Triple

T31174841
Position Surface form Disambiguated ID Type / Status
Subject Regierungsbezirk Oberbayern E794714 entity
Predicate contains P35 FINISHED
Object Landkreis Fürstenfeldbruck
Landkreis Fürstenfeldbruck is a rural district in Upper Bavaria, Germany, located west of Munich and known for its mix of suburban communities, agricultural areas, and historical sites.
E1964048 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: Landkreis Fürstenfeldbruck | Statement: [Regierungsbezirk Oberbayern, contains, Landkreis Fürstenfeldbruck]
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: Landkreis Fürstenfeldbruck
Triple: [Regierungsbezirk Oberbayern, contains, Landkreis Fürstenfeldbruck]
Generated description
Landkreis Fürstenfeldbruck is a rural district in Upper Bavaria, Germany, located west of Munich and known for its mix of suburban communities, agricultural areas, and historical sites.

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_69f224d5b9708190b6ca79ad2fd3a28a completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f698b3e96081909996ecea317507a4 completed May 3, 2026, 12:37 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b1438c5308190a2f21c6aa4e52b20 completed June 11, 2026, 8:02 p.m.
NEDg Description generation batch_6a2b14fb9b308190ad263463c0d2a5f4 completed June 11, 2026, 8:05 p.m.
NED2 Entity disambiguation (via description) batch_6a2b15aa5b1c8190bc2e6437bad1cc5e completed June 11, 2026, 8:08 p.m.
Created at: April 29, 2026, 9:07 p.m.