Triple

T1187396
Position Surface form Disambiguated ID Type / Status
Subject Rue de Valois E25277 entity
Predicate hasNeighbourhoodCharacter P9356 FINISHED
Object central Paris historic district LITERAL 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: central Paris historic district | Statement: [Rue de Valois, hasNeighbourhoodCharacter, central Paris historic district]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasNeighbourhoodCharacter
Context triple: [Rue de Valois, hasNeighbourhoodCharacter, central Paris historic district]
  • A. hasNeighbourhood
    Indicates that one entity is located within, or is associated with, a particular neighborhood area of another entity.
  • B. neighborhoodCharacteristic chosen
    Indicates that a particular characteristic, feature, or quality is associated with or describes a given neighborhood.
  • C. hasSuburbanCharacter
    Indicates that something possesses qualities or features typically associated with suburban areas, such as lower density, residential focus, and car-oriented development.
  • D. hasNearbyCommunity
    Indicates that one entity has another community located close to it in geographic or spatial terms.
  • E. neighborhood
    Indicates that one entity is located in close spatial proximity to another, typically within the same local area or district.
  • F. None of above.

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_69a49427d98881908646d6c63b8cea1e completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bd5578b08190bbe4089857fbf166 completed March 1, 2026, 10:27 p.m.
PD Predicate disambiguation batch_69a4bb5bacc481909e8dfd5215e4711a completed March 1, 2026, 10:19 p.m.
Created at: March 1, 2026, 7:45 p.m.