Triple

T7782628
Position Surface form Disambiguated ID Type / Status
Subject Saale-Holzland-Kreis E221559 entity
Predicate contains P35 FINISHED
Object Kahla
Kahla is a small town in the Saale valley of eastern Thuringia, Germany, known for its porcelain manufacturing industry.
 E693402 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: Kahla | Statement: [Saale-Holzland-Kreis, contains, Kahla]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kahla
Context triple: [Saale-Holzland-Kreis, contains, Kahla]
  • A. Kepez
    Kepez is a populous district and municipality within the city of Antalya in southern Turkey, known for its residential areas and growing urban infrastructure.
  • B. Kalkan
    Kalkan is a picturesque seaside town on Turkey’s Mediterranean coast, known for its historic architecture, steep cobbled streets, and upscale tourism.
  • C. Hereti
    Hereti was a historical region and principality in eastern Georgia, later often associated with Kakheti, known for its early Christian heritage and strategic location in the Caucasus.
  • D. Karlaplan
    Karlaplan is a prominent circular plaza and park with a central fountain in the Östermalm district of Stockholm, Sweden.
  • E. Zliten
    Zliten is a coastal city in northwestern Libya known for its historical Islamic architecture and location between Misrata and Al Khums.
  • 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: Kahla
Triple: [Saale-Holzland-Kreis, contains, Kahla]
Generated description
Kahla is a small town in the Saale valley of eastern Thuringia, Germany, known for its porcelain manufacturing industry.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kahla
Target entity description: Kahla is a small town in the Saale valley of eastern Thuringia, Germany, known for its porcelain manufacturing industry.
  • A. Kepez
    Kepez is a populous district and municipality within the city of Antalya in southern Turkey, known for its residential areas and growing urban infrastructure.
  • B. Kalkan
    Kalkan is a picturesque seaside town on Turkey’s Mediterranean coast, known for its historic architecture, steep cobbled streets, and upscale tourism.
  • C. Hereti
    Hereti was a historical region and principality in eastern Georgia, later often associated with Kakheti, known for its early Christian heritage and strategic location in the Caucasus.
  • D. Karlaplan
    Karlaplan is a prominent circular plaza and park with a central fountain in the Östermalm district of Stockholm, Sweden.
  • E. Zliten
    Zliten is a coastal city in northwestern Libya known for its historical Islamic architecture and location between Misrata and Al Khums.
  • 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_69ca83ebbef881909ac47f789145fef7 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cadf1f9c648190ac2b06d0d54035ea completed March 30, 2026, 8:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69caf5e400d881909d6cdeb7eaac3a59 completed March 30, 2026, 10:15 p.m.
NEDg Description generation batch_69caf81ebde881909bd131da8987b449 completed March 30, 2026, 10:24 p.m.
NED2 Entity disambiguation (via description) batch_69cafa013f348190a2067dee4a0c8c40 completed March 30, 2026, 10:32 p.m.
Created at: March 30, 2026, 4:21 p.m.