Triple

T25856511
Position Surface form Disambiguated ID Type / Status
Subject Jahn E651362 entity
Predicate hasNotableBearer P458 FINISHED
Object Hans Max Jahn
Hans Max Jahn was a German chemist known for his contributions to physical chemistry in the late 19th and early 20th centuries.
E2288935 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: Hans Max Jahn | Statement: [Jahn, hasNotableBearer, Hans Max Jahn]
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: Hans Max Jahn
Triple: [Jahn, hasNotableBearer, Hans Max Jahn]
Generated description
Hans Max Jahn was a German chemist known for his contributions to physical chemistry in the late 19th and early 20th centuries.

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_69e7ab39035c8190be15c8aaee1bb858 completed April 21, 2026, 4:52 p.m.
NER Named-entity recognition batch_69f60268b1388190b38c6d70cd028ecf completed May 2, 2026, 1:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a5aee33e67c8190bd013372c82aa181 completed July 18, 2026, 3:08 a.m.
NEDg Description generation batch_6a5aef0ba0888190a99f4487bec3681d completed July 18, 2026, 3:12 a.m.
NED2 Entity disambiguation (via description) batch_6a5aefb98eb08190abd4a73ec5a055ac completed July 18, 2026, 3:15 a.m.
Created at: April 22, 2026, 8 a.m.