Triple

T3758673
Position Surface form Disambiguated ID Type / Status
Subject Tunis Metro E82109 entity
Predicate hasLine P35 FINISHED
Object Line 4
Line 4 is a route of the Tunis Metro light rail network serving parts of the Tunis metropolitan area in Tunisia.
E390791 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 4 | Statement: [Tunis Metro, hasLine, Line 4]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Line 4
Context triple: [Tunis Metro, hasLine, Line 4]
  • A. Line 4
    Line 4 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 4
    Line 4 is a major line of the Santiago Metro in Chile, serving key residential and commercial areas in the southeastern part of the city.
  • C. Line 4
    Line 4 is a major north–south rapid transit route in the Beijing Subway system, serving key commercial, residential, and university areas of the city.
  • D. Line 4
    Line 4 is a rapid transit line of the Guangzhou Metro system in Guangzhou, China, serving key urban and suburban areas along its north–south corridor.
  • E. Line 4
    Line 4 is one of the main lines of the Tehran Metro rapid transit system, serving key east–west corridors across Iran’s capital 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 4
Triple: [Tunis Metro, hasLine, Line 4]
Generated description
Line 4 is a route of the Tunis Metro light rail network serving parts of the Tunis metropolitan area in Tunisia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Line 4
Target entity description: Line 4 is a route of the Tunis Metro light rail network serving parts of the Tunis metropolitan area in Tunisia.
  • A. Line 4
    Line 4 is a rapid transit line of the Barcelona Metro network that serves several central and coastal neighborhoods in the city.
  • B. Line 4
    Line 4 is one of the main north–south lines of the Paris Métro, known for serving central Paris and connecting key railway stations and neighborhoods.
  • C. Line 4
    Line 4 is a planned rapid transit route within the future Ho Chi Minh City Metro system in Vietnam.
  • D. Line 4
    Line 4 is a circular rapid transit route of the Shanghai Metro system that loops around central districts and provides key transfer connections across the network.
  • E. Line 4
    Line 4 is a major line of the Santiago Metro in Chile, serving key residential and commercial areas in the southeastern part 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_69ad8b1db40081908b61ffa6b78afd4d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcbc20b20819095fedf803aadc53a completed March 8, 2026, 7:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4fb0fc6248190b2f4adf3fd2d73d9 completed March 14, 2026, 6:07 a.m.
NEDg Description generation batch_69b4fc9107fc81908805740b98c7ae3e completed March 14, 2026, 6:13 a.m.
NED2 Entity disambiguation (via description) batch_69b4fced2620819083ab1e75393ba2ba completed March 14, 2026, 6:15 a.m.
Created at: March 8, 2026, 3:35 p.m.