such as the National Diet Building.
  • 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_69ca83a12d648190b1e4fe11e8a31890 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6956a6e08190bd3853a7c1c130eb completed April 1, 2026, 12:39 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2693f054081909fe58a252bd76226 completed April 5, 2026, 1:53 p.m.
NEDg Description generation batch_69d26db507e08190b0a94e6c3730ec19 completed April 5, 2026, 2:12 p.m.
NED2 Entity disambiguation (via description) batch_69d26e108a588190b8cdb9a496d07a82 completed April 5, 2026, 2:13 p.m.
Created at: March 30, 2026, 7:05 p.m.