Triple

T26661034
Position Surface form Disambiguated ID Type / Status
Subject Mel Giedroyc E666644 entity
Predicate birthName P65 FINISHED
Object Melanie Clare Sophie Giedroyc
Melanie Clare Sophie Giedroyc is a British television presenter, actress, and comedian best known for co-hosting "The Great British Bake Off" alongside Sue Perkins.
E1734695 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: Melanie Clare Sophie Giedroyc | Statement: [Mel Giedroyc, birthName, Melanie Clare Sophie Giedroyc]
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: Melanie Clare Sophie Giedroyc
Triple: [Mel Giedroyc, birthName, Melanie Clare Sophie Giedroyc]
Generated description
Melanie Clare Sophie Giedroyc is a British television presenter, actress, and comedian best known for co-hosting "The Great British Bake Off" alongside Sue Perkins.

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_69ee9cf8c7188190b9b00270a8a89164 completed April 26, 2026, 11:17 p.m.
NER Named-entity recognition batch_69f616bf30e8819082455721548f960e completed May 2, 2026, 3:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11ec4ad388819083cb330c6e42bac2 completed May 23, 2026, 6:04 p.m.
NEDg Description generation batch_6a11ecf5d69881908edb6de497f038c9 completed May 23, 2026, 6:07 p.m.
NED2 Entity disambiguation (via description) batch_6a11edfed6288190b0c75e8c4a0216ba completed May 23, 2026, 6:12 p.m.
Created at: April 27, 2026, 2:37 a.m.