Triple

T5074182
Position Surface form Disambiguated ID Type / Status
Subject Lille Metro E114351 entity
Predicate line P1293 FINISHED
Object Line 1
Line 1 is a major automated light metro route of the Lille Metro system in northern France, connecting key districts across the city and its suburbs.
E494065 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: [Lille Metro, line, Line 1]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Line 1
Context triple: [Lille Metro, line, 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 a major north–south rapid transit line of the Shanghai Metro and one of the system’s oldest and busiest routes.
  • C. Line 1
    Line 1 is one of the main lines of the Barcelona Metro rapid transit system, running on a largely east–west axis and serving several key districts of the city.
  • D. Line 1
    Line 1 is a major Beijing Subway route that runs north–south through the city’s central axis, serving key commercial and historical areas.
  • E. Line 1
    Line 1 is the first and main automated metro line of the Turin Metro system in Turin, Italy, connecting key areas of the city along an underground route.
  • 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: [Lille Metro, line, Line 1]
Generated description
Line 1 is a major automated light metro route of the Lille Metro system in northern France, connecting key districts across the city and its suburbs.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Line 1
Target entity description: Line 1 is a major automated light metro route of the Lille Metro system in northern France, connecting key districts across the city and its suburbs.
  • A. Line 1
    Line 1 is a major east–west rapid transit route of the Brussels Metro system, connecting key districts across the Belgian capital.
  • B. Line 1
    Line 1 is the first and main automated metro line of the Turin Metro system in Turin, Italy, connecting key areas of the city along an underground route.
  • C. Line 1
    Line 1 is a principal light rail route of the Tunis Metro system, serving key districts within the Tunis metropolitan area.
  • D. 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.
  • E. Line 1
    Line 1 is one of the main lines of the Barcelona Metro rapid transit system, running on a largely east–west axis and serving several 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_69bd443cf28c8190ad371d603563dbdd completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd74d0be1c819081b26235fe602a30 completed March 20, 2026, 4:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69beba689ee081909a6bb75c6da07db5 completed March 21, 2026, 3:34 p.m.
NEDg Description generation batch_69bebc0907fc8190904f5e0f63b6213f completed March 21, 2026, 3:40 p.m.
NED2 Entity disambiguation (via description) batch_69bebc5a79608190bad0fdd6e5a099ef completed March 21, 2026, 3:42 p.m.
Created at: March 20, 2026, 1:39 p.m.