Triple

T28355677
Position Surface form Disambiguated ID Type / Status
Subject Neds E718218 entity
Predicate castMember P1668 FINISHED
Object Marianna Palka
Marianna Palka is a Scottish actress, writer, and director known for her work in independent film and television, including creating and starring in the film "Good Dick" and appearing in the Netflix series "GLOW."
E1849946 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: Marianna Palka | Statement: [Neds, castMember, Marianna Palka]
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: Marianna Palka
Triple: [Neds, castMember, Marianna Palka]
Generated description
Marianna Palka is a Scottish actress, writer, and director known for her work in independent film and television, including creating and starring in the film "Good Dick" and appearing in the Netflix series "GLOW."

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_69eff6ec27b481908c8d7b86c47893d9 completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64c2ccd8c819099051395954582ca completed May 2, 2026, 7:10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a253785b7848190800552f74dc28ece completed June 7, 2026, 9:19 a.m.
NEDg Description generation batch_6a253c1c5b608190820e8b032fb74e40 completed June 7, 2026, 9:38 a.m.
NED2 Entity disambiguation (via description) batch_6a253fe575c48190834250931b48111c completed June 7, 2026, 9:54 a.m.
Created at: April 28, 2026, 12:49 a.m.