Triple

T7236329
Position Surface form Disambiguated ID Type / Status
Subject Giaever E155236 entity
Predicate hasVariantSpelling P457 FINISHED
Object Giæver
Giæver is a Norwegian surname borne by several notable figures in fields such as physics, literature, and public service.
 E651061 NE FINISHED

How this triple was built (4 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: Giæver | Statement: [Giaever, hasVariantSpelling, Giæver]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Giæver
Context triple: [Giaever, hasVariantSpelling, Giæver]
  • A. Tysvær
    Tysvær is a coastal municipality in southwestern Norway known for its fjords, islands, and location between the cities of Haugesund and Stavanger.
  • B. Gjende
    Gjende is a long, narrow glacial lake in Norway’s Jotunheimen mountains, renowned for its striking turquoise-green water and popular hiking routes along its shores.
  • C. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • D. Geilo
    Geilo is a Norwegian mountain village and popular year-round resort known for its skiing, hiking, and proximity to the Hardangervidda plateau.
  • E. Giske
    Giske is a coastal municipality in Møre og Romsdal county, Norway, known for its islands, fishing communities, and proximity to the town of Ålesund.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Giæver
Triple: [Giaever, hasVariantSpelling, Giæver]
Generated description
Giæver is a Norwegian surname borne by several notable figures in fields such as physics, literature, and public service.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Giæver
Target entity description: Giæver is a Norwegian surname borne by several notable figures in fields such as physics, literature, and public service.
  • A. Tysvær
    Tysvær is a coastal municipality in southwestern Norway known for its fjords, islands, and location between the cities of Haugesund and Stavanger.
  • B. Gjende
    Gjende is a long, narrow glacial lake in Norway’s Jotunheimen mountains, renowned for its striking turquoise-green water and popular hiking routes along its shores.
  • C. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • D. Geilo
    Geilo is a Norwegian mountain village and popular year-round resort known for its skiing, hiking, and proximity to the Hardangervidda plateau.
  • E. Giske
    Giske is a coastal municipality in Møre og Romsdal county, Norway, known for its islands, fishing communities, and proximity to the town of Ålesund.
  • F. None of above. chosen

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_69c688143bfc81908d4176617735e601 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6ea33dd3481908ebb050e1fab5aaa completed March 27, 2026, 8:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7cc32d6c48190ae78b3d1227bb868 completed March 28, 2026, 12:40 p.m.
NEDg Description generation batch_69c7cdce27548190bb0c026f70fd915a completed March 28, 2026, 12:47 p.m.
NED2 Entity disambiguation (via description) batch_69c7ce41ed088190b423ed7955c68eeb completed March 28, 2026, 12:49 p.m.
Created at: March 27, 2026, 2:55 p.m.