Triple

T2142824
Position Surface form Disambiguated ID Type / Status
Subject Toy Story 3 E46998 entity
Predicate mainCharacter P1183 FINISHED
Object Lotso
Lotso is the strawberry-scented teddy bear who serves as the main antagonist in Pixar's animated film Toy Story 3.
 E236693 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: Lotso | Statement: [Toy Story 3, mainCharacter, Lotso]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lotso
Context triple: [Toy Story 3, mainCharacter, Lotso]
  • A. Lontzen
    Lontzen is a municipality in eastern Belgium, located in the country’s German-speaking region near the border with Germany.
  • B. Lo-Toga
    Lo-Toga is an Oceanic language spoken on the Torres Islands in northern Vanuatu.
  • C. Liluah
    Liluah is a suburban locality in the Howrah district of West Bengal, India, known for its residential areas and railway facilities near Kolkata.
  • D. Hamutal
    Hamutal was a queen of Judah, known as the mother of the last king of Judah, Zedekiah, during the final years before the Babylonian exile.
  • E. Lemi
    Lemi is a small rural municipality in southeastern Finland known for its lakes, forests, and traditional Karelian culture.
  • 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: Lotso
Triple: [Toy Story 3, mainCharacter, Lotso]
Generated description
Lotso is the strawberry-scented teddy bear who serves as the main antagonist in Pixar's animated film Toy Story 3.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lotso
Target entity description: Lotso is the strawberry-scented teddy bear who serves as the main antagonist in Pixar's animated film Toy Story 3.
  • A. Lontzen
    Lontzen is a municipality in eastern Belgium, located in the country’s German-speaking region near the border with Germany.
  • B. Lo-Toga
    Lo-Toga is an Oceanic language spoken on the Torres Islands in northern Vanuatu.
  • C. Liluah
    Liluah is a suburban locality in the Howrah district of West Bengal, India, known for its residential areas and railway facilities near Kolkata.
  • D. Hamutal
    Hamutal was a queen of Judah, known as the mother of the last king of Judah, Zedekiah, during the final years before the Babylonian exile.
  • E. Lemi
    Lemi is a small rural municipality in southeastern Finland known for its lakes, forests, and traditional Karelian culture.
  • 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_69a88a1933e0819094f18426ed74180f completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abbe206db0819095772af5358dca55 completed March 7, 2026, 5:56 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae51b63e4081908a5d87af5d17d3c4 completed March 9, 2026, 4:51 a.m.
NEDg Description generation batch_69ae52d29d708190809ee4d5047b2755 completed March 9, 2026, 4:55 a.m.
NED2 Entity disambiguation (via description) batch_69ae532ec8808190b1ecd8c4f66df30d completed March 9, 2026, 4:57 a.m.
Created at: March 4, 2026, 7:44 p.m.