Triple

T282581
Position Surface form Disambiguated ID Type / Status
Subject Spanish Army E5821 entity
Predicate garrison P75 FINISHED
Object Madrid E4617 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: Madrid | Statement: [Spanish Army, garrison, Madrid]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Madrid
Context triple: [Spanish Army, garrison, Madrid]
  • A. Madrid chosen
    Madrid is the capital and largest city of Spain, renowned for its rich cultural heritage, historic architecture, and vibrant arts and nightlife scenes.
  • B. Madrid
    Madrid is a municipality in the Cundinamarca department of Colombia, located near Bogotá and known for its floriculture and agricultural production.
  • C. Barcelona
    Barcelona is a major Spanish Mediterranean city renowned for its distinctive Catalan culture, Gaudí architecture, and vibrant arts and nightlife scenes.
  • D. Valencia
    Valencia is a major Spanish coastal city known for its historic architecture, vibrant culture, and significant role as a key Mediterranean trade and tourism hub.
  • E. Seville, Spain
    Seville, Spain is a historic Andalusian city renowned for its Moorish-influenced architecture, vibrant flamenco culture, and landmarks such as the Seville Cathedral and the Alcázar.
  • 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_69a25946a7ac8190a78871c210213272 completed Feb. 28, 2026, 2:56 a.m.
NER Named-entity recognition batch_69a25e0c14b48190a5c936bab36180b3 completed Feb. 28, 2026, 3:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69a39d05c8e48190842e012378532d40 completed March 1, 2026, 1:57 a.m.
Created at: Feb. 28, 2026, 3:02 a.m.