Triple

T22204733
Position Surface form Disambiguated ID Type / Status
Subject Exelmans E548774 entity
Predicate hasFareSystem P395 FINISHED
Object Navigo NE NERFINISHED

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: [Exelmans, hasFareSystem, Navigo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Navigo
Context triple: [Exelmans, hasFareSystem, 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. Navia
    Navia is a coastal town and municipality in Asturias, northern Spain, known for its port, beaches, and role as a local commercial and tourist center.
  • C. Amasra
    Amasra is a historic coastal town and popular tourist destination on Turkey’s Black Sea coast, known for its scenic harbor, beaches, and ancient fortifications.
  • D. 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.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e3ecc7c8190b5f94cd8f42e9d37 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f12b27451081908c29d1915b6c4229 completed April 28, 2026, 9:48 p.m.
Created at: April 16, 2026, 8:36 p.m.