Triple

T26585966
Position Surface form Disambiguated ID Type / Status
Subject Field Marshal Laudon E667210 entity
Predicate familyName P18 FINISHED
Object von Laudon
Von Laudon is the noble family name of Austrian field marshal Ernst Gideon von Laudon, a prominent 18th-century military commander in the Habsburg Monarchy.
 E1732285 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: von Laudon | Statement: [Field Marshal Laudon, familyName, von Laudon]
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: von Laudon
Triple: [Field Marshal Laudon, familyName, von Laudon]
Generated description
Von Laudon is the noble family name of Austrian field marshal Ernst Gideon von Laudon, a prominent 18th-century military commander in the Habsburg Monarchy.

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_69ee9cfb7e548190b60a9031182f5a7e completed April 26, 2026, 11:17 p.m.
NER Named-entity recognition batch_69f615214310819095f1f8064313240a completed May 2, 2026, 3:15 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11c83713648190958104c9f812f6ab completed May 23, 2026, 3:31 p.m.
NEDg Description generation batch_6a11ca4e5a58819081ded261719245c6 completed May 23, 2026, 3:39 p.m.
NED2 Entity disambiguation (via description) batch_6a11cac2048c81908007d7be9e205599 completed May 23, 2026, 3:41 p.m.
Created at: April 27, 2026, 2:05 a.m.