Triple

T5358177
Position Surface form Disambiguated ID Type / Status
Subject Mattsies E102752 entity
Predicate partOf P40 FINISHED
Object Tussenhausen
Tussenhausen is a municipality in the district of Unterallgäu in Bavaria, Germany, known for its rural character and small villages such as Mattsies.
 E527401 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: Tussenhausen | Statement: [Mattsies, partOf, Tussenhausen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tussenhausen
Context triple: [Mattsies, partOf, Tussenhausen]
  • A. Hubersdorf
    Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
  • B. Ziegenhain
    Ziegenhain is a historic town in the German state of Hesse, known for its medieval fortifications and role in regional conflicts.
  • C. Teutschenthal
    Teutschenthal is a municipality in the Saalekreis district of Saxony-Anhalt in central Germany.
  • D. Irschenhausen
    Irschenhausen is a small village in Bavaria, Germany, known in part as the place where German field marshal Erich von Manstein died.
  • E. Heinersdorf
    Heinersdorf is a residential locality in the borough of Pankow in Berlin, Germany, known for its suburban character and proximity to the city center.
  • 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: Tussenhausen
Triple: [Mattsies, partOf, Tussenhausen]
Generated description
Tussenhausen is a municipality in the district of Unterallgäu in Bavaria, Germany, known for its rural character and small villages such as Mattsies.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tussenhausen
Target entity description: Tussenhausen is a municipality in the district of Unterallgäu in Bavaria, Germany, known for its rural character and small villages such as Mattsies.
  • A. Hubersdorf
    Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
  • B. Ziegenhain
    Ziegenhain is a historic town in the German state of Hesse, known for its medieval fortifications and role in regional conflicts.
  • C. Teutschenthal
    Teutschenthal is a municipality in the Saalekreis district of Saxony-Anhalt in central Germany.
  • D. Irschenhausen
    Irschenhausen is a small village in Bavaria, Germany, known in part as the place where German field marshal Erich von Manstein died.
  • E. Heinersdorf
    Heinersdorf is a residential locality in the borough of Pankow in Berlin, Germany, known for its suburban character and proximity to the city center.
  • 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_69bd43d8f7248190b64c140734b5c9a8 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd8631ca2c8190856258bf340f6e5d completed March 20, 2026, 5:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69bfe61a2cd8819089ea67309d284be4 completed March 22, 2026, 12:52 p.m.
NEDg Description generation batch_69bfe686b99881908bb9a952d5c120c9 completed March 22, 2026, 12:54 p.m.
NED2 Entity disambiguation (via description) batch_69bfe6e8d18c8190a676e59cd16d6b12 completed March 22, 2026, 12:56 p.m.
Created at: March 20, 2026, 2:02 p.m.