Triple

T25791112
Position Surface form Disambiguated ID Type / Status
Subject Kilbrandon Commission recommendations E649549 entity
Predicate namedAfter P63 FINISHED
Object Lord Kilbrandon
Lord Kilbrandon was a Scottish judge and law lord best known for chairing the influential Kilbrandon Commission on the constitution and devolution in the United Kingdom.
E1698853 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: Lord Kilbrandon | Statement: [Kilbrandon Commission recommendations, namedAfter, Lord Kilbrandon]
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: Lord Kilbrandon
Triple: [Kilbrandon Commission recommendations, namedAfter, Lord Kilbrandon]
Generated description
Lord Kilbrandon was a Scottish judge and law lord best known for chairing the influential Kilbrandon Commission on the constitution and devolution in the United Kingdom.

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_69e7ab33e9308190afe415dc6f9e8876 completed April 21, 2026, 4:52 p.m.
NER Named-entity recognition batch_69f5feff92388190824ab9cccb0224ef completed May 2, 2026, 1:41 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10da10a6848190a25efc1f124c4144 completed May 22, 2026, 10:34 p.m.
NEDg Description generation batch_6a10dc6fb1508190a14c70bbe0302671 completed May 22, 2026, 10:45 p.m.
NED2 Entity disambiguation (via description) batch_6a10dd08394081908d41ab46ad30a279 completed May 22, 2026, 10:47 p.m.
Created at: April 22, 2026, 5:59 a.m.