Triple

T13768422
Position Surface form Disambiguated ID Type / Status
Subject Hadseløya E330810 entity
Predicate hasSettlement P1068 FINISHED
Object Melbu E1092528 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: Melbu | Statement: [Hadseløya, hasSettlement, Melbu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Melbu
Context triple: [Hadseløya, hasSettlement, Melbu]
  • A. Melbu chosen
    Melbu is a small coastal village and fishing community in Hadsel Municipality in Nordland county, Norway.
  • B. Skien
    Skien is a historic city in southern Norway known as the birthplace of playwright Henrik Ibsen and as a regional commercial and industrial center.
  • C. Steinkjer
    Steinkjer is a town and municipality in central Norway that serves as an important regional center and administrative hub in Trøndelag county.
  • D. Kristiansund
    Kristiansund is a coastal city in western Norway known for its historic clipfish industry, distinctive layout across several islands, and picturesque harbor.
  • E. 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.
  • 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_69d81c583b0081909e408a17db517a21 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de0233ecc48190b934f085d2501eb1 completed April 14, 2026, 9 a.m.
NED1 Entity disambiguation (via context triple) batch_69fd7a2df73c8190aeb6f472ac7ebeed completed May 8, 2026, 5:52 a.m.
Created at: April 9, 2026, 10:10 p.m.