Triple

T28202869
Position Surface form Disambiguated ID Type / Status
Subject Mrs Brown's Boys E716933 entity
Predicate hasCharacter P2308 FINISHED
Object Mark Brown
Mark Brown is a fictional character from the Irish-British television sitcom "Mrs Brown's Boys," which centers on the loudmouthed matriarch Agnes Brown and her family.
 E1807357 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: Mark Brown | Statement: [Mrs Brown's Boys, hasCharacter, Mark Brown]
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: Mark Brown
Triple: [Mrs Brown's Boys, hasCharacter, Mark Brown]
Generated description
Mark Brown is a fictional character from the Irish-British television sitcom "Mrs Brown's Boys," which centers on the loudmouthed matriarch Agnes Brown and her family.

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_69f6430b5fdc819080fde35da72f33c4 completed May 2, 2026, 6:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15e6b778f48190a8a2f7257757a9f7 completed May 26, 2026, 6:30 p.m.
NEDg Description generation batch_6a15e78095b48190b38f875a418159a2 completed May 26, 2026, 6:33 p.m.
NED2 Entity disambiguation (via description) batch_6a15e8241cec819092154414d2bb5cad completed May 26, 2026, 6:36 p.m.
Created at: April 27, 2026, 10:33 p.m.