Triple

T38148562
Position Surface form Disambiguated ID Type / Status
Subject District of Grieskirchen E952687 entity
Predicate hasAdministrativeDivision P747 FINISHED
Object municipality of Peuerbach
The municipality of Peuerbach is a small Austrian town in the state of Upper Austria, known for its rural character and local historical significance.
E2258387 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: municipality of Peuerbach | Statement: [District of Grieskirchen, hasAdministrativeDivision, municipality of Peuerbach]
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: municipality of Peuerbach
Triple: [District of Grieskirchen, hasAdministrativeDivision, municipality of Peuerbach]
Generated description
The municipality of Peuerbach is a small Austrian town in the state of Upper Austria, known for its rural character and local historical significance.

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_69f76f0a67f4819080c492f61d688fcc completed May 3, 2026, 3:51 p.m.
NER Named-entity recognition batch_69fc462d13d08190a90114f40dd4b25a completed May 7, 2026, 7:58 a.m.
NED1 Entity disambiguation (via context triple) batch_6a417130a8188190a01fd5b53a251a41 completed June 28, 2026, 7:08 p.m.
NEDg Description generation batch_6a4172291ffc8190a67594e8b2cb42e0 completed June 28, 2026, 7:12 p.m.
NED2 Entity disambiguation (via description) batch_6a4172cc0a288190a82f0f22593f5861 completed June 28, 2026, 7:15 p.m.
Created at: May 3, 2026, 4:21 p.m.