Triple

T26858845

Position	Surface form	Disambiguated ID	Type / Status
Subject	Alfredo Valenzuela Puelma	`E676270`	entity
Predicate	studiedUnder	`P7251`	FINISHED
Object	Ernesto Kirchbach Ernesto Kirchbach was a 19th-century German painter and art teacher who became an influential figure in Chilean art education.	`E1774104`	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: Ernesto Kirchbach | Statement: [Alfredo Valenzuela Puelma, studiedUnder, Ernesto Kirchbach]

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: Ernesto Kirchbach
Triple: [Alfredo Valenzuela Puelma, studiedUnder, Ernesto Kirchbach]

Generated description

Ernesto Kirchbach was a 19th-century German painter and art teacher who became an influential figure in Chilean art education.

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_69eee9b9d7708190a15d7485709ae981`	completed	April 27, 2026, 4:44 a.m.
NER	Named-entity recognition	`batch_69f61b9a5de88190b91ba255dc74b6fc`	completed	May 2, 2026, 3:43 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a12bbb78a28819089b380cea9a1651f`	completed	May 24, 2026, 8:49 a.m.
NEDg	Description generation	`batch_6a12bc482bb481908d121283f113dbb8`	completed	May 24, 2026, 8:52 a.m.
NED2	Entity disambiguation (via description)	`batch_6a12bcd0c164819098f637afcad01642`	completed	May 24, 2026, 8:54 a.m.

Created at: April 27, 2026, 5:23 a.m.