Triple

T4687855
Position Surface form Disambiguated ID Type / Status
Subject Pen15 E103963 entity
Predicate mainCharacter P1183 FINISHED
Object Anna Kone
Anna Kone is one of the two awkward, imaginative seventh-grade best friends at the center of the coming-of-age comedy series "PEN15."
E459872 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: Anna Kone | Statement: [Pen15, mainCharacter, Anna Kone]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Anna Kone
Context triple: [Pen15, mainCharacter, Anna Kone]
  • A. Anna Konkle
    Anna Konkle is an American actress, writer, and producer best known for co-creating and starring in the coming-of-age comedy series "PEN15."
  • B. Katja Koroleva
    Katja Koroleva is an American soccer referee known for officiating at the highest levels of women’s professional and international football.
  • C. Daria Kulik
    Daria Kulik is the daughter of Russian Olympic figure skating champion Ilia Kulik.
  • D. Anastasie Arapova
    Anastasie Arapova was the Russian-born first wife of Finnish military leader and statesman Carl Gustaf Emil Mannerheim.
  • E. Hanna Turchynova
    Hanna Turchynova is a Ukrainian academic and public figure, best known as the wife of former acting President of Ukraine Oleksandr Turchynov.
  • 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: Anna Kone
Triple: [Pen15, mainCharacter, Anna Kone]
Generated description
Anna Kone is one of the two awkward, imaginative seventh-grade best friends at the center of the coming-of-age comedy series "PEN15."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Anna Kone
Target entity description: Anna Kone is one of the two awkward, imaginative seventh-grade best friends at the center of the coming-of-age comedy series "PEN15."
  • A. Anna Konkle chosen
    Anna Konkle is an American actress, writer, and producer best known for co-creating and starring in the coming-of-age comedy series "PEN15."
  • B. Katja Koroleva
    Katja Koroleva is an American soccer referee known for officiating at the highest levels of women’s professional and international football.
  • C. Daria Kulik
    Daria Kulik is the daughter of Russian Olympic figure skating champion Ilia Kulik.
  • D. Anastasie Arapova
    Anastasie Arapova was the Russian-born first wife of Finnish military leader and statesman Carl Gustaf Emil Mannerheim.
  • E. Hanna Turchynova
    Hanna Turchynova is a Ukrainian academic and public figure, best known as the wife of former acting President of Ukraine Oleksandr Turchynov.
  • F. None of above.

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_69bd43debbf08190b4bc372e286ec234 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd6397f6888190a9024a51d4d34f2b completed March 20, 2026, 3:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69be39d43ffc8190b1e6caf34d5eaf0d completed March 21, 2026, 6:25 a.m.
NEDg Description generation batch_69be3d05281481909a74ffb38fb5eb31 completed March 21, 2026, 6:39 a.m.
NED2 Entity disambiguation (via description) batch_69be3dc3ce048190a725ea4b8e8a1ad4 completed March 21, 2026, 6:42 a.m.
Created at: March 20, 2026, 1:16 p.m.