Triple

T24171950
Position Surface form Disambiguated ID Type / Status
Subject Denstone College E599160 entity
Predicate hasAlumni P51 FINISHED
Object Sir Richard Body
Sir Richard Body was a British Conservative politician and Eurosceptic Member of Parliament known for his long parliamentary career and advocacy of traditional rural and agricultural interests.
 E1621387 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: Sir Richard Body | Statement: [Denstone College, hasAlumni, Sir Richard Body]
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: Sir Richard Body
Triple: [Denstone College, hasAlumni, Sir Richard Body]
Generated description
Sir Richard Body was a British Conservative politician and Eurosceptic Member of Parliament known for his long parliamentary career and advocacy of traditional rural and agricultural interests.

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_69e288cbd62881909de32ca64a70c17b completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1e17b42948190ac539277ea50ed25 completed April 29, 2026, 10:46 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fad3d3388819096787b8f9abf2ef9 completed May 22, 2026, 1:11 a.m.
NEDg Description generation batch_6a0fae2d71448190b191a4877c698840 completed May 22, 2026, 1:15 a.m.
NED2 Entity disambiguation (via description) batch_6a0faf073c088190bbf21e4dd0434fc1 completed May 22, 2026, 1:19 a.m.
Created at: April 17, 2026, 11:33 p.m.