Triple

T7709789
Position Surface form Disambiguated ID Type / Status
Subject Battle of Abensberg E174718 entity
Predicate place P373 FINISHED
Object Abensberg
Abensberg is a historic town in Bavaria, Germany, known for its medieval architecture and its role as a Napoleonic-era battlefield.
 E692006 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: Abensberg | Statement: [Battle of Abensberg, place, Abensberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Abensberg
Context triple: [Battle of Abensberg, place, Abensberg]
  • A. Wilhelmsruh
    Wilhelmsruh is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and historical ties to Berlin’s former border zone.
  • B. Ansbach
    Ansbach is a historic town in the German state of Bavaria, known as the former residence of the Margraves of Brandenburg-Ansbach.
  • C. Hildburghausen
    Hildburghausen is a town in the German state of Thuringia that historically served as the residence of the dukes of Saxe-Hildburghausen.
  • D. Stolberg
    Stolberg is a historic German town in the Harz region, known for its well-preserved medieval architecture and role in early Reformation-era history.
  • E. Altenburg
    Altenburg is a historic town in eastern Thuringia, Germany, known for its playing-card tradition and as the birthplace of the card game Skat.
  • 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: Abensberg
Triple: [Battle of Abensberg, place, Abensberg]
Generated description
Abensberg is a historic town in Bavaria, Germany, known for its medieval architecture and its role as a Napoleonic-era battlefield.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Abensberg
Target entity description: Abensberg is a historic town in Bavaria, Germany, known for its medieval architecture and its role as a Napoleonic-era battlefield.
  • A. Wilhelmsruh
    Wilhelmsruh is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and historical ties to Berlin’s former border zone.
  • B. Ansbach
    Ansbach is a historic town in the German state of Bavaria, known as the former residence of the Margraves of Brandenburg-Ansbach.
  • C. Hildburghausen
    Hildburghausen is a town in the German state of Thuringia that historically served as the residence of the dukes of Saxe-Hildburghausen.
  • D. Stolberg
    Stolberg is a historic German town in the Harz region, known for its well-preserved medieval architecture and role in early Reformation-era history.
  • E. Altenburg
    Altenburg is a historic town in eastern Thuringia, Germany, known for its playing-card tradition and as the birthplace of the card game Skat.
  • 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_69c6995b3e8c8190833108f883d5f53c completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c702ac0060819084f9c0242a5ffa9a completed March 27, 2026, 10:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69c9cd7f9b2c81908a1f77a9cc37a0be completed March 30, 2026, 1:10 a.m.
NEDg Description generation batch_69c9ce2791d88190bc48f237e134e7a9 completed March 30, 2026, 1:13 a.m.
NED2 Entity disambiguation (via description) batch_69c9ce745fdc8190843c4d48722fb263 completed March 30, 2026, 1:14 a.m.
Created at: March 27, 2026, 4:04 p.m.