Triple

T20987054
Position Surface form Disambiguated ID Type / Status
Subject Tetsuji Tamayama E516917 entity
Predicate notableWork P4 FINISHED
Object Nana
"Nana" is a popular Japanese film adaptation of Ai Yazawa's manga, following the intertwined lives of two young women who share the same name and apartment in Tokyo.
E1460698 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: Nana | Statement: [Tetsuji Tamayama, notableWork, Nana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nana
Context triple: [Tetsuji Tamayama, notableWork, Nana]
  • A. Nana
    Nana is an 1880 naturalist novel by Émile Zola that follows the rise and fall of a Parisian courtesan as a critique of Second Empire society.
  • B. Nana
    "Nana" is a popular hit single by Tanzanian Bongo Flava artist Diamond Platnumz, known for its romantic theme and widespread success across East Africa.
  • C. Nana
    Nana is a figure in Phrygian mythology, often regarded as the mother of the vegetation god Attis and associated with themes of miraculous birth and fertility.
  • D. Nana
    Nana is an Indian honorific title commonly used to respectfully address an elder man or grandfather.
  • E. Nana
    Nana is a person or character associated with Louiset, likely within a shared narrative, social, or creative context.
  • 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: Nana
Triple: [Tetsuji Tamayama, notableWork, Nana]
Generated description
"Nana" is a popular Japanese film adaptation of Ai Yazawa's manga, following the intertwined lives of two young women who share the same name and apartment in Tokyo.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Nana
Target entity description: "Nana" is a popular Japanese film adaptation of Ai Yazawa's manga, following the intertwined lives of two young women who share the same name and apartment in Tokyo.
  • A. Nana
    Nana is a 1981 French television adaptation of Émile Zola’s novel, depicting the rise and fall of a Parisian courtesan in the late 19th century.
  • B. Nana
    Nana is a tough, elderly New Yorker from the Madagascar film series known for her surprising strength and comically aggressive encounters with Alex the Lion and his friends.
  • C. Nana
    Nana is a person or character associated with Louiset, likely within a shared narrative, social, or creative context.
  • D. Nana
    "Nana" is a popular hit single by Tanzanian Bongo Flava artist Diamond Platnumz, known for its romantic theme and widespread success across East Africa.
  • E. Nana
    Nana is an 1880 naturalist novel by Émile Zola that follows the rise and fall of a Parisian courtesan as a critique of Second Empire society.
  • 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_69e0b4ffac148190bbade9f0eceb660b completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6fbe3fbac819086d3079aaddca5b1 completed April 21, 2026, 4:24 a.m.
NED1 Entity disambiguation (via context triple) batch_6a092fc795ec81909f9f96498f7b5f3a completed May 17, 2026, 3:02 a.m.
NEDg Description generation batch_6a0930a5cffc8190a7d1cc19e5cacf49 completed May 17, 2026, 3:06 a.m.
NED2 Entity disambiguation (via description) batch_6a093133c77c8190a9e0326980c7717f completed May 17, 2026, 3:08 a.m.
Created at: April 16, 2026, 1:49 p.m.