Triple

T3063275
Position Surface form Disambiguated ID Type / Status
Subject Dom/Römer station E62044 entity
Predicate hasStationCode P1289 FINISHED
Object FDOR
FDOR is the station code assigned to Dom/Römer, a central urban rail stop in Frankfurt am Main, Germany.
 E323288 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: FDOR | Statement: [Dom/Römer station, hasStationCode, FDOR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: FDOR
Context triple: [Dom/Römer station, hasStationCode, FDOR]
  • A. DOR
    DOR is the standard abbreviation used for the Mexican football club Dorados de Sinaloa.
  • B. NDRF
    NDRF is India’s specialized federal force tasked with responding to natural and man-made disasters, conducting search and rescue, and supporting disaster management efforts across the country.
  • C. F.D.
    F.D. is the standard abbreviation of the Latin title "Fidei Defensor," historically used by English and later British monarchs to denote their role as "Defender of the Faith."
  • D. DAR
    DAR is the Philippine government agency responsible for implementing agrarian reform and redistributing agricultural land to farmers.
  • E. DHR
    DHR is the stock ticker symbol for Danaher Corporation, a global science and technology company focused on life sciences, diagnostics, and environmental and applied solutions.
  • 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: FDOR
Triple: [Dom/Römer station, hasStationCode, FDOR]
Generated description
FDOR is the station code assigned to Dom/Römer, a central urban rail stop in Frankfurt am Main, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: FDOR
Target entity description: FDOR is the station code assigned to Dom/Römer, a central urban rail stop in Frankfurt am Main, Germany.
  • A. DOR
    DOR is the standard abbreviation used for the Mexican football club Dorados de Sinaloa.
  • B. NDRF
    NDRF is India’s specialized federal force tasked with responding to natural and man-made disasters, conducting search and rescue, and supporting disaster management efforts across the country.
  • C. F.D.
    F.D. is the standard abbreviation of the Latin title "Fidei Defensor," historically used by English and later British monarchs to denote their role as "Defender of the Faith."
  • D. DAR
    DAR is the Philippine government agency responsible for implementing agrarian reform and redistributing agricultural land to farmers.
  • E. DHR
    DHR is the stock ticker symbol for Danaher Corporation, a global science and technology company focused on life sciences, diagnostics, and environmental and applied solutions.
  • 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_69ad85793e5c8190a358049bc4a98d8c completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ad9ea088fc819090b9d5bbcb268671 completed March 8, 2026, 4:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1ef118cb48190a1f666ead7c19a12 completed March 11, 2026, 10:39 p.m.
NEDg Description generation batch_69b1f1291670819083866bd4950d4124 completed March 11, 2026, 10:48 p.m.
NED2 Entity disambiguation (via description) batch_69b1f1990be08190bfa83c08323c5d40 completed March 11, 2026, 10:50 p.m.
Created at: March 8, 2026, 3:02 p.m.