Triple

T16029320
Position Surface form Disambiguated ID Type / Status
Subject Helsinki Central railway station E388801 entity
Predicate hasStationCode P1289 FINISHED
Object HKI
HKI is the station code for Helsinki Central railway station, the main rail transport hub in Finland’s capital city.
 E1188832 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: HKI | Statement: [Helsinki Central railway station, hasStationCode, HKI]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: HKI
Context triple: [Helsinki Central railway station, hasStationCode, HKI]
  • A. HK
    HK is a renowned German defense manufacturer best known for designing and producing small arms such as pistols, rifles, and submachine guns used by military and law enforcement worldwide.
  • B. HK
    HK is the vehicle registration code used for the Czech city of Hradec Králové.
  • C. HK
    HK is the vehicle registration code used for motor vehicles registered in Heraklion, a major city on the island of Crete in Greece.
  • D. HK
    HK is the vehicle registration code used on license plates for vehicles registered in Ahlden, a municipality in Lower Saxony, Germany.
  • E. HKJK
    HKJK is the ICAO airport code for Jomo Kenyatta International Airport, the main international gateway serving Nairobi, Kenya.
  • 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: HKI
Triple: [Helsinki Central railway station, hasStationCode, HKI]
Generated description
HKI is the station code for Helsinki Central railway station, the main rail transport hub in Finland’s capital city.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: HKI
Target entity description: HKI is the station code for Helsinki Central railway station, the main rail transport hub in Finland’s capital city.
  • A. HK
    HK is a renowned German defense manufacturer best known for designing and producing small arms such as pistols, rifles, and submachine guns used by military and law enforcement worldwide.
  • B. HK
    HK is the vehicle registration code used for the Czech city of Hradec Králové.
  • C. HK
    HK is the vehicle registration code used for motor vehicles registered in Heraklion, a major city on the island of Crete in Greece.
  • D. HK
    HK is the vehicle registration code used on license plates for vehicles registered in Ahlden, a municipality in Lower Saxony, Germany.
  • E. HKJK
    HKJK is the ICAO airport code for Jomo Kenyatta International Airport, the main international gateway serving Nairobi, Kenya.
  • 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_69d86dada3808190825d5f80d72fbe88 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1832a56ec8190a47fd2cf83a42fd4 completed April 17, 2026, 12:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffcf35ae808190aeb154a273c32a70 completed May 10, 2026, 12:20 a.m.
NEDg Description generation batch_69ffd0384804819080f63b86d5114950 completed May 10, 2026, 12:24 a.m.
NED2 Entity disambiguation (via description) batch_69ffd09e128c819081e92a479a4a24f9 completed May 10, 2026, 12:26 a.m.
Created at: April 10, 2026, 4:56 a.m.