Triple

T1841557
Position Surface form Disambiguated ID Type / Status
Subject Paris Metro E41186 entity
Predicate hasLine P35 FINISHED
Object 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.
 E206989 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: [Paris Metro, hasLine, Line 1]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line 1
Context triple: [Paris Metro, 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 Mexico City Metro, running east–west across the city and serving many central, high-traffic stations.
  • C. Line 1
    Line 1 is a major rapid transit line of the Guangzhou Metro system in Guangzhou, China, serving as one of the city's primary east–west corridors.
  • 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 one of the main east–west rapid transit lines of the Beijing Subway, serving as a core corridor through central Beijing.
  • 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: [Paris Metro, hasLine, Line 1]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Line 1
Target entity description: 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.
  • A. 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.
  • B. 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.
  • C. 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.
  • D. 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.
  • E. Line 1
    Line 1 is one of the main east–west rapid transit lines of the Beijing Subway, serving as a core corridor through central Beijing.
  • 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_69a88647f9388190909bc36e795bdaec completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb03e7a7481909c5b902034390ef1 completed March 7, 2026, 4:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69adc9bb92a88190a00b102d3be0383c completed March 8, 2026, 7:10 p.m.
NEDg Description generation batch_69adcaf078a0819082c4bb48a3820ada completed March 8, 2026, 7:16 p.m.
NED2 Entity disambiguation (via description) batch_69adce8a68848190ab56f5df7311dbca completed March 8, 2026, 7:31 p.m.
Created at: March 4, 2026, 7:33 p.m.