Triple

T14601470
Position Surface form Disambiguated ID Type / Status
Subject Stange E342714 entity
Predicate administrativeCentre P1474 FINISHED
Object Stangebyen
Stangebyen is a village in Innlandet county, Norway, serving as the main local hub for services, commerce, and administration in the Stange area.
E1108818 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: Stangebyen | Statement: [Stange, administrativeCentre, Stangebyen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Stangebyen
Context triple: [Stange, administrativeCentre, Stangebyen]
  • A. Teigebyen
    Teigebyen is a village in Viken county, Norway, serving as the main local hub for municipal services and community life in Nannestad.
  • B. Ryggebyen
    Ryggebyen is a small urban settlement in Østfold, Norway, functioning as the main local hub for services and administration in the Rygge area.
  • C. Homansbyen
    Homansbyen is a central residential neighborhood in Oslo, Norway, known for its 19th-century architecture and proximity to major commercial and cultural areas like Majorstuen.
  • D. Edsbyn
    Edsbyn is a small town in Gävleborg County, Sweden, known for its bandy team and role as a local industrial and service center.
  • E. Stålstaden
    Stålstaden is a Swedish city nickname referring to Eskilstuna’s historic role as a major steel and metalworking industrial center.
  • 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: Stangebyen
Triple: [Stange, administrativeCentre, Stangebyen]
Generated description
Stangebyen is a village in Innlandet county, Norway, serving as the main local hub for services, commerce, and administration in the Stange area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Stangebyen
Target entity description: Stangebyen is a village in Innlandet county, Norway, serving as the main local hub for services, commerce, and administration in the Stange area.
  • A. Teigebyen
    Teigebyen is a village in Viken county, Norway, serving as the main local hub for municipal services and community life in Nannestad.
  • B. Ryggebyen
    Ryggebyen is a small urban settlement in Østfold, Norway, functioning as the main local hub for services and administration in the Rygge area.
  • C. Homansbyen
    Homansbyen is a central residential neighborhood in Oslo, Norway, known for its 19th-century architecture and proximity to major commercial and cultural areas like Majorstuen.
  • D. Edsbyn
    Edsbyn is a small town in Gävleborg County, Sweden, known for its bandy team and role as a local industrial and service center.
  • E. Stålstaden
    Stålstaden is a Swedish city nickname referring to Eskilstuna’s historic role as a major steel and metalworking industrial center.
  • 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_69d822dec68081908c2553145c4051dc completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb438748081908020ce04b869866a completed April 14, 2026, 9:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd94cc9fbc819090ae4efe9bc618aa completed May 8, 2026, 7:46 a.m.
NEDg Description generation batch_69fd973985b881908f0c2fd201db8104 completed May 8, 2026, 7:56 a.m.
NED2 Entity disambiguation (via description) batch_69fd9843857c819089eb96564b8a9503 completed May 8, 2026, 8:01 a.m.
Created at: April 10, 2026, 1:25 a.m.