Triple

T15756022
Position Surface form Disambiguated ID Type / Status
Subject Moncloa district E381968 entity
Predicate borderedBy P224 FINISHED
Object Majadahonda E88462 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: Majadahonda | Statement: [Moncloa district, borderedBy, Majadahonda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Majadahonda
Context triple: [Moncloa district, borderedBy, Majadahonda]
  • A. Majadahonda chosen
    Majadahonda is a suburban municipality west of Madrid, Spain, known for its residential character, shopping centers, and sports facilities.
  • B. Ontinyent
    Ontinyent is a historic town in eastern Spain known for its textile industry, traditional festivals, and scenic setting along the Clariano River.
  • C. Madarihat
    Madarihat is a small town in West Bengal, India, known primarily as the main gateway and service hub for visitors to Jaldapara National Park.
  • D. Banegas
    Banegas is a Spanish-language surname borne by various notable individuals in the arts, sports, and public life across Latin America and Spain.
  • E. Otumba
    Otumba is a town in central Mexico historically notable as the site of the Battle of Otumba during the Spanish conquest.
  • 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_69d86d9e6b44819085d1f6a969ecb74c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e050b1ff4881909d5240d1d30f5c8b completed April 16, 2026, 3 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff87714a8481909f8489c73ac89c11 completed May 9, 2026, 7:13 p.m.
Created at: April 10, 2026, 4:47 a.m.