Triple

T13798021
Position Surface form Disambiguated ID Type / Status
Subject Hamar, Norway E331565 entity
Predicate hasFootballClub P346 FINISHED
Object HamKam
HamKam is a Norwegian professional football club based in the city of Hamar, known for competing in the country’s top football divisions.
 E1061826 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: HamKam | Statement: [Hamar, Norway, hasFootballClub, HamKam]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: HamKam
Context triple: [Hamar, Norway, hasFootballClub, HamKam]
  • A. Kanpetlet
    Kanpetlet is a small, remote town in western Myanmar known as a gateway to Nat Ma Taung (Mount Victoria) and its surrounding national park.
  • B. KAM
    KAM is the standard abbreviation for the Kamloops Blazers, a major junior ice hockey team in the Western Hockey League based in Kamloops, British Columbia.
  • C. Man Kam To
    Man Kam To is a border area and control point in Hong Kong near the boundary with mainland China.
  • D. Hamap
    Hamap is a Papuan language spoken on the Alor–Pantar archipelago in eastern Indonesia.
  • E. Ham
    Ham is a suburban riverside district in southwest London, England, known for its historic houses, green spaces, and proximity to the River Thames.
  • 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: HamKam
Triple: [Hamar, Norway, hasFootballClub, HamKam]
Generated description
HamKam is a Norwegian professional football club based in the city of Hamar, known for competing in the country’s top football divisions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: HamKam
Target entity description: HamKam is a Norwegian professional football club based in the city of Hamar, known for competing in the country’s top football divisions.
  • A. Kanpetlet
    Kanpetlet is a small, remote town in western Myanmar known as a gateway to Nat Ma Taung (Mount Victoria) and its surrounding national park.
  • B. KAM
    KAM is the standard abbreviation for the Kamloops Blazers, a major junior ice hockey team in the Western Hockey League based in Kamloops, British Columbia.
  • C. Man Kam To
    Man Kam To is a border area and control point in Hong Kong near the boundary with mainland China.
  • D. Hamap
    Hamap is a Papuan language spoken on the Alor–Pantar archipelago in eastern Indonesia.
  • E. Ham
    Ham is a municipality in the Belgian province of Limburg, known for its rural character and location in the Flemish Region.
  • 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_69d81c58feb08190a77bca8bf7d6d20f completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de025be1f08190aac525d72d7dc0c3 completed April 14, 2026, 9:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7b086d6d48190b823ed0a4403fbc5 completed May 3, 2026, 8:31 p.m.
NEDg Description generation batch_69f7b138158c8190957d43d529c37fa4 completed May 3, 2026, 8:34 p.m.
NED2 Entity disambiguation (via description) batch_69f7b1e0ab948190a046a68aa5e029a6 completed May 3, 2026, 8:36 p.m.
Created at: April 9, 2026, 10:11 p.m.