Triple

T23075153
Position Surface form Disambiguated ID Type / Status
Subject Asker E575307 entity
Predicate contains P35 FINISHED
Object Billingstad
Billingstad is a village and suburban area in Asker municipality in Viken county, Norway, known for its residential neighborhoods and commercial centers.
 E1569159 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: Billingstad | Statement: [Asker, contains, Billingstad]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Billingstad
Context triple: [Asker, contains, Billingstad]
  • A. Borgestad
    Borgestad is a village and industrial area in Telemark, Norway, known for its historic manor and former cement production.
  • B. Erstad
    Erstad is the surname of former Major League Baseball player and World Series champion Darin Erstad.
  • C. Ballstad
    Ballstad is a fishing village in Norway’s Lofoten archipelago, known for its scenic coastal landscape and traditional maritime culture.
  • D. Stadlandet
    Stadlandet is a rugged coastal peninsula in western Norway known for its exposed position to the North Atlantic and challenging maritime conditions.
  • E. Evenstad
    Evenstad is a small Norwegian locality known for hosting a campus of the Inland Norway University of Applied Sciences, particularly focused on environmental and wildlife-related studies.
  • 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: Billingstad
Triple: [Asker, contains, Billingstad]
Generated description
Billingstad is a village and suburban area in Asker municipality in Viken county, Norway, known for its residential neighborhoods and commercial centers.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Billingstad
Target entity description: Billingstad is a village and suburban area in Asker municipality in Viken county, Norway, known for its residential neighborhoods and commercial centers.
  • A. Borgestad
    Borgestad is a village and industrial area in Telemark, Norway, known for its historic manor and former cement production.
  • B. Erstad
    Erstad is the surname of former Major League Baseball player and World Series champion Darin Erstad.
  • C. Ballstad
    Ballstad is a fishing village in Norway’s Lofoten archipelago, known for its scenic coastal landscape and traditional maritime culture.
  • D. Stadlandet
    Stadlandet is a rugged coastal peninsula in western Norway known for its exposed position to the North Atlantic and challenging maritime conditions.
  • E. Evenstad
    Evenstad is a small Norwegian locality known for hosting a campus of the Inland Norway University of Applied Sciences, particularly focused on environmental and wildlife-related studies.
  • 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_69e245be28d48190ad1348d5a73db37d completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f18c62c200819099c92654493288ad completed April 29, 2026, 4:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0c15aaf74881909d2cd5f3d730f1b4 completed May 19, 2026, 7:47 a.m.
NEDg Description generation batch_6a0c15f186208190889e0766ddf03343 completed May 19, 2026, 7:49 a.m.
NED2 Entity disambiguation (via description) batch_6a0c165b716c8190b1c1e1683fe897db completed May 19, 2026, 7:50 a.m.
Created at: April 17, 2026, 3:56 p.m.