Triple

T35191356
Position Surface form Disambiguated ID Type / Status
Subject Ned Schneebly E1016126 entity
Predicate livesWith P4704 FINISHED
Object Patty Di Marco
Patty Di Marco is a character from the film "School of Rock," known as the strict and controlling girlfriend of Ned Schneebly who disapproves of his and Dewey Finn’s rock music lifestyle.
E2131421 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: Patty Di Marco | Statement: [Ned Schneebly, livesWith, Patty Di Marco]
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: Patty Di Marco
Triple: [Ned Schneebly, livesWith, Patty Di Marco]
Generated description
Patty Di Marco is a character from the film "School of Rock," known as the strict and controlling girlfriend of Ned Schneebly who disapproves of his and Dewey Finn’s rock music lifestyle.

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_69f76ddd815c8190b822eea06630f9fb completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f78dc8627c8190b19f34a1019a30f1 completed May 3, 2026, 6:02 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3804008dd881908a4d002f8b43841a completed June 21, 2026, 3:32 p.m.
NEDg Description generation batch_6a3804ad333081909c330fa860f3ac3c completed June 21, 2026, 3:35 p.m.
NED2 Entity disambiguation (via description) batch_6a380651733c8190be3a7832419137da completed June 21, 2026, 3:42 p.m.
Created at: May 3, 2026, 4:02 p.m.