Triple

T28310999
Position Surface form Disambiguated ID Type / Status
Subject Cotton E713991 entity
Predicate hasNotableBearer P458 FINISHED
Object Mary Ann Cotton
Mary Ann Cotton was a 19th-century English woman widely regarded as Britain's first female serial killer, convicted of murdering multiple family members, likely by poisoning them with arsenic.
 E1811332 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: Mary Ann Cotton | Statement: [Cotton, hasNotableBearer, Mary Ann Cotton]
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: Mary Ann Cotton
Triple: [Cotton, hasNotableBearer, Mary Ann Cotton]
Generated description
Mary Ann Cotton was a 19th-century English woman widely regarded as Britain's first female serial killer, convicted of murdering multiple family members, likely by poisoning them with arsenic.

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.