Triple

T5152375
Position Surface form Disambiguated ID Type / Status
Subject Norsjø E116225 entity
Predicate nearbyCity P350 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: [Norsjø, nearbyCity, Skien]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Skien
Context triple: [Norsjø, nearbyCity, 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_69bd445d94788190b72e2cc563120995 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd78daab708190a42734a14dddb2fc completed March 20, 2026, 4:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69bfba1e755081909649e0dd8c4d2270 completed March 22, 2026, 9:45 a.m.
Created at: March 20, 2026, 1:44 p.m.