Triple

T5899106
Position Surface form Disambiguated ID Type / Status
Subject Höchst E131174 entity
Predicate adjacentTo P224 FINISHED
Object Sossenheim
Sossenheim is a district in the west of Frankfurt am Main, Germany, known for its residential character and proximity to the Main River industrial areas.
 E585453 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: Sossenheim | Statement: [Höchst, adjacentTo, Sossenheim]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sossenheim
Context triple: [Höchst, adjacentTo, Sossenheim]
  • A. Hersbruck
    Hersbruck is a small historic town in the Franconian region of Bavaria, Germany, known for its picturesque setting in the Pegnitz Valley and traditional Bavarian architecture.
  • B. Schaafheim
    Schaafheim is a municipality in the state of Hesse in central Germany.
  • C. Landsberg
    Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
  • D. Hirschau
    Hirschau is a riverside meadow and recreational area within Munich’s Englischer Garten, known for its open green spaces, beer gardens, and walking paths along the Isar.
  • E. Schlettstadt
    Schlettstadt, now known as Sélestat, is a historic town in the Alsace region of northeastern France noted for its medieval architecture and humanist heritage.
  • 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: Sossenheim
Triple: [Höchst, adjacentTo, Sossenheim]
Generated description
Sossenheim is a district in the west of Frankfurt am Main, Germany, known for its residential character and proximity to the Main River industrial areas.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sossenheim
Target entity description: Sossenheim is a district in the west of Frankfurt am Main, Germany, known for its residential character and proximity to the Main River industrial areas.
  • A. Hersbruck
    Hersbruck is a small historic town in the Franconian region of Bavaria, Germany, known for its picturesque setting in the Pegnitz Valley and traditional Bavarian architecture.
  • B. Schaafheim
    Schaafheim is a municipality in the state of Hesse in central Germany.
  • C. Landsberg
    Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
  • D. Hirschau
    Hirschau is a riverside meadow and recreational area within Munich’s Englischer Garten, known for its open green spaces, beer gardens, and walking paths along the Isar.
  • E. Schlettstadt
    Schlettstadt, now known as Sélestat, is a historic town in the Alsace region of northeastern France noted for its medieval architecture and humanist heritage.
  • 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_69c00857439c819095950754176aa58a completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c036f7b3f48190a499d43f8ffb2fa7 completed March 22, 2026, 6:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69c603c7dac48190b742e9e5430b8bde completed March 27, 2026, 4:12 a.m.
NEDg Description generation batch_69c6047bff8c81908cfcaef23b78e022 completed March 27, 2026, 4:15 a.m.
NED2 Entity disambiguation (via description) batch_69c604e8d2748190b9a0505f6803ad07 completed March 27, 2026, 4:17 a.m.
Created at: March 22, 2026, 3:58 p.m.