Triple

T22782829
Position Surface form Disambiguated ID Type / Status
Subject Lucknow Junction E563883 entity
Predicate stationCode P1289 FINISHED
Object LJN
LJN is the station code for Lucknow Junction, a major railway hub in the city of Lucknow, India.
 E1553851 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: LJN | Statement: [Lucknow Junction, stationCode, LJN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LJN
Context triple: [Lucknow Junction, stationCode, LJN]
  • A. LJN
    LJN was an American toy and video game company best known for producing licensed but often critically panned video games and action figures in the 1980s and early 1990s.
  • B. JLN
    JLN is the three-letter IATA airport code for Joplin Regional Airport in Joplin, Missouri, United States.
  • C. LJ
    LJ is the third-generation model of the Holden Torana, a compact Australian car produced in the early 1970s and known for its performance-oriented variants.
  • D. LJ
    LJ is the IATA airline designator used for the South Korean low-cost carrier Jin Air.
  • E. LJ
    LJ is the standard postnominal abbreviation used to denote a Lord Justice, a senior judge in the Court of Appeal of England and Wales.
  • 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: LJN
Triple: [Lucknow Junction, stationCode, LJN]
Generated description
LJN is the station code for Lucknow Junction, a major railway hub in the city of Lucknow, India.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LJN
Target entity description: LJN is the station code for Lucknow Junction, a major railway hub in the city of Lucknow, India.
  • A. LJN
    LJN was an American toy and video game company best known for producing licensed but often critically panned video games and action figures in the 1980s and early 1990s.
  • B. JLN
    JLN is the three-letter IATA airport code for Joplin Regional Airport in Joplin, Missouri, United States.
  • C. LJ
    LJ is the IATA airline designator used for the South Korean low-cost carrier Jin Air.
  • D. LJ
    LJ is the third-generation model of the Holden Torana, a compact Australian car produced in the early 1970s and known for its performance-oriented variants.
  • E. LJ
    LJ is the nickname of Larry Johnson, a former NBA All-Star forward best known for his time with the Charlotte Hornets and New York Knicks in the 1990s.
  • 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_69e2455500788190b4b33030461f3bbd completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17c2ee4e88190951afb2abe69009f completed April 29, 2026, 3:34 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0b98291e208190be3e92b57cf2fccd completed May 18, 2026, 10:52 p.m.
NEDg Description generation batch_6a0b992ef5d0819081957a9633260400 completed May 18, 2026, 10:56 p.m.
NED2 Entity disambiguation (via description) batch_6a0b9a1129c8819084163353accc7e14 completed May 18, 2026, 11 p.m.
Created at: April 17, 2026, 3:29 p.m.