Triple

T36415104
Position Surface form Disambiguated ID Type / Status
Subject Groom of the Stool E896979 entity
Predicate positionHeldBy P8 FINISHED
Object Sir John Cheke
Sir John Cheke was a 16th-century English classical scholar and royal tutor to King Edward VI who became a prominent figure in the English Reformation and court politics.
 E2181661 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 Cheke | Statement: [Groom of the Stool, positionHeldBy, Sir John Cheke]
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 Cheke
Triple: [Groom of the Stool, positionHeldBy, Sir John Cheke]
Generated description
Sir John Cheke was a 16th-century English classical scholar and royal tutor to King Edward VI who became a prominent figure in the English Reformation and court politics.

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_69f76e54ce408190849acc3f7758937c completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7bd33c0a08190a6297c7b9b047e7d completed May 3, 2026, 9:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39b44fa3508190b847303ff5c0b464 completed June 22, 2026, 10:16 p.m.
NEDg Description generation batch_6a39b61fe0f8819083be78e09186c2d0 completed June 22, 2026, 10:24 p.m.
NED2 Entity disambiguation (via description) batch_6a39b6bb50a88190ad123d3823585299 completed June 22, 2026, 10:27 p.m.
Created at: May 3, 2026, 4:10 p.m.