Triple

T22705335
Position Surface form Disambiguated ID Type / Status
Subject Passy Cemetery E561436 entity
Predicate municipalCemeteryOf P38888 FINISHED
Object City of Paris 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: City of Paris | Statement: [Passy Cemetery, municipalCemeteryOf, City of Paris]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Paris
Context triple: [Passy Cemetery, municipalCemeteryOf, City of Paris]
  • A. Ville de Paris
    Ville de Paris was a prominent French ship of the line, originally the flagship of Admiral de Grasse, that played a central role in major naval engagements during the American Revolutionary War.
  • B. Ville de Paris chosen
    Ville de Paris is the municipal government and administrative authority responsible for managing and providing public services in the city of Paris, France.
  • C. Parigi
    Parigi is a town located in the Vikarabad district of the Indian state of Telangana.
  • D. Parigi
    Parigi is a coastal town that serves as the administrative center of Parigi Moutong Regency in Central Sulawesi, Indonesia.
  • E. Paris
    Paris is a budget-oriented AMD Sempron processor core designed for entry-level desktop computing.
  • 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_69e2454f1348819088d83f420925a5c1 completed April 17, 2026, 2:35 p.m.
NER Named-entity recognition batch_69f178cdc93481908f85d04560f8c285 completed April 29, 2026, 3:19 a.m.
Created at: April 17, 2026, 3:16 p.m.