Triple

T2302851
Position Surface form Disambiguated ID Type / Status
Subject Dublin Region E51769 entity
Predicate hasTransportSystem P473 FINISHED
Object Luas
Luas is Dublin’s modern light rail tram system, providing frequent urban and suburban public transport across the city and its surrounding areas.
 E253338 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: Luas | Statement: [Dublin Region, hasTransportSystem, Luas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Luas
Context triple: [Dublin Region, hasTransportSystem, Luas]
  • A. Laja
    Laja is a small Chilean city in the Biobío Region, known for its riverside setting and proximity to the Biobío River.
  • B. Laukahi
    Laukahi is a town located in the Madhubani district of the Indian state of Bihar.
  • C. Rangat
    Rangat is a coastal town and administrative hub located on Middle Andaman Island in the Andaman and Nicobar Islands, India.
  • D. Seloo
    Seloo is a town in the Indian state of Maharashtra, known as a local commercial and educational center within the Vidarbha region.
  • E. Lapa
    Lapa is a historic and bohemian neighborhood in Rio de Janeiro, Brazil, famous for its vibrant nightlife, samba clubs, and iconic aqueduct arches.
  • 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: Luas
Triple: [Dublin Region, hasTransportSystem, Luas]
Generated description
Luas is Dublin’s modern light rail tram system, providing frequent urban and suburban public transport across the city and its surrounding areas.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Luas
Target entity description: Luas is Dublin’s modern light rail tram system, providing frequent urban and suburban public transport across the city and its surrounding areas.
  • A. Laja
    Laja is a small Chilean city in the Biobío Region, known for its riverside setting and proximity to the Biobío River.
  • B. Laukahi
    Laukahi is a town located in the Madhubani district of the Indian state of Bihar.
  • C. Rangat
    Rangat is a coastal town and administrative hub located on Middle Andaman Island in the Andaman and Nicobar Islands, India.
  • D. Seloo
    Seloo is a town in the Indian state of Maharashtra, known as a local commercial and educational center within the Vidarbha region.
  • E. Lapa
    Lapa is a historic and bohemian neighborhood in Rio de Janeiro, Brazil, famous for its vibrant nightlife, samba clubs, and iconic aqueduct arches.
  • 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_69a88b0a9f248190bcff941463d8f65a completed March 4, 2026, 7:42 p.m.
NER Named-entity recognition batch_69abc5f101dc8190824346e6ae564e51 completed March 7, 2026, 6:30 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae7f332e788190b1dd4b8b0bbfe5d7 completed March 9, 2026, 8:05 a.m.
NEDg Description generation batch_69ae802a066881909aa4e7b00e29306f completed March 9, 2026, 8:09 a.m.
NED2 Entity disambiguation (via description) batch_69ae80c37ff48190a24b7806320ebc00 completed March 9, 2026, 8:11 a.m.
Created at: March 4, 2026, 7:49 p.m.