Triple

T8645542

Position	Surface form	Disambiguated ID	Type / Status
Subject	Josef Breuer	`E204763`	entity
Predicate	influencedBy	`P9`	FINISHED
Object	Johann Ritter von Oppolzer Johann Ritter von Oppolzer was a prominent 19th-century Austrian physician and clinician known for his influential work in internal medicine and medical education in Vienna.	`E2295621`	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: Johann Ritter von Oppolzer | Statement: [Josef Breuer, influencedBy, Johann Ritter von Oppolzer]

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: Johann Ritter von Oppolzer
Triple: [Josef Breuer, influencedBy, Johann Ritter von Oppolzer]

Generated description

Johann Ritter von Oppolzer was a prominent 19th-century Austrian physician and clinician known for his influential work in internal medicine and medical education in Vienna.

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_69ca834ca1c88190a11ffb0200342fac`	completed	March 30, 2026, 2:06 p.m.
NER	Named-entity recognition	`batch_69cc479999c881908c0c4e01c07d02d4`	completed	March 31, 2026, 10:15 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a81cb72a0308190a1d4576e267035d8`	completed	Aug. 16, 2026, 2:38 p.m.
NEDg	Description generation	`batch_6a81cc0c561881908985a0c22362a210`	completed	Aug. 16, 2026, 2:41 p.m.
NED2	Entity disambiguation (via description)	`batch_6a81cc5f482c81909f24d8cedca8635d`	completed	Aug. 16, 2026, 2:42 p.m.

Created at: March 30, 2026, 6:28 p.m.