Triple

T31564127
Position Surface form Disambiguated ID Type / Status
Subject Regen E805355 entity
Predicate locatedInProtectedArea P40 FINISHED
Object Upper Bavarian Forest Nature Park
Upper Bavarian Forest Nature Park is a protected natural area in eastern Bavaria, Germany, known for its forested low mountain landscapes, diverse wildlife, and outdoor recreation opportunities.
E1984809 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: Upper Bavarian Forest Nature Park | Statement: [Regen, locatedInProtectedArea, Upper Bavarian Forest Nature Park]
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: Upper Bavarian Forest Nature Park
Triple: [Regen, locatedInProtectedArea, Upper Bavarian Forest Nature Park]
Generated description
Upper Bavarian Forest Nature Park is a protected natural area in eastern Bavaria, Germany, known for its forested low mountain landscapes, diverse wildlife, and outdoor recreation opportunities.

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_69f348d2ee94819091918d1789398c29 completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6a7cb5d7c81908ee8e5d8660212de completed May 3, 2026, 1:41 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2e8a154b70819091329a6d2885d458 completed June 14, 2026, 11:01 a.m.
NEDg Description generation batch_6a2e8b0d7b9881909941c614281a6c05 completed June 14, 2026, 11:05 a.m.
NED2 Entity disambiguation (via description) batch_6a2e8c005eb48190a28f4f07ad7c70af completed June 14, 2026, 11:09 a.m.
Created at: April 30, 2026, 10:16 p.m.