Triple

T37894038

Position	Surface form	Disambiguated ID	Type / Status
Subject	Ruth Brown	`E945225`	entity
Predicate	notableWork	`P4`	FINISHED
Object	“(Mama) He Treats Your Daughter Mean” “(Mama) He Treats Your Daughter Mean” is a 1953 rhythm and blues hit single by Ruth Brown that became one of her signature songs and a classic of early R&B.	`E2246805`	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: “(Mama) He Treats Your Daughter Mean” | Statement: [Ruth Brown, notableWork, “(Mama) He Treats Your Daughter Mean”]

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: “(Mama) He Treats Your Daughter Mean”
Triple: [Ruth Brown, notableWork, “(Mama) He Treats Your Daughter Mean”]

Generated description

“(Mama) He Treats Your Daughter Mean” is a 1953 rhythm and blues hit single by Ruth Brown that became one of her signature songs and a classic of early R&B.

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_69f76ef0e8708190987c7254ed8c7abe`	completed	May 3, 2026, 3:51 p.m.
NER	Named-entity recognition	`batch_69fbbd388acc8190ad3e1ab9a542d9ca`	completed	May 6, 2026, 10:14 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a410437a1b881908b856bad717d7bbf`	completed	June 28, 2026, 11:23 a.m.
NEDg	Description generation	`batch_6a4104c79fb0819084b62acaa5ae7237`	completed	June 28, 2026, 11:25 a.m.
NED2	Entity disambiguation (via description)	`batch_6a4105a3a4308190af9513b596d0e4f2`	completed	June 28, 2026, 11:29 a.m.

Created at: May 3, 2026, 4:19 p.m.