Triple

T22277632
Position Surface form Disambiguated ID Type / Status
Subject Urunga railway station E550647 entity
Predicate hasStationCode P1289 FINISHED
Object URG
URG is the station code for Urunga railway station, a regional train stop in New South Wales, Australia.
 E1528994 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: URG | Statement: [Urunga railway station, hasStationCode, URG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: URG
Context triple: [Urunga railway station, hasStationCode, URG]
  • A. URG
    URG is the station code used to identify Uruguaiana Station on the Rio de Janeiro Metro system.
  • B. UR
    UR is the vehicle registration code used on license plates for vehicles registered in the Swiss canton of Uri.
  • C. UR
    UR is the commonly used abbreviation for the University of Redlands, a private liberal arts university in Redlands, California.
  • D. UR
    UR (Utbildningsradion) is Sweden’s public educational broadcasting company, producing and distributing educational radio, TV, and digital content.
  • E. HUR
    HUR is the station code used to identify Hurstville railway station in the Sydney Trains network.
  • 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: URG
Triple: [Urunga railway station, hasStationCode, URG]
Generated description
URG is the station code for Urunga railway station, a regional train stop in New South Wales, Australia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: URG
Target entity description: URG is the station code for Urunga railway station, a regional train stop in New South Wales, Australia.
  • A. URG
    URG is the station code used to identify Uruguaiana Station on the Rio de Janeiro Metro system.
  • B. UR
    UR is the vehicle registration code used on license plates for vehicles registered in the Swiss canton of Uri.
  • C. UR
    UR is the commonly used abbreviation for the University of Redlands, a private liberal arts university in Redlands, California.
  • D. UR
    UR (Utbildningsradion) is Sweden’s public educational broadcasting company, producing and distributing educational radio, TV, and digital content.
  • E. HUR
    HUR is the station code used to identify Hurstville railway station in the Sydney Trains network.
  • 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_69e11e44d538819097c6b8f333af3352 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f14ea8a6688190bcd168670420ec59 completed April 29, 2026, 12:19 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0abca612008190bcb1c38523223ed7 completed May 18, 2026, 7:15 a.m.
NEDg Description generation batch_6a0ac0d258d88190a73427e02373a4bc completed May 18, 2026, 7:33 a.m.
NED2 Entity disambiguation (via description) batch_6a0ac1e9666c8190b183c069deaf487c completed May 18, 2026, 7:38 a.m.
Created at: April 16, 2026, 8:40 p.m.