Triple

T31131314

Position	Surface form	Disambiguated ID	Type / Status
Subject	The Baker and the Beauty	`E793513`	entity
Predicate	starred	`P5563`	FINISHED
Object	Belissa Escobedo Belissa Escobedo is an American actress known for her roles in television and film, including prominent parts in series like "The Baker and the Beauty" and various contemporary dramas and comedies.	`E2246288`	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: Belissa Escobedo | Statement: [The Baker and the Beauty, starred, Belissa Escobedo]

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: Belissa Escobedo
Triple: [The Baker and the Beauty, starred, Belissa Escobedo]

Generated description

Belissa Escobedo is an American actress known for her roles in television and film, including prominent parts in series like "The Baker and the Beauty" and various contemporary dramas and comedies.

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_69f224d1701c819094f429798290e361`	completed	April 29, 2026, 3:33 p.m.
NER	Named-entity recognition	`batch_69f69740a0588190aad511f5f27d0aea`	completed	May 3, 2026, 12:30 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a4103fc6b2481908d85a6d286b90923`	completed	June 28, 2026, 11:22 a.m.
NEDg	Description generation	`batch_6a4104c79fb0819084b62acaa5ae7237`	completed	June 28, 2026, 11:25 a.m.
NED2	Entity disambiguation (via description)	`batch_6a41059ef42c81909a94722a1563fcd1`	completed	June 28, 2026, 11:29 a.m.

Created at: April 29, 2026, 9:05 p.m.