Triple

T26407442
Position Surface form Disambiguated ID Type / Status
Subject Anne Stanley, Countess of Castlehaven E663871 entity
Predicate nobleTitle P914 FINISHED
Object Countess of Castlehaven
The Countess of Castlehaven was an English noblewoman whose title became historically notable due to the scandal and trial surrounding her husband, the Earl of Castlehaven, in the 17th century.
E1759054 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: Countess of Castlehaven | Statement: [Anne Stanley, Countess of Castlehaven, nobleTitle, Countess of Castlehaven]
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: Countess of Castlehaven
Triple: [Anne Stanley, Countess of Castlehaven, nobleTitle, Countess of Castlehaven]
Generated description
The Countess of Castlehaven was an English noblewoman whose title became historically notable due to the scandal and trial surrounding her husband, the Earl of Castlehaven, in the 17th century.

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_69ee883931888190901be96d75ee23cc completed April 26, 2026, 9:48 p.m.
NER Named-entity recognition batch_69f610f980788190babd4aca7dd78065 completed May 2, 2026, 2:58 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1253585cd48190b40154c6a829a606 completed May 24, 2026, 1:24 a.m.
NEDg Description generation batch_6a12540036908190876fb0c9e9737862 completed May 24, 2026, 1:27 a.m.
NED2 Entity disambiguation (via description) batch_6a1254fc697c8190baf4f8adefcea4d2 completed May 24, 2026, 1:31 a.m.
Created at: April 26, 2026, 11:36 p.m.