Triple

T17009804
Position Surface form Disambiguated ID Type / Status
Subject Jiyugaoka Station E412667 entity
Predicate hasStationCode P1289 FINISHED
Object OM10
OM10 is the station code assigned to Jiyugaoka Station on the Tokyu railway network in Tokyo, Japan.
 E1244621 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: OM10 | Statement: [Jiyugaoka Station, hasStationCode, OM10]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: OM10
Context triple: [Jiyugaoka Station, hasStationCode, OM10]
  • A. OM
    OM is an American experimental rock band known for its hypnotic, drone-influenced sound and spiritually themed compositions.
  • B. OM
    OM is the commonly used abbreviation for Olympique de Marseille, a major French professional football club based in Marseille.
  • C. OM
    OM is the post-nominal abbreviation used by members of the Order of Merit, a prestigious British honor recognizing distinguished service in the armed forces, science, art, literature, or the promotion of culture.
  • D. OM
    OM is the two-letter ISO 3166-1 alpha-2 country code assigned to Oman for international identification and data standards.
  • E. OM
    OM was an Italian manufacturer known for producing vehicles such as the Milan series 1500 Peter Witt trams as well as trucks and other industrial transport equipment.
  • 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: OM10
Triple: [Jiyugaoka Station, hasStationCode, OM10]
Generated description
OM10 is the station code assigned to Jiyugaoka Station on the Tokyu railway network in Tokyo, Japan.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: OM10
Target entity description: OM10 is the station code assigned to Jiyugaoka Station on the Tokyu railway network in Tokyo, Japan.
  • A. OM
    OM is an American experimental rock band known for its hypnotic, drone-influenced sound and spiritually themed compositions.
  • B. OM
    OM is the post-nominal abbreviation used by members of the Order of Merit, a prestigious British honor recognizing distinguished service in the armed forces, science, art, literature, or the promotion of culture.
  • C. OM
    OM is the commonly used abbreviation for Olympique de Marseille, a major French professional football club based in Marseille.
  • D. OM
    OM is the post-nominal abbreviation used by recipients of the Order of the Cross of Terra Mariana, a high state decoration of Estonia typically awarded to foreign dignitaries for services to the Estonian state.
  • E. OM
    OM was an Italian manufacturer known for producing vehicles such as the Milan series 1500 Peter Witt trams as well as trucks and other industrial transport equipment.
  • 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_69d886cc4170819093deddc7b8b4b6a7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d47a8444819081f1262eb7dbda40 completed April 18, 2026, 6:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00dc241ec88190a3e868ab88b26f09 completed May 10, 2026, 7:27 p.m.
NEDg Description generation batch_6a0114d7d03c8190943777f4eac956fd completed May 10, 2026, 11:29 p.m.
NED2 Entity disambiguation (via description) batch_6a01159a08b081908fc82adc7cca532a completed May 10, 2026, 11:32 p.m.
Created at: April 10, 2026, 5:33 a.m.