Triple

T35736161
Position Surface form Disambiguated ID Type / Status
Subject Trabrennbahn Mariendorf E1032893 entity
Predicate ownedBy P347 FINISHED
Object Berliner Trabrenn-Verein e.V.
Berliner Trabrenn-Verein e.V. is a German harness racing association best known for operating and organizing trotting races at the historic Trabrennbahn Mariendorf in Berlin.
 E2153747 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: Berliner Trabrenn-Verein e.V. | Statement: [Trabrennbahn Mariendorf, ownedBy, Berliner Trabrenn-Verein e.V.]
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: Berliner Trabrenn-Verein e.V.
Triple: [Trabrennbahn Mariendorf, ownedBy, Berliner Trabrenn-Verein e.V.]
Generated description
Berliner Trabrenn-Verein e.V. is a German harness racing association best known for operating and organizing trotting races at the historic Trabrennbahn Mariendorf in Berlin.

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_69f76e10e59081908d81ad9ce22f40b6 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7a165efc88190a02f499fc928fcf1 completed May 3, 2026, 7:26 p.m.
NED1 Entity disambiguation (via context triple) batch_6a387d21234c81909d16906798d91a87 completed June 22, 2026, 12:09 a.m.
NEDg Description generation batch_6a387dc7ab8081908f48a7e3b3777343 completed June 22, 2026, 12:11 a.m.
NED2 Entity disambiguation (via description) batch_6a3881097d988190824ae388ccb2210d completed June 22, 2026, 12:25 a.m.
Created at: May 3, 2026, 4:05 p.m.