Triple

T16702191
Position Surface form Disambiguated ID Type / Status
Subject Lonavala railway station E405879 entity
Predicate hasStationCode P1289 FINISHED
Object LNL
LNL is the Indian Railways station code for Lonavala railway station, a key stop on the Mumbai–Pune route in Maharashtra, India.
 E1228808 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: LNL | Statement: [Lonavala railway station, hasStationCode, LNL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LNL
Context triple: [Lonavala railway station, hasStationCode, LNL]
  • A. LNZ
    LNZ is the IATA airport code for Linz Airport, the main international airport serving the city of Linz in Austria.
  • B. VNLK
    VNLK is the ICAO airport code for Tenzing-Hillary Airport, the small but famous high-altitude airfield serving Lukla in Nepal’s Everest region.
  • C. RNLN
    RNLN is the abbreviation commonly used for the Royal Netherlands Navy, the maritime branch of the Dutch armed forces.
  • D. NLA
    NLA refers to Switzerland’s top professional ice hockey league, known for featuring high-level European competition.
  • E. LNI
    LNI is the ICAO airline designator used for Lion Air, a major Indonesian low-cost carrier.
  • 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: LNL
Triple: [Lonavala railway station, hasStationCode, LNL]
Generated description
LNL is the Indian Railways station code for Lonavala railway station, a key stop on the Mumbai–Pune route in Maharashtra, India.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LNL
Target entity description: LNL is the Indian Railways station code for Lonavala railway station, a key stop on the Mumbai–Pune route in Maharashtra, India.
  • A. LNZ
    LNZ is the IATA airport code for Linz Airport, the main international airport serving the city of Linz in Austria.
  • B. VNLK
    VNLK is the ICAO airport code for Tenzing-Hillary Airport, the small but famous high-altitude airfield serving Lukla in Nepal’s Everest region.
  • C. RNLN
    RNLN is the abbreviation commonly used for the Royal Netherlands Navy, the maritime branch of the Dutch armed forces.
  • D. NLA
    NLA refers to Switzerland’s top professional ice hockey league, known for featuring high-level European competition.
  • E. LNI
    LNI is the ICAO airline designator used for Lion Air, a major Indonesian low-cost carrier.
  • 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_69d8838db21081909589220fd71440a4 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e383326d7081909ef4c3b724876513 completed April 18, 2026, 1:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0091a0dee08190a67ed5df2008c91e completed May 10, 2026, 2:09 p.m.
NEDg Description generation batch_6a00923f1da08190b6b2c869284099bc completed May 10, 2026, 2:12 p.m.
NED2 Entity disambiguation (via description) batch_6a00931aa1d88190a0775e74779b3a6b completed May 10, 2026, 2:15 p.m.
Created at: April 10, 2026, 5:19 a.m.