Triple

T18807591
Position Surface form Disambiguated ID Type / Status
Subject SL metro E459919 entity
Predicate hasLine P35 FINISHED
Object Line 14
Line 14 is a route of the SL metro system, serving as one of its numbered rapid transit lines.
 E1348906 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 14 | Statement: [SL metro, hasLine, Line 14]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line 14
Context triple: [SL metro, hasLine, Line 14]
  • A. Line 14
    Line 14 is a major rapid transit line of the Beijing Subway system that serves multiple key residential and commercial districts across the city.
  • B. Line 14
    Line 14 is a rapid transit line of the Guangzhou Metro system in Guangzhou, China, serving suburban and outlying districts with high-speed, longer-distance urban rail service.
  • C. Line 14
    Line 14 is a rapid transit line of the Shanghai Metro system that serves as one of the city's major east–west corridors.
  • D. Line 14
    Line 14 is a fully automated, high-capacity line of the Paris Métro known for its modern trains and role in relieving congestion on central routes.
  • E. Line 14
    Line 14 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving as part of the city's expanding urban rail network.
  • 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 14
Triple: [SL metro, hasLine, Line 14]
Generated description
Line 14 is a route of the SL metro system, serving as one of its numbered rapid transit lines.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Line 14
Target entity description: Line 14 is a route of the SL metro system, serving as one of its numbered rapid transit lines.
  • A. Line 14
    Line 14 is a major rapid transit line of the Beijing Subway system that serves multiple key residential and commercial districts across the city.
  • B. Line 14
    Line 14 is a rapid transit line of the Guangzhou Metro system in Guangzhou, China, serving suburban and outlying districts with high-speed, longer-distance urban rail service.
  • C. Line 14
    Line 14 is a rapid transit line of the Shanghai Metro system that serves as one of the city's major east–west corridors.
  • D. Line 14
    Line 14 is a fully automated, high-capacity line of the Paris Métro known for its modern trains and role in relieving congestion on central routes.
  • E. Line 14
    Line 14 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving as part of the city's expanding urban rail network.
  • 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_69d8d398c7d4819091cb2f7e48948aeb completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5a3d8ab9c819097834eac798ce810 completed April 20, 2026, 3:56 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0582a149688190b60f8708569d059f completed May 14, 2026, 8:06 a.m.
NEDg Description generation batch_6a05895b8aa48190ab277b3914318e17 completed May 14, 2026, 8:35 a.m.
NED2 Entity disambiguation (via description) batch_6a0589af5e2c81909b03ffb3c1adb8c5 completed May 14, 2026, 8:37 a.m.
Created at: April 10, 2026, 11:53 a.m.