Triple

T1356117
Position Surface form Disambiguated ID Type / Status
Subject Port of Oslo E28991 entity
Predicate hasPart P35 FINISHED
Object Sørenga terminal
Sørenga terminal is a key cargo and logistics facility within the Port of Oslo, handling maritime freight operations for the Norwegian capital.
E157587 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: Sørenga terminal | Statement: [Port of Oslo, hasPart, Sørenga terminal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sørenga terminal
Context triple: [Port of Oslo, hasPart, Sørenga terminal]
  • A. Kongsseteren
    Kongsseteren is a historic winter residence and retreat used by the Norwegian royal family near Oslo.
  • B. Kongsvinger
    Kongsvinger is a town and municipality in Innlandet county, Norway, known for its historic fortress overlooking the Glomma River and its role as a regional center near the Swedish border.
  • C. Tøyen
    Tøyen is a neighborhood in Oslo, Norway, known for its cultural institutions, parks, and educational facilities.
  • D. Støre
    Støre is a Norwegian surname most prominently associated with Jonas Gahr Støre, the Prime Minister of Norway and leader of the Labour Party.
  • E. Port of Halden
    Port of Halden is a Norwegian maritime port serving the town of Halden near the Swedish border, handling regional cargo and ferry traffic along the Oslofjord–Skagerrak corridor.
  • 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: Sørenga terminal
Triple: [Port of Oslo, hasPart, Sørenga terminal]
Generated description
Sørenga terminal is a key cargo and logistics facility within the Port of Oslo, handling maritime freight operations for the Norwegian capital.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Sørenga terminal
Target entity description: Sørenga terminal is a key cargo and logistics facility within the Port of Oslo, handling maritime freight operations for the Norwegian capital.
  • A. Kongsseteren
    Kongsseteren is a historic winter residence and retreat used by the Norwegian royal family near Oslo.
  • B. Kongsvinger
    Kongsvinger is a town and municipality in Innlandet county, Norway, known for its historic fortress overlooking the Glomma River and its role as a regional center near the Swedish border.
  • C. Tøyen
    Tøyen is a neighborhood in Oslo, Norway, known for its cultural institutions, parks, and educational facilities.
  • D. Støre
    Støre is a Norwegian surname most prominently associated with Jonas Gahr Støre, the Prime Minister of Norway and leader of the Labour Party.
  • E. Port of Halden
    Port of Halden is a Norwegian maritime port serving the town of Halden near the Swedish border, handling regional cargo and ferry traffic along the Oslofjord–Skagerrak corridor.
  • 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_69a498571d248190a0ac9eb02d97097f completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c28c8dd0819082f94c9e7c837c5f completed March 1, 2026, 10:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69acd47b63c081908a859a88ad5564b8 completed March 8, 2026, 1:44 a.m.
NEDg Description generation batch_69acd5314ac08190abf0ed287689dc5f completed March 8, 2026, 1:47 a.m.
NED2 Entity disambiguation (via description) batch_69acd59842f08190976724ad981de3d8 completed March 8, 2026, 1:49 a.m.
Created at: March 1, 2026, 7:56 p.m.