Triple

T1834164
Position Surface form Disambiguated ID Type / Status
Subject Canarsie E41026 entity
Predicate servedBySubwayLine P17559 FINISHED
Object L
The L is a New York City Subway line that runs crosstown through Manhattan and into Brooklyn, including service to neighborhoods such as Canarsie.
 E204029 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: L | Statement: [Canarsie, servedBySubwayLine, L]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: L
Context triple: [Canarsie, servedBySubwayLine, L]
  • A. L
    L is the enigmatic, emotionally complex protagonist of Hanne Ørstavik’s novel "Love," whose inner life and perspective drive the story’s exploration of isolation and longing.
  • B. L
    L is the vehicle registration code used on license plates for the German city and district of Leipzig.
  • C. L
    The L is a Chicago 'L' rapid transit line that serves the city’s West Side and western suburbs as part of the Chicago Transit Authority system.
  • D. Le
    Le is a common Vietnamese surname shared by many notable figures in the country’s history and culture.
  • E. LV
    LV is the two-letter ISO 3166-1 alpha-2 country code representing Latvia.
  • 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: L
Triple: [Canarsie, servedBySubwayLine, L]
Generated description
The L is a New York City Subway line that runs crosstown through Manhattan and into Brooklyn, including service to neighborhoods such as Canarsie.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: L
Target entity description: The L is a New York City Subway line that runs crosstown through Manhattan and into Brooklyn, including service to neighborhoods such as Canarsie.
  • A. L
    L is the enigmatic, emotionally complex protagonist of Hanne Ørstavik’s novel "Love," whose inner life and perspective drive the story’s exploration of isolation and longing.
  • B. L
    The L is a Chicago 'L' rapid transit line that serves the city’s West Side and western suburbs as part of the Chicago Transit Authority system.
  • C. L
    L is the vehicle registration code used on license plates for the German city and district of Leipzig.
  • D. Le
    Le is a common Vietnamese surname shared by many notable figures in the country’s history and culture.
  • E. LV
    LV is the two-letter ISO 3166-1 alpha-2 country code representing Latvia.
  • 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_69a88647f9388190909bc36e795bdaec completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb02540dc819081b19a09562139cd completed March 7, 2026, 4:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69adbf711c148190ba12d9bc81892095 completed March 8, 2026, 6:26 p.m.
NEDg Description generation batch_69adc080abb88190883c35943dc7cb10 completed March 8, 2026, 6:31 p.m.
NED2 Entity disambiguation (via description) batch_69adc13330888190a2b99c6fcbb97d49 completed March 8, 2026, 6:34 p.m.
Created at: March 4, 2026, 7:33 p.m.