Triple

T10609601
Position Surface form Disambiguated ID Type / Status
Subject Valencia B E275971 entity
Predicate stadiumLocation P40 FINISHED
Object Paterna E448265 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: Paterna | Statement: [Valencia B, stadiumLocation, Paterna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paterna
Context triple: [Valencia B, stadiumLocation, Paterna]
  • A. Paterna chosen
    Paterna is a municipality in eastern Spain known for its proximity to the city of Valencia and its mix of industrial activity and residential areas.
  • B. Alcorcón
    Alcorcón is a suburban city in central Spain that forms part of the metropolitan area of Madrid.
  • C. Valencia
    Valencia is a municipality in the Philippine province of Negros Oriental known for its cool climate, geothermal energy resources, and natural attractions such as waterfalls and mountain landscapes.
  • D. Valencia
    Valencia is a city in Ecuador that serves as the capital of Los Ríos Province’s Valencia Canton and is known for its agricultural surroundings and tropical climate.
  • E. Valencia
    Valencia was the original working title for the 2016 psychological thriller film "10 Cloverfield Lane."
  • 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_69d6aaf948d88190806cc3a8c47a3fb2 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d6df4d0a6881909fea20378085173d completed April 8, 2026, 11:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69e154679bb88190b2fffeea74d1fc50 completed April 16, 2026, 9:28 p.m.
Created at: April 8, 2026, 7:32 p.m.