Triple

T9364308
Position Surface form Disambiguated ID Type / Status
Subject district of Dachau E225361 entity
Predicate contains P35 FINISHED
Object Altomünster
Altomünster is a market town in Upper Bavaria, Germany, known for its historic monastery and rural Bavarian character.
E794711 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: Altomünster | Statement: [district of Dachau, contains, Altomünster]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Altomünster
Context triple: [district of Dachau, contains, Altomünster]
  • A. Altmünster
    Altmünster is a market town in Upper Austria, situated on the shores of Lake Traunsee and known for its scenic Alpine surroundings.
  • B. Angmering
    Angmering is a large village and civil parish in the Arun district of West Sussex, England, situated between Littlehampton and Worthing near the south coast.
  • C. Hornsberg
    Hornsberg is a waterfront residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
  • D. Alfhausen
    Alfhausen is a small municipality in Lower Saxony, Germany, known for its rural character and location within the Osnabrück region.
  • E. Philippsthal
    Philippsthal is a small municipality in the German state of Hesse, historically associated with the cadet branch of the House of Hesse known as Hesse-Philippsthal-Barchfeld.
  • 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: Altomünster
Triple: [district of Dachau, contains, Altomünster]
Generated description
Altomünster is a market town in Upper Bavaria, Germany, known for its historic monastery and rural Bavarian character.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Altomünster
Target entity description: Altomünster is a market town in Upper Bavaria, Germany, known for its historic monastery and rural Bavarian character.
  • A. Altmünster
    Altmünster is a market town in Upper Austria, situated on the shores of Lake Traunsee and known for its scenic Alpine surroundings.
  • B. Angmering
    Angmering is a large village and civil parish in the Arun district of West Sussex, England, situated between Littlehampton and Worthing near the south coast.
  • C. Hornsberg
    Hornsberg is a waterfront residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
  • D. Alfhausen
    Alfhausen is a small municipality in Lower Saxony, Germany, known for its rural character and location within the Osnabrück region.
  • E. Philippsthal
    Philippsthal is a small municipality in the German state of Hesse, historically associated with the cadet branch of the House of Hesse known as Hesse-Philippsthal-Barchfeld.
  • 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_69ca842bdd648190904131d58620d448 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd503fd7f081909655e2a880c84834 completed April 1, 2026, 5:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69d0f3f3c420819084b65fd4537aaf93 completed April 4, 2026, 11:20 a.m.
NEDg Description generation batch_69d0f619e91081909c2ec17e89376295 completed April 4, 2026, 11:29 a.m.
NED2 Entity disambiguation (via description) batch_69d0f6ce1a0c8190aca34958935e0e59 completed April 4, 2026, 11:32 a.m.
Created at: March 30, 2026, 7:42 p.m.