Triple

T35532590

Position	Surface form	Disambiguated ID	Type / Status
Subject	Ekkeharding dynasty	`E1026841`	entity
Predicate	founder	`P104`	FINISHED
Object	Gunther of Merseburg Gunther of Merseburg was a medieval German nobleman and early regional power-broker associated with the origins of the Ekkeharding dynasty in central Germany.	`E2144462`	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: Gunther of Merseburg | Statement: [Ekkeharding dynasty, founder, Gunther of Merseburg]

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: Gunther of Merseburg
Triple: [Ekkeharding dynasty, founder, Gunther of Merseburg]

Generated description

Gunther of Merseburg was a medieval German nobleman and early regional power-broker associated with the origins of the Ekkeharding dynasty in central Germany.

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_69f76dff7e508190b28ceeee770dce23`	completed	May 3, 2026, 3:47 p.m.
NER	Named-entity recognition	`batch_69f797d2da188190813233b0489cf62d`	completed	May 3, 2026, 6:45 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a384a4b03088190b93a018bc93b53c8`	completed	June 21, 2026, 8:32 p.m.
NEDg	Description generation	`batch_6a384b14247c81909c691fbbfad22c79`	completed	June 21, 2026, 8:35 p.m.
NED2	Entity disambiguation (via description)	`batch_6a384b9c09d88190afb8dc6aeb098dbe`	completed	June 21, 2026, 8:37 p.m.

Created at: May 3, 2026, 4:04 p.m.