Triple

T11931957
Position Surface form Disambiguated ID Type / Status
Subject Disability Living Allowance E283935 entity
Predicate shortName P43 FINISHED
Object DLA
DLA is a UK social security benefit that provides financial support to people with disabilities to help cover the extra costs of care and mobility.
 E954726 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: DLA | Statement: [Disability Living Allowance, shortName, DLA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DLA
Context triple: [Disability Living Allowance, shortName, DLA]
  • A. DLA
    DLA is the commonly used abbreviation for the Delhi Legislative Assembly, the unicameral law-making body of the National Capital Territory of Delhi in India.
  • B. DLA
    DLA is the IATA airport code for Douala International Airport, the main air gateway to Douala, Cameroon.
  • C. DCLA
    DCLA is the New York City government agency responsible for supporting and promoting the city’s cultural institutions, arts organizations, and creative communities.
  • D. DLS
    DLS is a conference that forms part of the SPLASH event, focusing on research and advances in dynamic languages and their applications.
  • E. DLG
    DLG is the vehicle registration code for the municipality of Blindheim in Germany.
  • 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: DLA
Triple: [Disability Living Allowance, shortName, DLA]
Generated description
DLA is a UK social security benefit that provides financial support to people with disabilities to help cover the extra costs of care and mobility.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: DLA
Target entity description: DLA is a UK social security benefit that provides financial support to people with disabilities to help cover the extra costs of care and mobility.
  • A. DLA
    DLA is the commonly used abbreviation for the Delhi Legislative Assembly, the unicameral law-making body of the National Capital Territory of Delhi in India.
  • B. DLA
    DLA is the IATA airport code for Douala International Airport, the main air gateway to Douala, Cameroon.
  • C. DCLA
    DCLA is the New York City government agency responsible for supporting and promoting the city’s cultural institutions, arts organizations, and creative communities.
  • D. DLS
    DLS is a conference that forms part of the SPLASH event, focusing on research and advances in dynamic languages and their applications.
  • E. DLG
    DLG is the vehicle registration code for the municipality of Blindheim in Germany.
  • 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_69d6ab2ce9c48190b5d39511b524f666 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d90305015c81908edb0d9d3d012b2e completed April 10, 2026, 2:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69f4406ee910819093c72738bfe3f92c completed May 1, 2026, 5:55 a.m.
NEDg Description generation batch_69f448fc874081908fe05f9d8aff11a3 completed May 1, 2026, 6:32 a.m.
NED2 Entity disambiguation (via description) batch_69f44afdc7b08190bdf47cfcb94c34c8 completed May 1, 2026, 6:41 a.m.
Created at: April 8, 2026, 9:45 p.m.