Triple

T6426069
Position Surface form Disambiguated ID Type / Status
Subject Namdalen E128060 entity
Predicate containsMunicipality P852 FINISHED
Object Lierne
Lierne is a sparsely populated municipality in Trøndelag county, Norway, known for its vast wilderness areas, national parks, and rich wildlife.
 E592933 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: Lierne | Statement: [Namdalen, containsMunicipality, Lierne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lierne
Context triple: [Namdalen, containsMunicipality, Lierne]
  • A. Liausson
    Liausson is a small commune in southern France’s Hérault department, known for its scenic setting on the shores of the artificial Lac du Salagou.
  • B. Tarsos
    Tarsos is the ancient name of the historic city of Tarsus in Cilicia, a significant cultural and commercial center in the eastern Mediterranean world.
  • C. Mora
    Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
  • D. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • E. 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.
  • 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: Lierne
Triple: [Namdalen, containsMunicipality, Lierne]
Generated description
Lierne is a sparsely populated municipality in Trøndelag county, Norway, known for its vast wilderness areas, national parks, and rich wildlife.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lierne
Target entity description: Lierne is a sparsely populated municipality in Trøndelag county, Norway, known for its vast wilderness areas, national parks, and rich wildlife.
  • A. Liausson
    Liausson is a small commune in southern France’s Hérault department, known for its scenic setting on the shores of the artificial Lac du Salagou.
  • B. Tarsos
    Tarsos is the ancient name of the historic city of Tarsus in Cilicia, a significant cultural and commercial center in the eastern Mediterranean world.
  • C. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • D. Mora
    Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
  • E. 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.
  • 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_69c00838de888190af2eec0b80495efa completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c0691f944c81909d4e5d8ef9e494b6 completed March 22, 2026, 10:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69c640e339108190bbb74c688de574cc completed March 27, 2026, 8:33 a.m.
NEDg Description generation batch_69c644a514f48190832bd628a071be46 completed March 27, 2026, 8:49 a.m.
NED2 Entity disambiguation (via description) batch_69c64527d1f88190b3b6f9a455f4ffce completed March 27, 2026, 8:51 a.m.
Created at: March 22, 2026, 4:43 p.m.