Triple

T6697856
Position Surface form Disambiguated ID Type / Status
Subject Culmer Metrorail station E152795 entity
Predicate hasStationCode P1289 FINISHED
Object CUL
CUL is the station code for Culmer Metrorail station in Miami’s rapid transit system.
 E610650 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: CUL | Statement: [Culmer Metrorail station, hasStationCode, CUL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: CUL
Context triple: [Culmer Metrorail station, hasStationCode, CUL]
  • A. CUL
    CUL is the main research library of the University of Cambridge and one of the largest and most important academic libraries in the United Kingdom.
  • B. CUN
    CUN is the IATA airport code for Cancún International Airport, a major gateway for international tourism to Mexico’s Caribbean coast.
  • C. KUL
    KUL is the IATA airport code for Kuala Lumpur International Airport, the main international gateway serving Malaysia’s capital region.
  • D. CYUL
    CYUL is the ICAO airport code for Montréal–Trudeau International Airport, the primary international airport serving Montreal, Canada.
  • E. CRL
    CRL is the ICAO airline designator used to identify Corsair International in aviation operations and communications.
  • 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: CUL
Triple: [Culmer Metrorail station, hasStationCode, CUL]
Generated description
CUL is the station code for Culmer Metrorail station in Miami’s rapid transit system.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: CUL
Target entity description: CUL is the station code for Culmer Metrorail station in Miami’s rapid transit system.
  • A. CUL
    CUL is the main research library of the University of Cambridge and one of the largest and most important academic libraries in the United Kingdom.
  • B. CUN
    CUN is the IATA airport code for Cancún International Airport, a major gateway for international tourism to Mexico’s Caribbean coast.
  • C. KUL
    KUL is the IATA airport code for Kuala Lumpur International Airport, the main international gateway serving Malaysia’s capital region.
  • D. CYUL
    CYUL is the ICAO airport code for Montréal–Trudeau International Airport, the primary international airport serving Montreal, Canada.
  • E. CRL
    CRL is the ICAO airline designator used to identify Corsair International in aviation operations and communications.
  • 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_69c68807adbc8190b8632df42b39eda0 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d0a4da9881908d79c410b4cff868 completed March 27, 2026, 6:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6f7bd965881908a128d97f1c94dd0 completed March 27, 2026, 9:33 p.m.
NEDg Description generation batch_69c6f86fb37c8190ba0ed52d998643d6 completed March 27, 2026, 9:36 p.m.
NED2 Entity disambiguation (via description) batch_69c6f8d6c9d481908d988984e0aed6bc completed March 27, 2026, 9:38 p.m.
Created at: March 27, 2026, 2:05 p.m.