Triple

T15996330
Position Surface form Disambiguated ID Type / Status
Subject Line 3 (Tunis Metro) E387974 entity
Predicate lineDesignation P5539 FINISHED
Object Line 3
Line 3 is a light rail route of the Tunis Metro system in Tunis, Tunisia, serving key districts of the city as part of its urban public transport network.
E387974 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: [Line 3 (Tunis Metro), lineDesignation, Line 3]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Line 3
Context triple: [Line 3 (Tunis Metro), lineDesignation, 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 future rapid transit route of the Seville Metro intended to extend and improve the city’s urban rail network.
  • 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 rapid transit line of the Shijiazhuang Metro system in Shijiazhuang, Hebei, China.
  • 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: [Line 3 (Tunis Metro), lineDesignation, Line 3]
Generated description
Line 3 is a light rail route of the Tunis Metro system in Tunis, Tunisia, serving key districts of the city as part of its urban public transport network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Line 3
Target entity description: Line 3 is a light rail route of the Tunis Metro system in Tunis, Tunisia, serving key districts of the city as part of its urban public transport network.
  • A. Line 3 chosen
    Line 3 is one of the light rail routes of the Tunis Metro system, serving urban districts within the Tunis metropolitan area.
  • B. Line 3
    Line 3 is a major rapid transit route of the STC Metro system, serving key districts along its corridor.
  • 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 Paris Métro, running in an east–west direction across the city and serving several central districts.
  • E. 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.
  • 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_69d86daa562c81908aacc179c0fe8fb5 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1578709608190bae7bafa59280849 completed April 16, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffc3d79dec8190b02e003f93e5dad6 completed May 9, 2026, 11:31 p.m.
NEDg Description generation batch_69ffc50688ac8190b0911ce889254af3 completed May 9, 2026, 11:36 p.m.
NED2 Entity disambiguation (via description) batch_69ffc5902904819097a2c5efbde55882 completed May 9, 2026, 11:38 p.m.
Created at: April 10, 2026, 4:55 a.m.