Triple

T14350765
Position Surface form Disambiguated ID Type / Status
Subject Javel – André Citroën E355844 entity
Predicate namedAfter P63 FINISHED
Object Javel district E206229 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: Javel district | Statement: [Javel – André Citroën, namedAfter, Javel district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Javel district
Context triple: [Javel – André Citroën, namedAfter, Javel district]
  • A. Javel district chosen
    Javel district is a neighborhood in southwestern Paris known historically for its industrial past and riverside location along the Seine.
  • B. Omate District
    Omate District is an administrative district located within Peru's southern Andean region, known for its rural communities and highland landscapes.
  • C. Damous District
    Damous District is an administrative district in northern Algeria, located within Tipaza Province along the Mediterranean coast.
  • D. Tinta District
    Tinta District is an administrative district in Peru’s Cusco Region, located within Canchis Province and known for its Andean highland geography and traditional Quechua-speaking communities.
  • E. Mapo District
    Mapo District is a vibrant administrative and cultural area in western Seoul, South Korea, known for neighborhoods like Hongdae and its lively arts, nightlife, and dining scenes.
  • 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_69fd550aef908190a7ec49e409f92dc7 completed May 8, 2026, 3:14 a.m.
Created at: April 10, 2026, 1:14 a.m.