Triple

T1673877
Position Surface form Disambiguated ID Type / Status
Subject Burgenlandkreis E36186 entity
Predicate contains P35 FINISHED
Object Freyburg (Unstrut)
Freyburg (Unstrut) is a historic wine-growing town in Saxony-Anhalt, Germany, renowned for its vineyards and medieval architecture along the Unstrut River.
 E189697 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: Freyburg (Unstrut) | Statement: [Burgenlandkreis, contains, Freyburg (Unstrut)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Freyburg (Unstrut)
Context triple: [Burgenlandkreis, contains, Freyburg (Unstrut)]
  • A. Weiterstadt
    Weiterstadt is a town in the German state of Hesse, located near Darmstadt and known for its residential areas and commercial centers.
  • B. Hubersdorf
    Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
  • C. Freiberg
    Freiberg is a historic mining town in eastern Germany renowned for its silver mining heritage and well-preserved medieval architecture.
  • D. Lichtenfels
    Lichtenfels is a town in the Upper Franconia region of Bavaria, Germany, known for its basket-making tradition and historic architecture.
  • E. Wittenau
    Wittenau is a locality in the Reinickendorf borough of Berlin, Germany, known primarily as a residential area with good transport connections.
  • 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: Freyburg (Unstrut)
Triple: [Burgenlandkreis, contains, Freyburg (Unstrut)]
Generated description
Freyburg (Unstrut) is a historic wine-growing town in Saxony-Anhalt, Germany, renowned for its vineyards and medieval architecture along the Unstrut River.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Freyburg (Unstrut)
Target entity description: Freyburg (Unstrut) is a historic wine-growing town in Saxony-Anhalt, Germany, renowned for its vineyards and medieval architecture along the Unstrut River.
  • A. Weiterstadt
    Weiterstadt is a town in the German state of Hesse, located near Darmstadt and known for its residential areas and commercial centers.
  • B. Hubersdorf
    Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
  • C. Freiberg
    Freiberg is a historic mining town in eastern Germany renowned for its silver mining heritage and well-preserved medieval architecture.
  • D. Lichtenfels
    Lichtenfels is a town in the Upper Franconia region of Bavaria, Germany, known for its basket-making tradition and historic architecture.
  • E. Wittenau
    Wittenau is a locality in the Reinickendorf borough of Berlin, Germany, known primarily as a residential area with good transport connections.
  • 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_69a8861286808190939afff3ce8ee31e completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa6246753081909dace4eacf9cb1c0 completed March 6, 2026, 5:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad71b341bc8190b79f76f426dfa7dd completed March 8, 2026, 12:55 p.m.
NEDg Description generation batch_69ad735efb0081909bacb7fc0f2d7cbd completed March 8, 2026, 1:02 p.m.
NED2 Entity disambiguation (via description) batch_69ad73b461148190b7b9d9d07233223b completed March 8, 2026, 1:03 p.m.
Created at: March 4, 2026, 7:29 p.m.