Triple

T28928029
Position Surface form Disambiguated ID Type / Status
Subject Deutschlandsberg E733703 entity
Predicate locatedInAdministrativeUnit P40 FINISHED
Object Deutschlandsberg District
Deutschlandsberg District is an administrative district in the Austrian state of Styria, known for its rural landscapes, wine-growing areas, and small towns.
E1870870 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: Deutschlandsberg District | Statement: [Deutschlandsberg, locatedInAdministrativeUnit, Deutschlandsberg District]
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: Deutschlandsberg District
Triple: [Deutschlandsberg, locatedInAdministrativeUnit, Deutschlandsberg District]
Generated description
Deutschlandsberg District is an administrative district in the Austrian state of Styria, known for its rural landscapes, wine-growing areas, and small towns.

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_69f05b0b49b08190b8994b339c7980f6 completed April 28, 2026, 7 a.m.
NER Named-entity recognition batch_69f65b50b3d081908d4164f0506236d6 completed May 2, 2026, 8:15 p.m.
NED1 Entity disambiguation (via context triple) batch_6a260bf9ccac819080a82a9a5933ee96 completed June 8, 2026, 12:25 a.m.
NEDg Description generation batch_6a2611a7743c8190a62513eb4f3a1828 completed June 8, 2026, 12:49 a.m.
NED2 Entity disambiguation (via description) batch_6a261223305481909e0befe2eda8a9c1 completed June 8, 2026, 12:51 a.m.
Created at: April 28, 2026, 8:25 a.m.