Triple

T32247542
Position Surface form Disambiguated ID Type / Status
Subject The Nut Job 2: Nutty by Nature E823792 entity
Predicate featuresCharacter P626 FINISHED
Object Heather Muldoon
Heather Muldoon is a young girl character in the animated film "The Nut Job 2: Nutty by Nature," known for her adventurous spirit and connection to the park animals.
E2112518 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: Heather Muldoon | Statement: [The Nut Job 2: Nutty by Nature, featuresCharacter, Heather Muldoon]
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: Heather Muldoon
Triple: [The Nut Job 2: Nutty by Nature, featuresCharacter, Heather Muldoon]
Generated description
Heather Muldoon is a young girl character in the animated film "The Nut Job 2: Nutty by Nature," known for her adventurous spirit and connection to the park animals.

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_69f3490cdda88190a9d61e11252a771f completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bc34616481908aee15d62ced6d46 completed May 3, 2026, 3:08 a.m.
NED1 Entity disambiguation (via context triple) batch_6a376f83b46c81909acd9b0d135dbc6e completed June 21, 2026, 4:58 a.m.
NEDg Description generation batch_6a376ff513648190a3fa8ef93765fd2f completed June 21, 2026, 5 a.m.
NED2 Entity disambiguation (via description) batch_6a377056e3f8819087c206896eaeab07 completed June 21, 2026, 5:02 a.m.
Created at: May 1, 2026, 12:40 a.m.