Triple

T31026873
Position Surface form Disambiguated ID Type / Status
Subject Teesdorf E790602 entity
Predicate hasFacility P105 FINISHED
Object ÖAMTC driving technique center Teesdorf
The ÖAMTC driving technique center Teesdorf is a specialized Austrian training facility where drivers can practice and improve their driving skills and safety techniques under controlled conditions.
E1942582 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: ÖAMTC driving technique center Teesdorf | Statement: [Teesdorf, hasFacility, ÖAMTC driving technique center Teesdorf]
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: ÖAMTC driving technique center Teesdorf
Triple: [Teesdorf, hasFacility, ÖAMTC driving technique center Teesdorf]
Generated description
The ÖAMTC driving technique center Teesdorf is a specialized Austrian training facility where drivers can practice and improve their driving skills and safety techniques under controlled conditions.

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_69f224c811508190a7de096a5b1f5798 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f694be17fc8190beb0ab5831c750e4 completed May 3, 2026, 12:20 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2918488af48190983adc3fd65d162d completed June 10, 2026, 7:54 a.m.
NEDg Description generation batch_6a2918c754a081908efa5cad3cbcd61a completed June 10, 2026, 7:56 a.m.
NED2 Entity disambiguation (via description) batch_6a291aec054081908bbb56f7999c456c completed June 10, 2026, 8:06 a.m.
Created at: April 29, 2026, 8:58 p.m.