Triple

T14350850
Position Surface form Disambiguated ID Type / Status
Subject Duroc station E355846 entity
Predicate ticketingSystem P3383 FINISHED
Object Navigo E227653 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: Navigo | Statement: [Duroc station, ticketingSystem, Navigo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Navigo
Context triple: [Duroc station, ticketingSystem, Navigo]
  • A. Navigo chosen
    Navigo is the contactless smart card ticketing system used for public transportation across the Île-de-France region, including Paris.
  • B. Ussita
    Ussita is a small mountain town in Italy’s Marche region, known for its location in the Sibillini Mountains and its traditional rural character.
  • C. Maleva
    Maleva is the wise Romani woman and mother of the original Wolf Man who serves as a mystical guide and bearer of the werewolf curse’s lore in Universal’s classic horror films.
  • D. Taganga
    Taganga is a small fishing village and popular backpacker destination on Colombia’s Caribbean coast, known for its beaches, diving, and proximity to Tayrona National Natural Park.
  • E. Riva
    Riva is a coastal neighborhood and popular recreational area on the Asian side of Istanbul, known for its beaches, river, and natural scenery.
  • 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_69d82790a7e08190877e2d349b2e8d8e completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de8f4e1e588190bdc7aaf7a2819948 completed April 14, 2026, 7:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd4c4335e481909d4db39b8d25edc9 completed May 8, 2026, 2:36 a.m.
Created at: April 10, 2026, 1:14 a.m.