Triple

T28203138
Position Surface form Disambiguated ID Type / Status
Subject Psychoville E716941 entity
Predicate hasCharacter P2308 FINISHED
Object Maureen Sowerbutts
Maureen Sowerbutts is a timid, middle-aged serial killer and overprotective mother featured as one of the darkly comic central characters in the British TV series "Psychoville."
E1935806 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: Maureen Sowerbutts | Statement: [Psychoville, hasCharacter, Maureen Sowerbutts]
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: Maureen Sowerbutts
Triple: [Psychoville, hasCharacter, Maureen Sowerbutts]
Generated description
Maureen Sowerbutts is a timid, middle-aged serial killer and overprotective mother featured as one of the darkly comic central characters in the British TV series "Psychoville."

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_69efd6b826908190857e6e7dad74ed93 completed April 27, 2026, 9:35 p.m.
NER Named-entity recognition batch_69f6430c4510819089589fec7d1a01e6 completed May 2, 2026, 6:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a28c7a8258881909e6005d375901c65 completed June 10, 2026, 2:10 a.m.
NEDg Description generation batch_6a28ca5ca8dc81909225215a8a02bf6f completed June 10, 2026, 2:22 a.m.
NED2 Entity disambiguation (via description) batch_6a28cc7003c081908373122f59284b68 completed June 10, 2026, 2:31 a.m.
Created at: April 27, 2026, 10:33 p.m.