Triple

T13233477
Position Surface form Disambiguated ID Type / Status
Subject Los Angeles Railway E315079 entity
Predicate alsoKnownAs P39 FINISHED
Object LARy
LARy was a historic streetcar transit system that operated extensive electric rail lines throughout Los Angeles in the early to mid-20th century.
 E1029634 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: LARy | Statement: [Los Angeles Railway, alsoKnownAs, LARy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LARy
Context triple: [Los Angeles Railway, alsoKnownAs, LARy]
  • A. Lar
    Lar is a historic city in Iran’s Fars Province, known for its traditional architecture and role as a regional commercial center.
  • B. LARAS
    LARAS is the acronym for the Latin Recording Academy, the organization best known for presenting the Latin Grammy Awards and promoting Latin music and its creators worldwide.
  • C. LAL
    LAL is the standard NBA abbreviation for the Los Angeles Lakers basketball franchise.
  • D. LAL
    LAL is the IATA airport code for Lakeland Linder International Airport, a public airport serving Lakeland, Florida.
  • E. LER
    LER is the vehicle registration code assigned to the German island municipality of Borkum.
  • 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: LARy
Triple: [Los Angeles Railway, alsoKnownAs, LARy]
Generated description
LARy was a historic streetcar transit system that operated extensive electric rail lines throughout Los Angeles in the early to mid-20th century.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LARy
Target entity description: LARy was a historic streetcar transit system that operated extensive electric rail lines throughout Los Angeles in the early to mid-20th century.
  • A. Lar
    Lar is a historic city in Iran’s Fars Province, known for its traditional architecture and role as a regional commercial center.
  • B. LARAS
    LARAS is the acronym for the Latin Recording Academy, the organization best known for presenting the Latin Grammy Awards and promoting Latin music and its creators worldwide.
  • C. LAL
    LAL is the standard NBA abbreviation for the Los Angeles Lakers basketball franchise.
  • D. LAL
    LAL is the IATA airport code for Lakeland Linder International Airport, a public airport serving Lakeland, Florida.
  • E. LER
    LER is the vehicle registration code assigned to the German island municipality of Borkum.
  • 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_69d806affc688190a25b6ccc588e9c72 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98d34ff288190bdb550a019b7a470 completed April 10, 2026, 11:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6ff2dca2c81909cab1aa868ad575d completed May 3, 2026, 7:54 a.m.
NEDg Description generation batch_69f70476310c8190b13dc948c1f1ce95 completed May 3, 2026, 8:16 a.m.
NED2 Entity disambiguation (via description) batch_69f70578047c819089fc3044eceb4eac completed May 3, 2026, 8:21 a.m.
Created at: April 9, 2026, 9:22 p.m.