Triple

T8883756
Position Surface form Disambiguated ID Type / Status
Subject Saint-Lazare E211473 entity
Predicate metroLine P848 FINISHED
Object Line 3
Line 3 is one of the main lines of the Paris Métro, running in an east–west direction across the city and serving several central districts.
E765411 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: [Saint-Lazare, metroLine, Line 3]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Line 3
Context triple: [Saint-Lazare, metroLine, 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 north–south route of the Seoul Metropolitan Subway system, connecting key residential and commercial districts across the city and into surrounding areas.
  • C. Line 3
    Line 3 is a major rapid transit route of the STC Metro system, serving key districts along its corridor.
  • D. Line 3
    Line 3 is a major line of the Saint Petersburg Metro system, serving as one of the city's primary rapid transit routes.
  • E. Line 3
    Line 3 is a major line of the Sofia Metro rapid transit system in Sofia, Bulgaria, serving key residential and commercial areas of the city.
  • 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: [Saint-Lazare, metroLine, Line 3]
Generated description
Line 3 is one of the main lines of the Paris Métro, running in an east–west direction across the city and serving several central districts.
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 Paris Métro, running in an east–west direction across the city and serving several central districts.
  • A. 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.
  • B. 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.
  • C. 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.
  • D. Line 3
    Line 3 is a major rapid transit route of the STC Metro system, serving key districts along its corridor.
  • E. Line 3
    Line 3 is a major north–south route of the Seoul Metropolitan Subway system, connecting key residential and commercial districts across the city and into surrounding areas.
  • 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_69ca838f9e20819096ab1f236a70381a completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc616b2d988190b923ef1e33aab787 completed April 1, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfabd254148190b5ea3d308fe96851 completed April 3, 2026, noon
NEDg Description generation batch_69cfafb878048190b311342fbd93145e completed April 3, 2026, 12:16 p.m.
NED2 Entity disambiguation (via description) batch_69cfb0392038819083f730a45787260b completed April 3, 2026, 12:19 p.m.
Created at: March 30, 2026, 6:53 p.m.