Triple

T14888351
Position Surface form Disambiguated ID Type / Status
Subject Budapest Metro Line 3 E359687 entity
Predicate alsoKnownAs P39 FINISHED
Object Blue Line
Blue Line is the common name for Budapest Metro Line 3, one of the main rapid transit lines serving Hungary’s capital city.
E1126166 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: Blue Line | Statement: [Budapest Metro Line 3, alsoKnownAs, Blue Line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Blue Line
Context triple: [Budapest Metro Line 3, alsoKnownAs, Blue Line]
  • A. Blue Line
    The Blue Line is one of the color-coded rapid transit routes in the Washington Metro system, running through key parts of Washington, D.C. and its Virginia suburbs.
  • B. Blue Line
    The Blue Line is a light rail route in the Dallas Area Rapid Transit (DART) system serving key neighborhoods and suburbs in the Dallas–Fort Worth metroplex.
  • C. Blue Line
    The Blue Line is one of the primary routes of the MetroLink light rail system serving the St. Louis metropolitan area.
  • D. Blue Line
    The Blue Line is a planned rapid transit corridor of Bengaluru’s Namma Metro network intended to expand connectivity across additional parts of the city.
  • E. Blue Line
    The Blue Line is one of the aerial cable car routes in La Paz–El Alto’s Mi Teleférico urban transit system, providing high-altitude public transportation across the Bolivian cities.
  • 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: Blue Line
Triple: [Budapest Metro Line 3, alsoKnownAs, Blue Line]
Generated description
Blue Line is the common name for Budapest Metro Line 3, one of the main rapid transit lines serving Hungary’s capital city.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Blue Line
Target entity description: Blue Line is the common name for Budapest Metro Line 3, one of the main rapid transit lines serving Hungary’s capital city.
  • A. Blue Line
    Blue Line is the common name for Line 3 of the Athens Metro, a major rapid transit route serving the Athens metropolitan area in Greece.
  • B. Blue Line
    The Blue Line is one of the main lines of the Lisbon Metro system, serving key central and northern areas of Portugal’s capital city.
  • C. Blue Line
    The Blue Line is a planned rapid transit corridor of Bengaluru’s Namma Metro network intended to expand connectivity across additional parts of the city.
  • D. Blue Line
    The Blue Line is one of the Montreal Metro’s rapid transit lines, running east–west to serve several central and northeastern neighborhoods of the city.
  • E. Blue Line
    The Blue Line is one of the main corridors of the Chennai Metro rapid transit system, connecting key areas of Chennai, India.
  • 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_69d827980cbc8190a0c569ae3940a1d9 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69ded5f6cf5c8190b6b28f58fafe5d59 completed April 15, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe6b5f22c08190a9530cbd78cfc801 completed May 8, 2026, 11:01 p.m.
NEDg Description generation batch_69fe6f9b33748190aee0c27879866ca1 completed May 8, 2026, 11:19 p.m.
NED2 Entity disambiguation (via description) batch_69fe703e8c28819081b7bfe638a2202e completed May 8, 2026, 11:22 p.m.
Created at: April 10, 2026, 2:08 a.m.