Triple

T4632434
Position Surface form Disambiguated ID Type / Status
Subject Ivorypress E101447 entity
Predicate headquartersLocation P62 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: [Ivorypress, headquartersLocation, Madrid]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Madrid
Context triple: [Ivorypress, headquartersLocation, Madrid]
  • A. Madrid
    Madrid is a municipality in the Cundinamarca department of Colombia, located near Bogotá and known for its floriculture and agricultural production.
  • B. 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.
  • C. Madri
    Madri is a princess from the Mahabharata epic, known as the second wife of King Pandu and the mother of the twins Nakula and Sahadeva.
  • D. Seville
    Seville is a historic Spanish city in Andalusia renowned for its rich Moorish and Christian heritage, iconic landmarks like the Giralda and Alcázar, and vibrant cultural traditions such as flamenco.
  • E. Madrid metropolitan area
    The Madrid metropolitan area is the large urban and economic region centered on Spain’s capital city, encompassing Madrid and its surrounding municipalities and suburbs.
  • 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_69bd43d2f1c081908cd4b7ec48ecc73d completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd5a342ffc8190a911d0598ed230bb completed March 20, 2026, 2:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdfa3f792881908f7d0bc1f09d517e completed March 21, 2026, 1:54 a.m.
Created at: March 20, 2026, 1:13 p.m.