Triple

T15558024
Position Surface form Disambiguated ID Type / Status
Subject Haugesund Airport Karmøy E370920 entity
Predicate IATAcode P418 FINISHED
Object HAU
HAU is the IATA airport code for Haugesund Airport, Karmøy, which serves the Haugesund region in Norway.
 E1163391 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: HAU | Statement: [Haugesund Airport Karmøy, IATAcode, HAU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: HAU
Context triple: [Haugesund Airport Karmøy, IATAcode, HAU]
  • A. Hau
    Hau is the surname of Danish physicist Lene Vestergaard Hau, known for her pioneering work in slowing and stopping light.
  • B. Ha
    "Ha" is a track by rapper Juvenile, notable for its distinctive second-person narrative style and repetitive use of the word "ha," from his influential 1998 album *400 Degreez*.
  • C. HAHSTA
    HAHSTA is a public health agency focused on preventing and managing HIV/AIDS, hepatitis, sexually transmitted diseases, and tuberculosis.
  • D. HAJ
    HAJ is the three-letter IATA airport code for Hannover Airport in Hanover, Germany.
  • E. HAF
    HAF is the commonly used abbreviation for the Hellenic Air Force, the air warfare branch of Greece’s armed forces.
  • 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: HAU
Triple: [Haugesund Airport Karmøy, IATAcode, HAU]
Generated description
HAU is the IATA airport code for Haugesund Airport, Karmøy, which serves the Haugesund region in Norway.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: HAU
Target entity description: HAU is the IATA airport code for Haugesund Airport, Karmøy, which serves the Haugesund region in Norway.
  • A. Hau
    Hau is the surname of Danish physicist Lene Vestergaard Hau, known for her pioneering work in slowing and stopping light.
  • B. Ha
    "Ha" is a track by rapper Juvenile, notable for its distinctive second-person narrative style and repetitive use of the word "ha," from his influential 1998 album *400 Degreez*.
  • C. HAHSTA
    HAHSTA is a public health agency focused on preventing and managing HIV/AIDS, hepatitis, sexually transmitted diseases, and tuberculosis.
  • D. HAJ
    HAJ is the three-letter IATA airport code for Hannover Airport in Hanover, Germany.
  • E. HAF
    HAF is the commonly used abbreviation for the Hellenic Air Force, the air warfare branch of Greece’s armed forces.
  • 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_69d85cc6cf40819091f4a5facee1ebe6 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04dda3ab88190ab383333ce69fe8f completed April 16, 2026, 2:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff456635588190a2473bcff3ae4a53 completed May 9, 2026, 2:32 p.m.
NEDg Description generation batch_69ff46f44b2c81909f65f0ab455c6549 completed May 9, 2026, 2:38 p.m.
NED2 Entity disambiguation (via description) batch_69ff477a63b48190a453cf669dfda228 completed May 9, 2026, 2:40 p.m.
Created at: April 10, 2026, 4:09 a.m.