Triple

T25924718
Position Surface form Disambiguated ID Type / Status
Subject Queen of Hanover E653268 entity
Predicate positionHeldBy P8 FINISHED
Object Princess Caroline of Ansbach
Princess Caroline of Ansbach was a German-born British queen consort, wife of King George II, and an influential political figure in early 18th-century Britain.
E1741119 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: Princess Caroline of Ansbach | Statement: [Queen of Hanover, positionHeldBy, Princess Caroline of Ansbach]
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: Princess Caroline of Ansbach
Triple: [Queen of Hanover, positionHeldBy, Princess Caroline of Ansbach]
Generated description
Princess Caroline of Ansbach was a German-born British queen consort, wife of King George II, and an influential political figure in early 18th-century Britain.

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_69e7ab3eb9b881909c1390690551f868 completed April 21, 2026, 4:52 p.m.
NER Named-entity recognition batch_69f603ec76dc8190ab95147d3cf1591d completed May 2, 2026, 2:02 p.m.
NED1 Entity disambiguation (via context triple) batch_6a120915df4c819095496676b27bdc33 completed May 23, 2026, 8:07 p.m.
NEDg Description generation batch_6a120a567424819083cc2364aa6ec9da completed May 23, 2026, 8:13 p.m.
NED2 Entity disambiguation (via description) batch_6a120b5561cc81909195b6d74ec74b50 completed May 23, 2026, 8:17 p.m.
Created at: April 22, 2026, 8:35 a.m.