Triple

T9001621
Position Surface form Disambiguated ID Type / Status
Subject Kaguya-sama: Love Is War E215050 entity
Predicate author P4 FINISHED
Object Aka Akasaka E835328 NE FINISHED

How this triple was built (2 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, author, 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, author, Aka Akasaka]
  • A. Aka Akasaka chosen
    Aka Akasaka is a Japanese manga artist and writer best known for creating the hit romantic comedy series "Kaguya-sama: Love Is War."
  • B. Akasaka
    Akasaka is a central Tokyo district known for its business centers, upscale hotels, and vibrant nightlife.
  • C. Roppongi
    Roppongi is a central Tokyo district famous for its vibrant nightlife, international community, and major art and entertainment complexes.
  • D. 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.
  • E. Shibuya
    Shibuya is a major commercial and entertainment district in Tokyo, Japan, famous for its bustling streets, youth culture, and iconic landmarks.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d28161b6ec8190ab91f7b00995e889 completed April 5, 2026, 3:36 p.m.
Created at: March 30, 2026, 7:05 p.m.