Triple

T12566875
Position Surface form Disambiguated ID Type / Status
Subject Province of Westphalia E295497 entity
Predicate containsSettlement P847 FINISHED
Object Finnentrop
Finnentrop is a municipality in western Germany known for its location in the Sauerland region of North Rhine-Westphalia and its mix of rural landscapes and industrial activity.
E990676 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: Finnentrop | Statement: [Province of Westphalia, containsSettlement, Finnentrop]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Finnentrop
Context triple: [Province of Westphalia, containsSettlement, Finnentrop]
  • A. Finnås
    Finnås is a village and former church-centered parish on the island municipality of Bømlo in Vestland county, Norway.
  • B. Fenstad
    Fenstad is a village in Nes municipality in Akershus, Norway.
  • C. Bjerke
    Bjerke is a neighborhood in the Bjerke borough of Oslo, Norway, known primarily as a residential area with local services and amenities.
  • D. Flesberg
    Flesberg is a rural municipality in southeastern Norway known for its forests, traditional wooden architecture, and location in the Numedal valley.
  • E. Trondenes
    Trondenes is a historic former municipality and parish in northern Norway, known for its medieval stone church and role as an administrative center in the Harstad region.
  • 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: Finnentrop
Triple: [Province of Westphalia, containsSettlement, Finnentrop]
Generated description
Finnentrop is a municipality in western Germany known for its location in the Sauerland region of North Rhine-Westphalia and its mix of rural landscapes and industrial activity.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Finnentrop
Target entity description: Finnentrop is a municipality in western Germany known for its location in the Sauerland region of North Rhine-Westphalia and its mix of rural landscapes and industrial activity.
  • A. Finnås
    Finnås is a village and former church-centered parish on the island municipality of Bømlo in Vestland county, Norway.
  • B. Fenstad
    Fenstad is a village in Nes municipality in Akershus, Norway.
  • C. Bjerke
    Bjerke is a neighborhood in the Bjerke borough of Oslo, Norway, known primarily as a residential area with local services and amenities.
  • D. Flesberg
    Flesberg is a rural municipality in southeastern Norway known for its forests, traditional wooden architecture, and location in the Numedal valley.
  • E. Trondenes
    Trondenes is a historic former municipality and parish in northern Norway, known for its medieval stone church and role as an administrative center in the Harstad region.
  • 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_69d6ad9cac2c81908e8a7bed82d1e21d completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d954a325948190994bcfc9d571a3a8 completed April 10, 2026, 7:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f655914f908190afbebbec3cb57e73 completed May 2, 2026, 7:50 p.m.
NEDg Description generation batch_69f657e504c881909b960acc7758b39d completed May 2, 2026, 8 p.m.
NED2 Entity disambiguation (via description) batch_69f658a80fd08190b1b8c161ca6e56ec completed May 2, 2026, 8:03 p.m.
Created at: April 8, 2026, 11:49 p.m.