Triple

T30974591
Position Surface form Disambiguated ID Type / Status
Subject Duke of Cleveland E789195 entity
Predicate hasTitleHolder P1911 FINISHED
Object Harry Powlett, 4th Duke of Cleveland
Harry Powlett, 4th Duke of Cleveland, was a 19th-century British peer and politician who served as a Member of Parliament and held various court and local offices.
E1963848 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: Harry Powlett, 4th Duke of Cleveland | Statement: [Duke of Cleveland, hasTitleHolder, Harry Powlett, 4th Duke of Cleveland]
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: Harry Powlett, 4th Duke of Cleveland
Triple: [Duke of Cleveland, hasTitleHolder, Harry Powlett, 4th Duke of Cleveland]
Generated description
Harry Powlett, 4th Duke of Cleveland, was a 19th-century British peer and politician who served as a Member of Parliament and held various court and local offices.

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_69f224c4831c8190be53924ec25a150a completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f6938b41cc8190818fa0ccc7a00479 completed May 3, 2026, 12:15 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b074fb46c8190a21ca08527870c81 completed June 11, 2026, 7:06 p.m.
NEDg Description generation batch_6a2b0bf7b9788190af18130254a846b4 completed June 11, 2026, 7:26 p.m.
NED2 Entity disambiguation (via description) batch_6a2b0c519db0819093c1913497020eab completed June 11, 2026, 7:28 p.m.
Created at: April 29, 2026, 8:55 p.m.