Triple

T7001939
Position Surface form Disambiguated ID Type / Status
Subject Musegg Wall E162356 entity
Predicate hasPart P35 FINISHED
Object Nölliturm
Nölliturm is a historic medieval tower in Lucerne, Switzerland, known as one of the prominent fortification towers along the Musegg Wall.
 E634682 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: Nölliturm | Statement: [Musegg Wall, hasPart, Nölliturm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nölliturm
Context triple: [Musegg Wall, hasPart, Nölliturm]
  • A. Lappan tower
    Lappan tower is a historic bell tower and prominent architectural landmark in the German city of Oldenburg.
  • B. Schmalzturm
    Schmalzturm is a historic medieval tower and notable architectural landmark in the Bavarian town of Weißenburg in Bayern, Germany.
  • C. Schmalzturm
    Schmalzturm is a historic medieval tower in the Bavarian town of Landsberg am Lech, notable as a landmark of its old town fortifications.
  • D. Roter Turm
    Roter Turm is a historic medieval tower and prominent architectural landmark in the city center of Chemnitz, Germany.
  • E. Roter Turm
    Roter Turm is a historic clock and bell tower in Halle (Saale), Germany, and one of the city’s most recognizable architectural landmarks.
  • 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: Nölliturm
Triple: [Musegg Wall, hasPart, Nölliturm]
Generated description
Nölliturm is a historic medieval tower in Lucerne, Switzerland, known as one of the prominent fortification towers along the Musegg Wall.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nölliturm
Target entity description: Nölliturm is a historic medieval tower in Lucerne, Switzerland, known as one of the prominent fortification towers along the Musegg Wall.
  • A. Lappan tower
    Lappan tower is a historic bell tower and prominent architectural landmark in the German city of Oldenburg.
  • B. Schmalzturm
    Schmalzturm is a historic medieval tower and notable architectural landmark in the Bavarian town of Weißenburg in Bayern, Germany.
  • C. Schmalzturm
    Schmalzturm is a historic medieval tower in the Bavarian town of Landsberg am Lech, notable as a landmark of its old town fortifications.
  • D. Roter Turm
    Roter Turm is a historic medieval tower and prominent architectural landmark in the city center of Chemnitz, Germany.
  • E. Roter Turm
    Roter Turm is a historic clock and bell tower in Halle (Saale), Germany, and one of the city’s most recognizable architectural landmarks.
  • 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_69c68857ffc08190857dc62cd5253777 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6dc1115c48190a9363473ae21b6c1 completed March 27, 2026, 7:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69c76a310eb08190a0fc1de2814aea08 completed March 28, 2026, 5:42 a.m.
NEDg Description generation batch_69c76b1d881481908ef5a6614246ca1e completed March 28, 2026, 5:46 a.m.
NED2 Entity disambiguation (via description) batch_69c76be95ecc8190a57ff197f236d434 completed March 28, 2026, 5:49 a.m.
Created at: March 27, 2026, 2:33 p.m.