Triple

T940095
Position Surface form Disambiguated ID Type / Status
Subject Island of Montreal E20285 entity
Predicate contains P35 FINISHED
Object LaSalle
LaSalle is a residential borough of Montreal, Quebec, known for its riverside parks along the St. Lawrence River and its diverse, largely suburban community.
 E110598 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: LaSalle | Statement: [Island of Montreal, contains, LaSalle]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LaSalle
Context triple: [Island of Montreal, contains, LaSalle]
  • A. LaSalle/Van Buren
    LaSalle/Van Buren is a Chicago 'L' station in the Loop that serves as one of the downtown stops on the CTA's Pink Line.
  • B. Shawmut
    Shawmut is a neighborhood rapid transit station on Boston's MBTA Red Line, located in the Dorchester area.
  • C. Freeport
    Freeport is a waterfront village on Long Island in New York known for its marinas, fishing industry, and nautical tourism.
  • D. Dix
    Dix is the surname of Dorothea Dix, the 19th-century American social reformer known for her pioneering work in mental health care and prison reform.
  • E. Larcomar
    Larcomar is a popular cliffside shopping and entertainment center in Lima, Peru, overlooking the Pacific Ocean and known for its restaurants, boutiques, and ocean views.
  • 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: LaSalle
Triple: [Island of Montreal, contains, LaSalle]
Generated description
LaSalle is a residential borough of Montreal, Quebec, known for its riverside parks along the St. Lawrence River and its diverse, largely suburban community.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LaSalle
Target entity description: LaSalle is a residential borough of Montreal, Quebec, known for its riverside parks along the St. Lawrence River and its diverse, largely suburban community.
  • A. LaSalle/Van Buren
    LaSalle/Van Buren is a Chicago 'L' station in the Loop that serves as one of the downtown stops on the CTA's Pink Line.
  • B. Shawmut
    Shawmut is a neighborhood rapid transit station on Boston's MBTA Red Line, located in the Dorchester area.
  • C. Freeport
    Freeport is a waterfront village on Long Island in New York known for its marinas, fishing industry, and nautical tourism.
  • D. Dix
    Dix is the surname of Dorothea Dix, the 19th-century American social reformer known for her pioneering work in mental health care and prison reform.
  • E. Larcomar
    Larcomar is a popular cliffside shopping and entertainment center in Lima, Peru, overlooking the Pacific Ocean and known for its restaurants, boutiques, and ocean views.
  • 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_69a493b0270c81909e6c9ce310f6aa55 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b38b7da08190ac0853655dab678a completed March 1, 2026, 9:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69a826e30c448190acc1457a63d27a4a completed March 4, 2026, 12:34 p.m.
NEDg Description generation batch_69a8343e16908190af102cfce025c31f completed March 4, 2026, 1:31 p.m.
NED2 Entity disambiguation (via description) batch_69a834f3a9288190a8cd28165379cec6 completed March 4, 2026, 1:34 p.m.
Created at: March 1, 2026, 7:40 p.m.