Triple

T17082147
Position Surface form Disambiguated ID Type / Status
Subject Autovía M-503 E414498 entity
Predicate connectsTo P845 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: [Autovía M-503, connectsTo, Majadahonda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Majadahonda
Context triple: [Autovía M-503, connectsTo, 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_69d886cef44c8190ba56c44b4e863e64 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3dbe408d48190b4f52c2102eae7c2 completed April 18, 2026, 7:30 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0170e153808190b2a253f64da87737 completed May 11, 2026, 6:02 a.m.
Created at: April 10, 2026, 5:34 a.m.