Triple

T24030171
Position Surface form Disambiguated ID Type / Status
Subject Kreis Zittau E595078 entity
Predicate successorAdministrativeUnit P6576 FINISHED
Object Landkreis Löbau-Zittau
Landkreis Löbau-Zittau was a former rural district in the Free State of Saxony in eastern Germany, located along the borders with Poland and the Czech Republic.
E595078 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 Löbau-Zittau | Statement: [Kreis Zittau, successorAdministrativeUnit, Landkreis Löbau-Zittau]
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 Löbau-Zittau
Triple: [Kreis Zittau, successorAdministrativeUnit, Landkreis Löbau-Zittau]
Generated description
Landkreis Löbau-Zittau was a former rural district in the Free State of Saxony in eastern Germany, located along the borders with Poland and the Czech Republic.

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_69e288bf45f08190a1b6ed8cd0b9e86b completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1d76fbedc8190a2f936729cb69993 completed April 29, 2026, 10:03 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fad03ec3881909043e801e083c4a6 completed May 22, 2026, 1:10 a.m.
NEDg Description generation batch_6a0fae10893c819092a3ecd95b6b9198 completed May 22, 2026, 1:14 a.m.
NED2 Entity disambiguation (via description) batch_6a0faf345eac8190b8a648c3add470bd completed May 22, 2026, 1:19 a.m.
Created at: April 17, 2026, 9:55 p.m.