Triple

T2280471
Position Surface form Disambiguated ID Type / Status
Subject Barcelona Metro E51267 entity
Predicate fareMedium P1303 FINISHED
Object T-casual
T-casual is a popular single-person, multi-trip public transport ticket used across Barcelona’s integrated metro and bus network.
 E249589 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: T-casual | Statement: [Barcelona Metro, fareMedium, T-casual]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: T-casual
Context triple: [Barcelona Metro, fareMedium, T-casual]
  • A. TC
    TC is the Constitutional Court of Peru, the country’s highest body responsible for interpreting and safeguarding the constitution and constitutional rights.
  • B. TC
    TC is the two-letter ISO 3166-1 alpha-2 country code assigned to the Turks and Caicos Islands.
  • C. TC
    TC is the common abbreviation for the Trilateral Commission, a non-governmental policy discussion group that brings together leaders from North America, Europe, and Asia to address global issues.
  • D. TC
    TC is the standard abbreviation for the IEEE Transactions on Computers, a leading peer-reviewed journal covering research in computer science and engineering.
  • E. TCA
    TCA is the commonly used abbreviation for the Technical Cooperation Administration, a former U.S. government agency responsible for administering foreign aid and technical assistance programs.
  • 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: T-casual
Triple: [Barcelona Metro, fareMedium, T-casual]
Generated description
T-casual is a popular single-person, multi-trip public transport ticket used across Barcelona’s integrated metro and bus network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: T-casual
Target entity description: T-casual is a popular single-person, multi-trip public transport ticket used across Barcelona’s integrated metro and bus network.
  • A. TC
    TC is the two-letter ISO 3166-1 alpha-2 country code assigned to the Turks and Caicos Islands.
  • B. TC
    TC is the Constitutional Court of Peru, the country’s highest body responsible for interpreting and safeguarding the constitution and constitutional rights.
  • C. TC
    TC is the common abbreviation for the Trilateral Commission, a non-governmental policy discussion group that brings together leaders from North America, Europe, and Asia to address global issues.
  • D. TC
    TC is the standard abbreviation for the IEEE Transactions on Computers, a leading peer-reviewed journal covering research in computer science and engineering.
  • E. TCA
    TCA is the commonly used abbreviation for the Technical Cooperation Administration, a former U.S. government agency responsible for administering foreign aid and technical assistance programs.
  • 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_69a88b08e4308190bdac9aebcca1c91a completed March 4, 2026, 7:42 p.m.
NER Named-entity recognition batch_69abc21ac3d48190abef254e1c3f45e8 completed March 7, 2026, 6:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae71e48fb081908498f826167020a2 completed March 9, 2026, 7:08 a.m.
NEDg Description generation batch_69ae72bf22088190a2c111a71eb0dda7 completed March 9, 2026, 7:11 a.m.
NED2 Entity disambiguation (via description) batch_69ae731ab8bc819090fac5b311cb5fe0 completed March 9, 2026, 7:13 a.m.
Created at: March 4, 2026, 7:48 p.m.