Triple

T30139365
Position Surface form Disambiguated ID Type / Status
Subject Kibworth Beauchamp E766083 entity
Predicate hasSecondarySchool P3445 FINISHED
Object The Kibworth School
The Kibworth School is a secondary educational institution serving students from the village of Kibworth Beauchamp and the surrounding area in Leicestershire, England.
E1904231 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: The Kibworth School | Statement: [Kibworth Beauchamp, hasSecondarySchool, The Kibworth School]
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: The Kibworth School
Triple: [Kibworth Beauchamp, hasSecondarySchool, The Kibworth School]
Generated description
The Kibworth School is a secondary educational institution serving students from the village of Kibworth Beauchamp and the surrounding area in Leicestershire, England.

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_69f2247909048190ae86c2160cf8b566 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67e86d38c8190a100b82da345b6ca completed May 2, 2026, 10:45 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27584663988190b7f6a12e81e18031 completed June 9, 2026, 12:03 a.m.
NEDg Description generation batch_6a275a7d33848190ba11aeb45c7e8b83 completed June 9, 2026, 12:12 a.m.
NED2 Entity disambiguation (via description) batch_6a275b11987081908ec648ce1eeceed3 completed June 9, 2026, 12:15 a.m.
Created at: April 29, 2026, 7:17 p.m.