Triple

T33262313
Position Surface form Disambiguated ID Type / Status
Subject Peanut E851547 entity
Predicate associatedWith P37 FINISHED
Object Walter (Jeff Dunham character)
Walter is a grumpy, sharp-tongued elderly ventriloquist dummy created and performed by comedian Jeff Dunham, known for his sarcastic rants and deadpan humor.
 E2044264 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: Walter (Jeff Dunham character) | Statement: [Peanut, associatedWith, Walter (Jeff Dunham character)]
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: Walter (Jeff Dunham character)
Triple: [Peanut, associatedWith, Walter (Jeff Dunham character)]
Generated description
Walter is a grumpy, sharp-tongued elderly ventriloquist dummy created and performed by comedian Jeff Dunham, known for his sarcastic rants and deadpan humor.

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_69f349642dac81908a37ffcc3b976a55 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6de1b945081909542b56cdf95f6a1 completed May 3, 2026, 5:33 a.m.
NED1 Entity disambiguation (via context triple) batch_6a35391830008190aecc808c80e957b2 completed June 19, 2026, 12:42 p.m.
NEDg Description generation batch_6a353ae6960081909db529aeb306427a completed June 19, 2026, 12:49 p.m.
NED2 Entity disambiguation (via description) batch_6a353ba955bc8190a11654aeedea59cd completed June 19, 2026, 12:52 p.m.
Created at: May 1, 2026, 1:32 a.m.