Triple

T28311010
Position Surface form Disambiguated ID Type / Status
Subject Cotton E713991 entity
Predicate hasNotableBearer P458 FINISHED
Object Sir John Cotton, 3rd Baronet
Sir John Cotton, 3rd Baronet, was an English landowner and politician from the prominent Cotton family who served as a Member of Parliament in the late 17th and early 18th centuries.
 E1811337 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 John Cotton, 3rd Baronet | Statement: [Cotton, hasNotableBearer, Sir John Cotton, 3rd Baronet]
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 John Cotton, 3rd Baronet
Triple: [Cotton, hasNotableBearer, Sir John Cotton, 3rd Baronet]
Generated description
Sir John Cotton, 3rd Baronet, was an English landowner and politician from the prominent Cotton family who served as a Member of Parliament in the late 17th and early 18th 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_69efb5256afc8190b9322d25c3ae6320 completed April 27, 2026, 7:12 p.m.
NER Named-entity recognition batch_69f644e1e0b48190b48f3e7b5d408dbd completed May 2, 2026, 6:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a16073aba50819091421bd46a4c2382 completed May 26, 2026, 8:48 p.m.
NEDg Description generation batch_6a161461afac81909c4f6f35530f73de completed May 26, 2026, 9:45 p.m.
NED2 Entity disambiguation (via description) batch_6a161524fd648190b932ebe251413aa3 completed May 26, 2026, 9:48 p.m.
Created at: April 27, 2026, 11:40 p.m.