Triple

T9581907
Position Surface form Disambiguated ID Type / Status
Subject Mexico City Metrobús E231191 entity
Predicate hasLine P35 FINISHED
Object Line 1
Line 1 is the inaugural and one of the busiest routes of the Mexico City Metrobús bus rapid transit system, running along a major north–south corridor of the city.
E808672 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 1 | Statement: [Mexico City Metrobús, hasLine, Line 1]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Line 1
Context triple: [Mexico City Metrobús, hasLine, Line 1]
  • A. Line 1
    Line 1 is the oldest and one of the busiest lines of the Santiago Metro, running primarily east–west across central Santiago, Chile.
  • B. Line 1
    Line 1 is the oldest and one of the busiest lines of the Paris Métro, running primarily east–west through central Paris and serving many major landmarks.
  • C. Line 1
    Line 1 is the first and main rapid transit line of the Seville Metro system in Seville, Spain, connecting key districts across the metropolitan area.
  • D. Line 1
    Line 1 is the designation for the Red Line, one of the main rapid transit corridors of the Delhi Metro network in India.
  • E. Line 1
    Line 1 is a major Milan Metro line that serves key areas of the city, including Milano Cadorna station.
  • 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 1
Triple: [Mexico City Metrobús, hasLine, Line 1]
Generated description
Line 1 is the inaugural and one of the busiest routes of the Mexico City Metrobús bus rapid transit system, running along a major north–south corridor of the city.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Line 1
Target entity description: Line 1 is the inaugural and one of the busiest routes of the Mexico City Metrobús bus rapid transit system, running along a major north–south corridor of the city.
  • A. Line 1 chosen
    Line 1 is the inaugural corridor of the Mexico City Metrobús bus rapid transit system, serving as a major north–south transit route in the city.
  • B. Line 1
    Line 1 is the oldest and one of the busiest lines of the Mexico City Metro, running east–west across the city and serving many central, high-traffic stations.
  • C. Line 1
    Line 1 is the main north–south route of the Tehran Metro system, serving as one of its busiest and most important rapid transit lines.
  • D. Line 1
    Line 1 is a major north–south rapid transit line of the Shanghai Metro and one of the system’s oldest and busiest routes.
  • E. Line 1
    Line 1 is the first and one of the main rapid transit lines of the Nanjing Metro system in Nanjing, China, connecting key urban areas along a north–south corridor.
  • F. None of above.

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_69ca848161688190a68d514a0a9d5129 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd99cd59008190888eb11f00f61994 completed April 1, 2026, 10:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1790fbfb88190b1d12f5ed3d4ef7e completed April 4, 2026, 8:48 p.m.
NEDg Description generation batch_69d179a4006c81909e983559b5fd7698 completed April 4, 2026, 8:50 p.m.
NED2 Entity disambiguation (via description) batch_69d17a3aebfc8190acbe62ac900dd9e7 completed April 4, 2026, 8:53 p.m.
Created at: March 30, 2026, 8:05 p.m.