Triple

T20926531
Position Surface form Disambiguated ID Type / Status
Subject LaSalle station E515358 entity
Predicate hasStationCode P1289 FINISHED
Object LS
LS is the station code for LaSalle station, a public transit stop in the Montreal Metro system.
 E1458440 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: LS | Statement: [LaSalle station, hasStationCode, LS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LS
Context triple: [LaSalle station, hasStationCode, LS]
  • A. LS
    LS is the IATA airline designator used by the British low-cost carrier Jet2.com.
  • B. LS
    LS is a base trim level designation commonly used by Chevrolet for entry-level versions of its vehicles, including the Trailblazer.
  • C. LS
    LS is the common abbreviation and nickname for FC Lausanne-Sport, a professional football club based in Lausanne, Switzerland.
  • D. SL
    SL is a mid-range trim level of the Vauxhall Viva that added extra comfort and cosmetic features over the base model.
  • E. SL
    The Mercedes-Benz SL is a long-running line of luxury grand touring roadsters renowned for combining high performance with elegant design and advanced technology.
  • 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: LS
Triple: [LaSalle station, hasStationCode, LS]
Generated description
LS is the station code for LaSalle station, a public transit stop in the Montreal Metro system.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LS
Target entity description: LS is the station code for LaSalle station, a public transit stop in the Montreal Metro system.
  • A. LS
    LS is the IATA airline designator used by the British low-cost carrier Jet2.com.
  • B. LS
    LS is a base trim level designation commonly used by Chevrolet for entry-level versions of its vehicles, including the Trailblazer.
  • C. LS
    LS is the common abbreviation and nickname for FC Lausanne-Sport, a professional football club based in Lausanne, Switzerland.
  • D. SL
    SL is a mid-range trim level of the Vauxhall Viva that added extra comfort and cosmetic features over the base model.
  • E. SL
    SL is a mid-range trim level designation used by Holden for certain Torana models, offering additional comfort and convenience features over the base variants.
  • 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_69e0b4fb431c8190b9d40e6a72f0cc87 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6f65200b08190ac208204a20f5a6a completed April 21, 2026, 4 a.m.
NED1 Entity disambiguation (via context triple) batch_6a091fcca00c819094ed5e08706a269b completed May 17, 2026, 1:54 a.m.
NEDg Description generation batch_6a092118d5a0819095da7046d704fd99 completed May 17, 2026, 1:59 a.m.
NED2 Entity disambiguation (via description) batch_6a09218e766c8190a35907cfbd778ec8 completed May 17, 2026, 2:01 a.m.
Created at: April 16, 2026, 12:49 p.m.