Triple

T20879919
Position Surface form Disambiguated ID Type / Status
Subject Mannheim Hauptbahnhof E514116 entity
Predicate hasDS100Code P1289 FINISHED
Object RMA
RMA is the DS100 railway station code used to identify Mannheim Hauptbahnhof in the German rail network.
 E1455088 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: RMA | Statement: [Mannheim Hauptbahnhof, hasDS100Code, RMA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: RMA
Context triple: [Mannheim Hauptbahnhof, hasDS100Code, RMA]
  • A. RMA
    RMA is a U.S. Department of Agriculture agency responsible for overseeing federal crop insurance and risk management programs for farmers and ranchers.
  • B. RMA
    RMA is the IATA airport code for Roma Airport, a regional airport serving the town of Roma in Queensland, Australia.
  • C. RMA
    RMA is the central bank of Bhutan responsible for issuing currency, managing monetary policy, and overseeing the country’s financial system.
  • D. RMA
    RMA is New Zealand’s principal environmental and resource management law that governs how natural and physical resources are used, developed, and protected.
  • E. RM
    RM is a UK postcode area in east London and parts of Essex, covering districts such as Romford and surrounding suburbs.
  • 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: RMA
Triple: [Mannheim Hauptbahnhof, hasDS100Code, RMA]
Generated description
RMA is the DS100 railway station code used to identify Mannheim Hauptbahnhof in the German rail network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: RMA
Target entity description: RMA is the DS100 railway station code used to identify Mannheim Hauptbahnhof in the German rail network.
  • A. RMA
    RMA is a U.S. Department of Agriculture agency responsible for overseeing federal crop insurance and risk management programs for farmers and ranchers.
  • B. RMA
    RMA is New Zealand’s principal environmental and resource management law that governs how natural and physical resources are used, developed, and protected.
  • C. RMA
    RMA is the IATA airport code for Roma Airport, a regional airport serving the town of Roma in Queensland, Australia.
  • D. RMA
    RMA is the central bank of Bhutan responsible for issuing currency, managing monetary policy, and overseeing the country’s financial system.
  • E. RM
    RM is a UK postcode area in east London and parts of Essex, covering districts such as Romford and surrounding suburbs.
  • 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_69e0b4f733f081908a401c0b7beb0b9f completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c678b394819096a17de9e04cd74f completed April 21, 2026, 12:36 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0913a242e08190be0a234ba5221a3a completed May 17, 2026, 1:02 a.m.
NEDg Description generation batch_6a09146856cc819087dbfbe5f9b24bb1 completed May 17, 2026, 1:05 a.m.
NED2 Entity disambiguation (via description) batch_6a0915506ff48190a97f89092dac2ca2 completed May 17, 2026, 1:09 a.m.
Created at: April 16, 2026, 12:45 p.m.