Triple

T2280431
Position Surface form Disambiguated ID Type / Status
Subject Barcelona Metro E51267 entity
Predicate hasLine P35 FINISHED
Object Line 3
Line 3 is one of the main lines of the Barcelona Metro system, running through central parts of the city and connecting several key stations and neighborhoods.
E254040 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: Line 3 | Statement: [Barcelona Metro, hasLine, Line 3]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Line 3
Context triple: [Barcelona Metro, hasLine, Line 3]
  • A. Line 3
    Line 3 is a route of Mexico City’s Metrobús bus rapid transit system that serves key corridors with dedicated lanes and high-capacity articulated buses.
  • B. Line 3
    Line 3 is a major rapid transit route of the Guangzhou Metro system, known for its high passenger volume and key role in connecting central urban areas with the airport and suburban districts.
  • C. Line 3
    Line 3 is a rapid transit line of the Toronto subway system, commonly known as the Scarborough RT, that served the Scarborough district.
  • D. Line 3
    Line 3 is a major trolleybus route within Geneva’s public transport system, connecting key districts of the city.
  • E. Line 3
    Line 3 is one of the main lines of the Mexico City Metro system, running in a generally north–south direction and serving several key residential and commercial areas.
  • 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: Line 3
Triple: [Barcelona Metro, hasLine, Line 3]
Generated description
Line 3 is one of the main lines of the Barcelona Metro system, running through central parts of the city and connecting several key stations and neighborhoods.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Line 3
Target entity description: Line 3 is one of the main lines of the Barcelona Metro system, running through central parts of the city and connecting several key stations and neighborhoods.
  • A. Line 3
    Line 3 is a major line of the Moscow Metro system, known for serving central Moscow and connecting key residential and commercial districts.
  • B. Line 3
    Line 3 is a major north–south rapid transit route of the Shanghai Metro system, known for its elevated tracks and extensive coverage across the city.
  • C. Line 3
    Line 3 is a major north–south route of the Tehran Metro system, connecting key residential and commercial areas across the city.
  • D. Line 3
    Line 3 is one of the main lines of the Mexico City Metro system, running in a generally north–south direction and serving several key residential and commercial areas.
  • E. Line 3
    Line 3 is a major trolleybus route within Geneva’s public transport system, connecting key districts of the city.
  • 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_69a88b08e4308190bdac9aebcca1c91a completed March 4, 2026, 7:42 p.m.
NER Named-entity recognition batch_69abc21ac3d48190abef254e1c3f45e8 completed March 7, 2026, 6:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae7f11ea248190be3092985edcb29b completed March 9, 2026, 8:04 a.m.
NEDg Description generation batch_69ae80be3354819088486da5e4c05909 completed March 9, 2026, 8:11 a.m.
NED2 Entity disambiguation (via description) batch_69ae8171d1fc8190bc95d1eedc629294 completed March 9, 2026, 8:14 a.m.
Created at: March 4, 2026, 7:48 p.m.