Triple

T12898331
Position Surface form Disambiguated ID Type / Status
Subject MRT Orange Line E308550 entity
Predicate lineDesignation P5539 FINISHED
Object Orange
Orange is the color used to identify and represent the MRT Orange Line in the transit system.
E832715 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: Orange | Statement: [MRT Orange Line, lineDesignation, Orange]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Orange
Context triple: [MRT Orange Line, lineDesignation, Orange]
  • A. Orange
    Orange is a historic town in southeastern France best known for giving its name and origin to the Dutch royal House of Orange-Nassau.
  • B. Orange
    Orange is a regional city in the Central Tablelands of New South Wales, Australia, known for its cool-climate wines, agriculture, and growing tourism industry.
  • C. Orange
    Orange is a small suburban village in Cuyahoga County, Ohio, known for its residential character and proximity to the Cleveland metropolitan area.
  • D. Orange
    Orange is a citrus-flavored sports drink variety known for its bright, tangy taste and association with energy and hydration.
  • E. Orange
    Orange was the original name of the town now known as Hillsborough in North Carolina, reflecting its early colonial-era identity.
  • 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: Orange
Triple: [MRT Orange Line, lineDesignation, Orange]
Generated description
Orange is the color used to identify and represent the MRT Orange Line in the transit system.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Orange
Target entity description: Orange is the color used to identify and represent the MRT Orange Line in the transit system.
  • A. Orange
    Orange is one of the color-coded lines of Miami’s Metrorail system, serving as a distinct route that includes Brickell station among its stops.
  • B. Orange
    Orange is one of the primary lines of the Washington Metro rapid transit system, serving multiple stations across the Washington, D.C. metropolitan area.
  • C. Orange chosen
    Orange is a bright, warm color commonly associated with energy, visibility, and caution, often used in transportation and signage systems.
  • D. Orange
    Orange is a citrus-flavored sports drink variety known for its bright, tangy taste and association with energy and hydration.
  • E. Orange
    Orange is the nickname and primary identity of Syracuse University's athletic teams, especially its prominent men's basketball program.
  • 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_69d7bdf7c1f0819098102569a8d8cbf5 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d9717f3fc48190b61c8f6f36cd0725 completed April 10, 2026, 9:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6a55f98c08190b8910b1443841fa7 completed May 3, 2026, 1:31 a.m.
NEDg Description generation batch_69f6a6179cdc8190976daa1384032445 completed May 3, 2026, 1:34 a.m.
NED2 Entity disambiguation (via description) batch_69f6a6cbec348190a96a0194b2d6be4b completed May 3, 2026, 1:37 a.m.
Created at: April 9, 2026, 5:40 p.m.