Triple

T21832018
Position Surface form Disambiguated ID Type / Status
Subject Uruguaiana Station (Rio de Janeiro Metro) E539019 entity
Predicate hasStationCode P1289 FINISHED
Object URG
URG is the station code used to identify Uruguaiana Station on the Rio de Janeiro Metro system.
 E1504292 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: [Uruguaiana Station (Rio de Janeiro Metro), hasStationCode, URG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: URG
Context triple: [Uruguaiana Station (Rio de Janeiro Metro), hasStationCode, URG]
  • A. UR
    UR is the vehicle registration code used on license plates for vehicles registered in the Swiss canton of Uri.
  • B. UR
    UR is the commonly used abbreviation for the University of Redlands, a private liberal arts university in Redlands, California.
  • C. UR
    UR (Utbildningsradion) is Sweden’s public educational broadcasting company, producing and distributing educational radio, TV, and digital content.
  • D. HUR
    HUR is the station code used to identify Hurstville railway station in the Sydney Trains network.
  • E. URS
    URS was the FIFA country code used to represent the Soviet Union national football team in international competitions.
  • 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: [Uruguaiana Station (Rio de Janeiro Metro), hasStationCode, URG]
Generated description
URG is the station code used to identify Uruguaiana Station on the Rio de Janeiro Metro system.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: URG
Target entity description: URG is the station code used to identify Uruguaiana Station on the Rio de Janeiro Metro system.
  • A. UR
    UR is the vehicle registration code used on license plates for vehicles registered in the Swiss canton of Uri.
  • B. UR
    UR is the commonly used abbreviation for the University of Redlands, a private liberal arts university in Redlands, California.
  • C. UR
    UR (Utbildningsradion) is Sweden’s public educational broadcasting company, producing and distributing educational radio, TV, and digital content.
  • D. HUR
    HUR is the station code used to identify Hurstville railway station in the Sydney Trains network.
  • E. URS
    URS was the FIFA country code used to represent the Soviet Union national football team in international competitions.
  • 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_69e0c475cda88190987d08f23caebdc1 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69f0a7a4d2d0819088ded045caeab52d completed April 28, 2026, 12:27 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a45eafcc08190bc104ab80428a497 completed May 17, 2026, 10:49 p.m.
NEDg Description generation batch_6a0a46c771348190aa1fa8b3b8fcac3f completed May 17, 2026, 10:52 p.m.
NED2 Entity disambiguation (via description) batch_6a0a47bf44c881908504b2537649adf6 completed May 17, 2026, 10:57 p.m.
Created at: April 16, 2026, 6:55 p.m.