Triple

T20708149
Position Surface form Disambiguated ID Type / Status
Subject Hovedbanen company E508958 entity
Predicate operatedOnRoute P21808 FINISHED
Object Oslo–Eidsvoll
Oslo–Eidsvoll is a historic railway route in Norway connecting the capital city Oslo with the town of Eidsvoll.
 E1448756 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: Oslo–Eidsvoll | Statement: [Hovedbanen company, operatedOnRoute, Oslo–Eidsvoll]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Oslo–Eidsvoll
Context triple: [Hovedbanen company, operatedOnRoute, Oslo–Eidsvoll]
  • A. Eidsvoll
    Eidsvoll is a historic Norwegian town best known as the site where Norway’s constitution was drafted and signed in 1814.
  • B. Oslo
    Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
  • C. Oslo
    Oslo is a collection of shared libraries that provide common code and patterns used across various OpenStack projects.
  • D. Trondheim
    Trondheim is a historic Norwegian city in Trøndelag county, known for its medieval Nidaros Cathedral and role as a former capital of Norway.
  • E. Fredrikstad
    Fredrikstad is a coastal city in southeastern Norway known for its well-preserved fortified old town and role as a regional educational and commercial 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: Oslo–Eidsvoll
Triple: [Hovedbanen company, operatedOnRoute, Oslo–Eidsvoll]
Generated description
Oslo–Eidsvoll is a historic railway route in Norway connecting the capital city Oslo with the town of Eidsvoll.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Oslo–Eidsvoll
Target entity description: Oslo–Eidsvoll is a historic railway route in Norway connecting the capital city Oslo with the town of Eidsvoll.
  • A. Eidsvoll
    Eidsvoll is a historic Norwegian town best known as the site where Norway’s constitution was drafted and signed in 1814.
  • B. Oslo
    Oslo is the capital and largest city of Norway, known as a major cultural, economic, and governmental center.
  • C. Oslo
    Oslo is a collection of shared libraries that provide common code and patterns used across various OpenStack projects.
  • D. Trondheim
    Trondheim is a historic Norwegian city in Trøndelag county, known for its medieval Nidaros Cathedral and role as a former capital of Norway.
  • E. Fredrikstad
    Fredrikstad is a coastal city in southeastern Norway known for its well-preserved fortified old town and role as a regional educational and commercial 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_69e0b4c40ad88190b81f77695366d328 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c1952e888190877b79933970f7b0 completed April 21, 2026, 12:15 a.m.
NED1 Entity disambiguation (via context triple) batch_6a08e82e8a788190b76d1394f1b767df completed May 16, 2026, 9:57 p.m.
NEDg Description generation batch_6a08e94cc0648190b5ab35aac139f7b1 completed May 16, 2026, 10:01 p.m.
NED2 Entity disambiguation (via description) batch_6a08e9ebe3cc8190b79909df95dc0471 completed May 16, 2026, 10:04 p.m.
Created at: April 16, 2026, 12:14 p.m.