Triple

T15995031
Position Surface form Disambiguated ID Type / Status
Subject Hénon metro station E387939 entity
Predicate fareSystem P395 FINISHED
Object TCL ticketing system E487753 NE FINISHED

How this triple was built (2 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: TCL ticketing system | Statement: [Hénon metro station, fareSystem, TCL ticketing system]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: TCL ticketing system
Context triple: [Hénon metro station, fareSystem, TCL ticketing system]
  • A. TCL fare system chosen
    The TCL fare system is the integrated public transport ticketing and pricing network used across Lyon’s buses, trams, and metro services.
  • B. TCL bus network
    The TCL bus network is the public bus system serving Lyon and its surrounding metropolitan area in France.
  • C. TCK
    TCK is a suite of tests, tools, and documentation used to verify that a technology implementation complies with a specific Java or Jakarta EE specification.
  • D. TCK
    TCK is a well-known horse racing track in Tokyo, Japan, officially known as Ohi Racecourse.
  • E. CTC
    CTC is the national trade union federation of Cuba that represents and coordinates the activities of Cuban workers across various sectors.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d86daa562c81908aacc179c0fe8fb5 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e15785fad48190af0556e7ddfd29c5 completed April 16, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffc3d5d72081908aa235c5ad9b5707 completed May 9, 2026, 11:31 p.m.
Created at: April 10, 2026, 4:55 a.m.