Triple

T1518485
Position Surface form Disambiguated ID Type / Status
Subject Frozen E32172 entity
Predicate mainCharacter P1183 FINISHED
Object Sven
Sven is the lovable reindeer companion in Disney's animated film "Frozen," known for his close bond with Kristoff and his expressive, dog-like personality.
 E185432 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: Sven | Statement: [Frozen, mainCharacter, Sven]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sven
Context triple: [Frozen, mainCharacter, Sven]
  • A. Johan
    Johan is the given first name of J. Erik Jonsson, an American businessman and philanthropist who co-founded Texas Instruments and served as mayor of Dallas.
  • B. Andreas
    Andreas is a masculine given name of Greek origin, commonly used in various European and international cultures.
  • C. Lars
    Lars is a masculine given name of Scandinavian origin, commonly used in countries such as Norway, Sweden, and Denmark.
  • D. Mikael
    Mikael is a masculine given name commonly used in Scandinavian and Finnish cultures, equivalent to Michael.
  • E. Svante
    Svante is the given name of Swedish geneticist Svante Pääbo, a Nobel Prize–winning pioneer in the field of paleogenomics.
  • 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: Sven
Triple: [Frozen, mainCharacter, Sven]
Generated description
Sven is the lovable reindeer companion in Disney's animated film "Frozen," known for his close bond with Kristoff and his expressive, dog-like personality.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sven
Target entity description: Sven is the lovable reindeer companion in Disney's animated film "Frozen," known for his close bond with Kristoff and his expressive, dog-like personality.
  • A. Johan
    Johan is the given first name of J. Erik Jonsson, an American businessman and philanthropist who co-founded Texas Instruments and served as mayor of Dallas.
  • B. Andreas
    Andreas is a masculine given name of Greek origin, commonly used in various European and international cultures.
  • C. Lars
    Lars is a masculine given name of Scandinavian origin, commonly used in countries such as Norway, Sweden, and Denmark.
  • D. Mikael
    Mikael is a masculine given name commonly used in Scandinavian and Finnish cultures, equivalent to Michael.
  • E. Svante
    Svante is the given name of Swedish geneticist Svante Pääbo, a Nobel Prize–winning pioneer in the field of paleogenomics.
  • 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_69a885e8caf88190a5fbb6159ce87786 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a907ed44ac8190953e428c831e24df completed March 5, 2026, 4:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad58b487c08190bb2b1c259bd39db0 completed March 8, 2026, 11:08 a.m.
NEDg Description generation batch_69ad5c73d7fc8190a2983c2a33b2ce01 completed March 8, 2026, 11:24 a.m.
NED2 Entity disambiguation (via description) batch_69ad5cf99a908190b8af9e7bb1949d80 completed March 8, 2026, 11:26 a.m.
Created at: March 4, 2026, 7:26 p.m.