Triple

T16089541
Position Surface form Disambiguated ID Type / Status
Subject Line C (Prague Metro) E390323 entity
Predicate hasLineCode P19896 FINISHED
Object C
C is a metro line of the Prague Metro system, serving as one of the main north–south rapid transit routes through the city.
 E1192864 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: C | Statement: [Line C (Prague Metro), hasLineCode, C]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: C
Context triple: [Line C (Prague Metro), hasLineCode, C]
  • A. C
    C is a foundational, general-purpose programming language known for its efficiency, low-level memory access, and influence on many later languages such as C++, Java, and Python.
  • B. C
    C is a local service on the New York City Subway that runs along the Eighth Avenue Line in Manhattan and continues through Brooklyn.
  • C. C
    C is the New York Stock Exchange ticker symbol for Citigroup Inc., a major global financial services and banking corporation.
  • D. C
    C is a Copenhagen S-train commuter rail line that runs through central Copenhagen and connects key suburban areas in the metropolitan network.
  • E. C
    C is a tram route designation used in the Strasbourg tramway network in France.
  • 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: C
Triple: [Line C (Prague Metro), hasLineCode, C]
Generated description
C is a metro line of the Prague Metro system, serving as one of the main north–south rapid transit routes through the city.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: C
Target entity description: C is a metro line of the Prague Metro system, serving as one of the main north–south rapid transit routes through the city.
  • A. C
    C is a Copenhagen S-train commuter rail line that runs through central Copenhagen and connects key suburban areas in the metropolitan network.
  • B. C
    C is a light rail service designation used by the Los Angeles Metro system for one of its primary rail lines.
  • C. C
    C is a tram route designation used in the Strasbourg tramway network in France.
  • D. C
    C is a local service on the New York City Subway that runs along the Eighth Avenue Line in Manhattan and continues through Brooklyn.
  • E. C
    C is a foundational, general-purpose programming language known for its efficiency, low-level memory access, and influence on many later languages such as C++, Java, and Python.
  • 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_69d87f198bc48190a8b7e53ca15b7ead completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e1845161908190adca2af94710b2cc completed April 17, 2026, 12:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffe490d494819081f812811f032702 completed May 10, 2026, 1:51 a.m.
NEDg Description generation batch_69ffe63f757c81908c7dc3c5ae3075c6 completed May 10, 2026, 1:58 a.m.
NED2 Entity disambiguation (via description) batch_69ffe6b3f25481908dd4b6108b5d95c0 completed May 10, 2026, 2 a.m.
Created at: April 10, 2026, 4:59 a.m.