Triple

T27985244
Position Surface form Disambiguated ID Type / Status
Subject Master of the Ceremonies E706725 entity
Predicate positionHeldBy P8 FINISHED
Object Sir John Miller
Sir John Miller was a senior British courtier who served for many years as the Queen’s representative in organizing and overseeing formal royal ceremonies and state occasions.
 E1795947 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: Sir John Miller | Statement: [Master of the Ceremonies, positionHeldBy, Sir John Miller]
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: Sir John Miller
Triple: [Master of the Ceremonies, positionHeldBy, Sir John Miller]
Generated description
Sir John Miller was a senior British courtier who served for many years as the Queen’s representative in organizing and overseeing formal royal ceremonies and state occasions.

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_69ef96b8b8d88190bad5e4ae966bf14e completed April 27, 2026, 5:02 p.m.
NER Named-entity recognition batch_69f63b6bf1d881908818670a5daa816e completed May 2, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a131180eba88190af888538f3dbbc0c completed May 24, 2026, 2:56 p.m.
NEDg Description generation batch_6a1312b5baf88190a9279556df3173ab completed May 24, 2026, 3:01 p.m.
NED2 Entity disambiguation (via description) batch_6a13133814d48190991b1eaaf1e93bb7 completed May 24, 2026, 3:03 p.m.
Created at: April 27, 2026, 7:47 p.m.