Triple

T33801408
Position Surface form Disambiguated ID Type / Status
Subject Baron Dacre E866227 entity
Predicate hasTitleHolder P1911 FINISHED
Object Thomas Henry Brand, 23rd Baron Dacre
Thomas Henry Brand, 23rd Baron Dacre, was a British peer and landowner who held the historic Dacre barony in the late 19th and early 20th centuries.
E2072749 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: Thomas Henry Brand, 23rd Baron Dacre | Statement: [Baron Dacre, hasTitleHolder, Thomas Henry Brand, 23rd Baron Dacre]
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: Thomas Henry Brand, 23rd Baron Dacre
Triple: [Baron Dacre, hasTitleHolder, Thomas Henry Brand, 23rd Baron Dacre]
Generated description
Thomas Henry Brand, 23rd Baron Dacre, was a British peer and landowner who held the historic Dacre barony in the late 19th and early 20th centuries.

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_69f3499057fc81909d862b1309a3bd71 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6ff4ad30c8190b1eabaa000a6bf77 completed May 3, 2026, 7:54 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36822a78088190b3905b97a7f56794 completed June 20, 2026, 12:06 p.m.
NEDg Description generation batch_6a3682f4f07881909b9ba46c003191cc completed June 20, 2026, 12:09 p.m.
NED2 Entity disambiguation (via description) batch_6a3683779ad4819092fd470251b6db4f completed June 20, 2026, 12:11 p.m.
Created at: May 1, 2026, 1:46 a.m.