Triple

T9540534
Position Surface form Disambiguated ID Type / Status
Subject Landshut (district) E230144 entity
Predicate contains P35 FINISHED
Object Altdorf (Landshut)
Altdorf (Landshut) is a municipality in Lower Bavaria, Germany, situated near the city of Landshut.
 E805362 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: Altdorf (Landshut) | Statement: [Landshut (district), contains, Altdorf (Landshut)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Altdorf (Landshut)
Context triple: [Landshut (district), contains, Altdorf (Landshut)]
  • A. Altdorf bei Nürnberg
    Altdorf bei Nürnberg is a small historic town in Bavaria, Germany, known for its former university and proximity to the city of Nuremberg.
  • B. Landshut
    Landshut is a historic Bavarian city in southeastern Germany known for its well-preserved medieval architecture and the landmark Trausnitz Castle.
  • C. Altdorf
    Altdorf is a Swiss town in the canton of Uri, known as a historic transit point through the Alps and its association with the William Tell legend.
  • D. Kempten
    Kempten is a historic town in Bavaria, Germany, considered one of the country’s oldest urban settlements and known for its location in the Allgäu region.
  • E. Kaufbeuren
    Kaufbeuren is a historic Bavarian town in southern Germany known for its well-preserved medieval old town and traditional Swabian culture.
  • 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: Altdorf (Landshut)
Triple: [Landshut (district), contains, Altdorf (Landshut)]
Generated description
Altdorf (Landshut) is a municipality in Lower Bavaria, Germany, situated near the city of Landshut.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Altdorf (Landshut)
Target entity description: Altdorf (Landshut) is a municipality in Lower Bavaria, Germany, situated near the city of Landshut.
  • A. Altdorf bei Nürnberg
    Altdorf bei Nürnberg is a small historic town in Bavaria, Germany, known for its former university and proximity to the city of Nuremberg.
  • B. Landshut
    Landshut is a historic Bavarian city in southeastern Germany known for its well-preserved medieval architecture and the landmark Trausnitz Castle.
  • C. Altdorf
    Altdorf is a Swiss town in the canton of Uri, known as a historic transit point through the Alps and its association with the William Tell legend.
  • D. Kempten
    Kempten is a historic town in Bavaria, Germany, considered one of the country’s oldest urban settlements and known for its location in the Allgäu region.
  • E. Kaufbeuren
    Kaufbeuren is a historic Bavarian town in southern Germany known for its well-preserved medieval old town and traditional Swabian culture.
  • 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_69ca847b1b3081908f72bc932c17cc41 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd98e695948190ab107fff38c57de7 completed April 1, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69d14c5da5a081909d646c69d5de8e18 completed April 4, 2026, 5:37 p.m.
NEDg Description generation batch_69d14d0c39c88190a705470104dc7b80 completed April 4, 2026, 5:40 p.m.
NED2 Entity disambiguation (via description) batch_69d14d79065081908a4e619c71e0d359 completed April 4, 2026, 5:42 p.m.
Created at: March 30, 2026, 8:01 p.m.