Triple

T9364908
Position Surface form Disambiguated ID Type / Status
Subject Viljandi E225374 entity
Predicate hasTwinTown P919 FINISHED
Object Skien E114133 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: Skien | Statement: [Viljandi, hasTwinTown, Skien]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Skien
Context triple: [Viljandi, hasTwinTown, Skien]
  • A. Skien chosen
    Skien is a historic city in southern Norway known as the birthplace of playwright Henrik Ibsen and as a regional commercial and industrial center.
  • B. Steinkjer
    Steinkjer is a town and municipality in central Norway that serves as an important regional center and administrative hub in Trøndelag county.
  • C. Sogndal
    Sogndal is a village and municipality in Vestland county, Norway, known for its scenic fjord landscape, agriculture, and as a regional education and service center.
  • D. Porsgrunn
    Porsgrunn is an industrial and port city in Telemark county in southeastern Norway, known for its porcelain production and location along the Telemark Canal.
  • E. Tvedestrand
    Tvedestrand is a coastal town and municipality in southern Norway known for its wooden houses, maritime heritage, and picturesque archipelago.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca842cbddc819099d71ecec48cf9e5 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd503fd7f081909655e2a880c84834 completed April 1, 2026, 5:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69d100d3444c81908b14f165ec128b76 completed April 4, 2026, 12:15 p.m.
Created at: March 30, 2026, 7:43 p.m.