Triple

T24849486
Position Surface form Disambiguated ID Type / Status
Subject Ernst Ruska E621846 entity
Predicate academicAdvisor P167 FINISHED
Object Max Knoll
Max Knoll was a German electrical engineer and physicist best known as a co-inventor and pioneer of the electron microscope.
E1654277 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: Max Knoll | Statement: [Ernst Ruska, academicAdvisor, Max Knoll]
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: Max Knoll
Triple: [Ernst Ruska, academicAdvisor, Max Knoll]
Generated description
Max Knoll was a German electrical engineer and physicist best known as a co-inventor and pioneer of the electron microscope.

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_69e2fac297e481909d3aedc75f585e42 completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f422d3d0308190b343e22170f080c3 completed May 1, 2026, 3:49 a.m.
NED1 Entity disambiguation (via context triple) batch_6a101c533540819094411da3f6178d1d completed May 22, 2026, 9:05 a.m.
NEDg Description generation batch_6a102814f838819094ed41d653039f72 completed May 22, 2026, 9:55 a.m.
NED2 Entity disambiguation (via description) batch_6a10294485508190a91d9ec391181047 completed May 22, 2026, 10 a.m.
Created at: April 18, 2026, 5:20 a.m.