Triple

T18127823
Position Surface form Disambiguated ID Type / Status
Subject Challenge Roth triathlon E433925 entity
Predicate sponsor P67 FINISHED
Object DATEV
DATEV is a German software and IT services cooperative specializing in tax, accounting, and payroll solutions for professionals and businesses.
 E1308419 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: DATEV | Statement: [Challenge Roth triathlon, sponsor, DATEV]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DATEV
Context triple: [Challenge Roth triathlon, sponsor, DATEV]
  • A. DAV
    DAV is the IATA airport code for Enrique Malek International Airport in David, Panama.
  • B. DAV
    DAV is the station code used to identify Davisville station in the Toronto subway system.
  • C. Deltek
    Deltek is a software company specializing in enterprise resource planning and project-based solutions for government contractors and professional services firms.
  • D. TDEC
    TDEC is the state agency responsible for protecting Tennessee’s environment and managing its natural resources, parks, and conservation programs.
  • E. DEVB
    DEVB is the abbreviation for the Development Bureau, a government body responsible for planning, land development, public works, and related infrastructure policies.
  • 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: DATEV
Triple: [Challenge Roth triathlon, sponsor, DATEV]
Generated description
DATEV is a German software and IT services cooperative specializing in tax, accounting, and payroll solutions for professionals and businesses.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: DATEV
Target entity description: DATEV is a German software and IT services cooperative specializing in tax, accounting, and payroll solutions for professionals and businesses.
  • A. DAV
    DAV is the IATA airport code for Enrique Malek International Airport in David, Panama.
  • B. DAV
    DAV is the station code used to identify Davisville station in the Toronto subway system.
  • C. Deltek
    Deltek is a software company specializing in enterprise resource planning and project-based solutions for government contractors and professional services firms.
  • D. TDEC
    TDEC is the state agency responsible for protecting Tennessee’s environment and managing its natural resources, parks, and conservation programs.
  • E. DEVB
    DEVB is the abbreviation for the Development Bureau, a government body responsible for planning, land development, public works, and related infrastructure policies.
  • 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_69d8b909e8cc81908df4cc2b8ea6d11f completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4ddef4cd88190b16ef0d6ed3968c6 completed April 19, 2026, 1:51 p.m.
NED1 Entity disambiguation (via context triple) batch_6a038543ec98819080e1bb3981634ecb completed May 12, 2026, 7:53 p.m.
NEDg Description generation batch_6a0389649bac8190bb5f189e562ab8c3 completed May 12, 2026, 8:11 p.m.
NED2 Entity disambiguation (via description) batch_6a0389eb3928819080b6d257698e662c completed May 12, 2026, 8:13 p.m.
Created at: April 10, 2026, 10:29 a.m.