Triple

T32194831
Position Surface form Disambiguated ID Type / Status
Subject Marie Louise Pelline de Dalberg E822375 entity
Predicate father P120 FINISHED
Object Emmerich Joseph von Dalberg
Emmerich Joseph von Dalberg was a German statesman and diplomat of the late 18th and early 19th centuries who served in high offices within the Holy Roman Empire and later in Napoleonic France.
E2027176 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: Emmerich Joseph von Dalberg | Statement: [Marie Louise Pelline de Dalberg, father, Emmerich Joseph von Dalberg]
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: Emmerich Joseph von Dalberg
Triple: [Marie Louise Pelline de Dalberg, father, Emmerich Joseph von Dalberg]
Generated description
Emmerich Joseph von Dalberg was a German statesman and diplomat of the late 18th and early 19th centuries who served in high offices within the Holy Roman Empire and later in Napoleonic France.

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_69f3490819cc81909bae1f8ce99423c5 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bb35e9a08190bb96bb25c32e0812 completed May 3, 2026, 3:04 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34c65a4a7c819091a10230b133c090 completed June 19, 2026, 4:32 a.m.
NEDg Description generation batch_6a34c8099fcc8190a33a3dcf68af6e3c completed June 19, 2026, 4:39 a.m.
NED2 Entity disambiguation (via description) batch_6a34c884ca608190ac64f8cf72d50d18 completed June 19, 2026, 4:41 a.m.
Created at: May 1, 2026, 12:35 a.m.