Triple

T10984951
Position Surface form Disambiguated ID Type / Status
Subject Tverskaya E259604 entity
Predicate metroLineColor P34412 FINISHED
Object green line
The green line refers to the Zamoskvoretskaya Line, one of the busiest and oldest lines of the Moscow Metro system.
 E898155 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: green line | Statement: [Tverskaya, metroLineColor, green line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: green line
Context triple: [Tverskaya, metroLineColor, green line]
  • A. Green line
    The Green line is one of the main color-coded routes in the Stockholm metro system, serving numerous central and suburban stations across the city.
  • B. Green line
    The Green line is a major rapid transit route on the Barcelona Metro system, serving numerous central and outlying neighborhoods across the city.
  • C. Green (as part of Green Line)
    Green (as part of the Green Line) is the color designation used for Chicago's Green Line rapid transit route, including its Englewood Branch, within the Chicago 'L' system.
  • D. Blue line
    The Blue line is one of the main lines of the Stockholm metro system, connecting central Stockholm with several northern and western suburbs.
  • E. Red line
    The Red line is one of the main color-coded routes of the Stockholm metro system, serving numerous central and suburban stations across 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: green line
Triple: [Tverskaya, metroLineColor, green line]
Generated description
The green line refers to the Zamoskvoretskaya Line, one of the busiest and oldest lines of the Moscow Metro system.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: green line
Target entity description: The green line refers to the Zamoskvoretskaya Line, one of the busiest and oldest lines of the Moscow Metro system.
  • A. Green line
    The Green line is one of the main color-coded routes in the Stockholm metro system, serving numerous central and suburban stations across the city.
  • B. Green line
    The Green line is a major rapid transit route on the Barcelona Metro system, serving numerous central and outlying neighborhoods across the city.
  • C. Green (as part of Green Line)
    Green (as part of the Green Line) is the color designation used for Chicago's Green Line rapid transit route, including its Englewood Branch, within the Chicago 'L' system.
  • D. Blue line
    The Blue line is one of the main lines of the Stockholm metro system, connecting central Stockholm with several northern and western suburbs.
  • E. Red line
    The Red line is one of the main color-coded routes of the Stockholm metro system, serving numerous central and suburban stations across 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_69d6aa895f4c8190887a15460ef622f4 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d772ed1eb88190b7333b746f76a088 completed April 9, 2026, 9:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69e344d860b08190a035570191c54d7c completed April 18, 2026, 8:46 a.m.
NEDg Description generation batch_69e3556e8b408190a02a1fe194ae5750 completed April 18, 2026, 9:57 a.m.
NED2 Entity disambiguation (via description) batch_69e3591ecd548190b049ce95fe3f86d9 completed April 18, 2026, 10:12 a.m.
Created at: April 8, 2026, 9:24 p.m.