Triple

T4734892
Position Surface form Disambiguated ID Type / Status
Subject Lake Hallwil E105099 entity
Predicate nearSettlement P3883 FINISHED
Object Mosen
Mosen is a small Swiss village in the canton of Lucerne, situated in a rural lakeside setting in central Switzerland.
 E464403 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: Mosen | Statement: [Lake Hallwil, nearSettlement, Mosen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mosen
Context triple: [Lake Hallwil, nearSettlement, Mosen]
  • A. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • B. Mora
    Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
  • C. Mora
    Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
  • D. Moudon
    Moudon is a historic town and former district capital in the canton of Vaud, Switzerland, known for its medieval old town and location in the Broye valley.
  • E. Odda
    Odda is a town in western Norway known for its dramatic fjord landscape, industrial heritage, and proximity to popular hiking destinations like Trolltunga.
  • 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: Mosen
Triple: [Lake Hallwil, nearSettlement, Mosen]
Generated description
Mosen is a small Swiss village in the canton of Lucerne, situated in a rural lakeside setting in central Switzerland.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mosen
Target entity description: Mosen is a small Swiss village in the canton of Lucerne, situated in a rural lakeside setting in central Switzerland.
  • A. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • B. Mora
    Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
  • C. Mora
    Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
  • D. Moudon
    Moudon is a historic town and former district capital in the canton of Vaud, Switzerland, known for its medieval old town and location in the Broye valley.
  • E. Odda
    Odda is a town in western Norway known for its dramatic fjord landscape, industrial heritage, and proximity to popular hiking destinations like Trolltunga.
  • 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_69bd43ee52048190b81a4f066534ffb3 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd648148b48190ae30631115339ba1 completed March 20, 2026, 3:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69be10b0b8d08190ad1cd262ee870f08 completed March 21, 2026, 3:29 a.m.
NEDg Description generation batch_69be1485bcf0819096693abc5fbf34e3 completed March 21, 2026, 3:46 a.m.
NED2 Entity disambiguation (via description) batch_69be153db02881908a28b3ede1fdcb54 completed March 21, 2026, 3:49 a.m.
Created at: March 20, 2026, 1:19 p.m.