Triple

T5899087
Position Surface form Disambiguated ID Type / Status
Subject Höchst E131174 entity
Predicate knownFor P22 FINISHED
Object Altstadt
Altstadt is the historic old town district of Höchst, characterized by its preserved medieval architecture and traditional charm.
 E554615 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: Altstadt | Statement: [Höchst, knownFor, Altstadt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Altstadt
Context triple: [Höchst, knownFor, Altstadt]
  • A. Altstadt
    Altstadt is the historic old town district of Dresden, Germany, known for its baroque architecture and major cultural landmarks.
  • B. Altstadt
    Altstadt is the historic old town of Zürich, Switzerland, known for its medieval streets, preserved architecture, and cultural landmarks along the Limmat River.
  • C. Altstadt
    Altstadt is the historic old town of Salzburg, Austria, renowned for its well-preserved baroque architecture and status as a UNESCO World Heritage Site.
  • D. Altstadt
    Altstadt is the historic old town of Düsseldorf, Germany, known for its dense concentration of bars, traditional breweries, and cultural landmarks along the Rhine River.
  • E. Old Town (Altstadt)
    Old Town (Altstadt) is Cologne’s historic city center, known for its narrow cobbled streets, traditional houses, and numerous breweries and pubs near the Rhine.
  • 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: Altstadt
Triple: [Höchst, knownFor, Altstadt]
Generated description
Altstadt is the historic old town district of Höchst, characterized by its preserved medieval architecture and traditional charm.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Altstadt
Target entity description: Altstadt is the historic old town district of Höchst, characterized by its preserved medieval architecture and traditional charm.
  • A. Altstadt
    Altstadt is the historic old town of Salzburg, Austria, renowned for its well-preserved baroque architecture and status as a UNESCO World Heritage Site.
  • B. Altstadt
    Altstadt is the historic old town of Düsseldorf, Germany, known for its dense concentration of bars, traditional breweries, and cultural landmarks along the Rhine River.
  • C. Altstadt
    Altstadt is the historic old town district of Dresden, Germany, known for its baroque architecture and major cultural landmarks.
  • D. Altstadt
    Altstadt is the historic old town of Zürich, Switzerland, known for its medieval streets, preserved architecture, and cultural landmarks along the Limmat River.
  • E. Old Town (Altstadt)
    Old Town (Altstadt) is Cologne’s historic city center, known for its narrow cobbled streets, traditional houses, and numerous breweries and pubs near the Rhine.
  • 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_69c0b15df86481908b59717b9de63655 completed March 23, 2026, 3:19 a.m.
NEDg Description generation batch_69c0b27438a08190ab6b72c8fd682bf6 completed March 23, 2026, 3:24 a.m.
NED2 Entity disambiguation (via description) batch_69c0b309a70081908ad3e819879b17e4 completed March 23, 2026, 3:27 a.m.
Created at: March 22, 2026, 3:58 p.m.