Triple

T12002640
Position Surface form Disambiguated ID Type / Status
Subject Yolanda Díaz E285702 entity
Predicate memberOf P10 FINISHED
Object Sumar E593246 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: Sumar | Statement: [Yolanda Díaz, memberOf, Sumar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sumar
Context triple: [Yolanda Díaz, memberOf, Sumar]
  • A. Sumar chosen
    Sumar is a Spanish left-wing political platform and electoral alliance led by Yolanda Díaz that brings together various progressive parties and movements.
  • B. Sumaré
    Sumaré is a municipality in southeastern Brazil located in the interior region of the state of São Paulo, known for its industrial activity and integration into the Campinas metropolitan area.
  • C. Tumeremo
    Tumeremo is a mining town in southeastern Venezuela known for its gold deposits and location within Bolívar State.
  • D. Diez
    Diez is a small historic town in western Germany’s Rhineland-Palatinate, known for its picturesque setting on the Lahn River and its prominent hilltop castle.
  • E. Asomante
    Asomante is a rural barrio (district) of the municipality of Aibonito in Puerto Rico, known for its mountainous landscape and cool climate.
  • 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_69d6ab45a368819084fce08bf0dc3705 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903c36b248190b446b17def94885b completed April 10, 2026, 2:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69f48ae4733c81909956cc8d6bae343a completed May 1, 2026, 11:13 a.m.
Created at: April 8, 2026, 9:46 p.m.