Triple

T37533194
Position Surface form Disambiguated ID Type / Status
Subject Lara Jill Miller E933114 entity
Predicate notableRole P22 FINISHED
Object Izzy in Clifford’s Puppy Days
Izzy in Clifford’s Puppy Days is a character from the animated preschool series "Clifford’s Puppy Days," which follows Clifford’s adventures as a small puppy.
 E2230621 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: Izzy in Clifford’s Puppy Days | Statement: [Lara Jill Miller, notableRole, Izzy in Clifford’s Puppy Days]
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: Izzy in Clifford’s Puppy Days
Triple: [Lara Jill Miller, notableRole, Izzy in Clifford’s Puppy Days]
Generated description
Izzy in Clifford’s Puppy Days is a character from the animated preschool series "Clifford’s Puppy Days," which follows Clifford’s adventures as a small puppy.

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_69f76ec8862c8190bfa24145f5480642 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fba3f888ac8190b6020e1e3c3076e4 completed May 6, 2026, 8:26 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40954abdbc8190ba1e0c4543b29404 completed June 28, 2026, 3:30 a.m.
NEDg Description generation batch_6a4096691bf88190b88d47fe76576953 completed June 28, 2026, 3:35 a.m.
NED2 Entity disambiguation (via description) batch_6a4096e66a708190ac9cec52f9372283 completed June 28, 2026, 3:37 a.m.
Created at: May 3, 2026, 4:17 p.m.