Triple

T15751513
Position Surface form Disambiguated ID Type / Status
Subject Vikran E381856 entity
Predicate locatedInFormerCounty P29444 FINISHED
Object Troms E80793 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: Troms | Statement: [Vikran, locatedInFormerCounty, Troms]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Troms
Context triple: [Vikran, locatedInFormerCounty, Troms]
  • A. Troms chosen
    Troms was a former county in northern Norway known for its Arctic landscapes, coastal fjords, and the city of Tromsø.
  • B. Trondenes
    Trondenes is a historic former municipality and parish in northern Norway, known for its medieval stone church and role as an administrative center in the Harstad region.
  • C. Tjørhom
    Tjørhom is a small village in southwestern Norway known for its mountainous landscape and proximity to popular skiing and outdoor recreation areas.
  • D. Tjuneroy
    Tjuneroy was an ancient Egyptian official, likely a high-ranking scribe or priest under Ramesses II, associated with the creation of the Saqqara King List.
  • E. Bjerke
    Bjerke is a neighborhood in the Bjerke borough of Oslo, Norway, known primarily as a residential area with local services and amenities.
  • 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_69d86d9e6b44819085d1f6a969ecb74c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e05030e31081908c307a8dc7067db4 completed April 16, 2026, 2:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffa9361f5c8190b68702154d05bbc2 completed May 9, 2026, 9:37 p.m.
Created at: April 10, 2026, 4:47 a.m.