Triple

T24813874
Position Surface form Disambiguated ID Type / Status
Subject Oslo Metro Kolsås Line E620861 entity
Predicate hasStation P35 FINISHED
Object Jernbanetorget Station
Jernbanetorget Station is a central Oslo Metro station and major public transport hub located beneath the main railway square in downtown Oslo, Norway.
E1783420 NE FINISHED

How this triple was built (2 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: Jernbanetorget Station | Statement: [Oslo Metro Kolsås Line, hasStation, Jernbanetorget Station]
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: Jernbanetorget Station
Triple: [Oslo Metro Kolsås Line, hasStation, Jernbanetorget Station]
Generated description
Jernbanetorget Station is a central Oslo Metro station and major public transport hub located beneath the main railway square in downtown Oslo, Norway.

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_69e2fabfd4648190bd0e5c7f4dbb6cab completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f4220c07dc81909878ee802bb2e8d0 completed May 1, 2026, 3:46 a.m.
NED1 Entity disambiguation (via context triple) batch_6a12da5bf38481908d247051af42bb60 completed May 24, 2026, 11 a.m.
NEDg Description generation batch_6a12daf3e7948190bb82f9eac6800971 completed May 24, 2026, 11:03 a.m.
NED2 Entity disambiguation (via description) batch_6a12db74542081909ede3d27600fb26b completed May 24, 2026, 11:05 a.m.
Created at: April 18, 2026, 4:59 a.m.