Triple

T15283951
Position Surface form Disambiguated ID Type / Status
Subject Finnish Institute for Health and Welfare E365344 entity
Predicate abbreviation P43 FINISHED
Object THL
THL is the Finnish Institute for Health and Welfare, a national expert agency responsible for research, development, and guidance on public health and social welfare in Finland.
 E1148305 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: THL | Statement: [Finnish Institute for Health and Welfare, abbreviation, THL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: THL
Context triple: [Finnish Institute for Health and Welfare, abbreviation, THL]
  • A. TLH
    TLH is the IATA airport code for Tallahassee International Airport, the primary commercial airport serving Florida’s state capital.
  • B. THLCH
    THLCH is the UN/LOCODE identifier for the major deep-sea commercial port of Laem Chabang in Thailand.
  • C. THS
    THS is the stock ticker symbol for TreeHouse Foods, a U.S.-based manufacturer of private-label packaged foods and beverages.
  • D. RTHL
    RTHL is an abbreviation for Recorded Texas Historic Landmark, the highest designation given by the state of Texas to historically significant structures.
  • E. THP
    THP is the station code for Thorildsplan, a Stockholm metro station on the green line in Sweden.
  • 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: THL
Triple: [Finnish Institute for Health and Welfare, abbreviation, THL]
Generated description
THL is the Finnish Institute for Health and Welfare, a national expert agency responsible for research, development, and guidance on public health and social welfare in Finland.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: THL
Target entity description: THL is the Finnish Institute for Health and Welfare, a national expert agency responsible for research, development, and guidance on public health and social welfare in Finland.
  • A. TLH
    TLH is the IATA airport code for Tallahassee International Airport, the primary commercial airport serving Florida’s state capital.
  • B. THLCH
    THLCH is the UN/LOCODE identifier for the major deep-sea commercial port of Laem Chabang in Thailand.
  • C. THS
    THS is the stock ticker symbol for TreeHouse Foods, a U.S.-based manufacturer of private-label packaged foods and beverages.
  • D. RTHL
    RTHL is an abbreviation for Recorded Texas Historic Landmark, the highest designation given by the state of Texas to historically significant structures.
  • E. THP
    THP is the station code for Thorildsplan, a Stockholm metro station on the green line in Sweden.
  • 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_69d85a103d9081908c1ea6c4c73ac8e3 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e00e53c9588190a6cb61ac8805c706 completed April 15, 2026, 10:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69feef798a588190981c77e6f4c6be78 completed May 9, 2026, 8:25 a.m.
NEDg Description generation batch_69fef1c3c054819096b1cf2e7887be49 completed May 9, 2026, 8:35 a.m.
NED2 Entity disambiguation (via description) batch_69fef24136c08190add6cbe1c6b2c0e2 completed May 9, 2026, 8:37 a.m.
Created at: April 10, 2026, 3:15 a.m.