Triple

T26350420
Position Surface form Disambiguated ID Type / Status
Subject Neuberger E662885 entity
Predicate hasNotableBearer P458 FINISHED
Object Herman Neuberger
Herman Neuberger was a prominent German sports official best known for serving as president of the German Football Association (DFB) and playing a key role in organizing major international football tournaments.
E1732222 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: Herman Neuberger | Statement: [Neuberger, hasNotableBearer, Herman Neuberger]
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: Herman Neuberger
Triple: [Neuberger, hasNotableBearer, Herman Neuberger]
Generated description
Herman Neuberger was a prominent German sports official best known for serving as president of the German Football Association (DFB) and playing a key role in organizing major international football tournaments.

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_69ee8130fc44819094e5ab1da201cd7b completed April 26, 2026, 9:18 p.m.
NER Named-entity recognition batch_69f60feb75a08190be5002cfacabce78 completed May 2, 2026, 2:53 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11c7f7b0108190b7456ecbf5412db3 completed May 23, 2026, 3:29 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 26, 2026, 10:44 p.m.