Triple

T9010339
Position Surface form Disambiguated ID Type / Status
Subject Landkreis Günzburg E215451 entity
Predicate contains P35 FINISHED
Object Landensberg
Landensberg is a small municipality in the Bavarian region of southern Germany.
 E784656 NE FINISHED

How this triple was built (4 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: Landensberg | Statement: [Landkreis Günzburg, contains, Landensberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Landensberg
Context triple: [Landkreis Günzburg, contains, Landensberg]
  • A. Triesenberg
    Triesenberg is a mountainous municipality in Liechtenstein known for its traditional Walser culture and scenic alpine landscapes.
  • B. Reinsberg
    Reinsberg is a municipality in the German state of Saxony, located within the administrative district of Mittelsachsen.
  • C. Lochau
    Lochau is a locality in Germany historically noted as the place where the influential Reformation-era prince Frederick the Wise died.
  • D. Johannisberg
    Johannisberg is a prominent peak in the Austrian Alps, located in the High Tauern range near the Grossglockner.
  • E. Hornsberg
    Hornsberg is a waterfront residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Landensberg
Triple: [Landkreis Günzburg, contains, Landensberg]
Generated description
Landensberg is a small municipality in the Bavarian region of southern Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Landensberg
Target entity description: Landensberg is a small municipality in the Bavarian region of southern Germany.
  • A. Triesenberg
    Triesenberg is a mountainous municipality in Liechtenstein known for its traditional Walser culture and scenic alpine landscapes.
  • B. Reinsberg
    Reinsberg is a municipality in the German state of Saxony, located within the administrative district of Mittelsachsen.
  • C. Lochau
    Lochau is a locality in Germany historically noted as the place where the influential Reformation-era prince Frederick the Wise died.
  • D. Johannisberg
    Johannisberg is a prominent peak in the Austrian Alps, located in the High Tauern range near the Grossglockner.
  • E. Hornsberg
    Hornsberg is a waterfront residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
  • F. None of above. chosen

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_69ca83a2bf088190986ee7a8eb90407d completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc69c1571881908d0b144786b5ee1f completed April 1, 2026, 12:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69d065b8e9fc8190b1eff91f2048e771 completed April 4, 2026, 1:13 a.m.
NEDg Description generation batch_69d066f0aa588190997de81afd8dc0b5 completed April 4, 2026, 1:18 a.m.
NED2 Entity disambiguation (via description) batch_69d067e17fac819095544182f3232bf5 completed April 4, 2026, 1:22 a.m.
Created at: March 30, 2026, 7:06 p.m.