Triple

T6496887
Position Surface form Disambiguated ID Type / Status
Subject Madrid Río park E148779 entity
Predicate locatedInDistrict P40 FINISHED
Object Carabanchel E611537 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: Carabanchel | Statement: [Madrid Río park, locatedInDistrict, Carabanchel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Carabanchel
Context triple: [Madrid Río park, locatedInDistrict, Carabanchel]
  • A. Carabanchel chosen
    Carabanchel is a district in southwestern Madrid, Spain, known for its residential neighborhoods, historical prison site, and integration into the city's metro network.
  • B. Chamartín
    Chamartín is a district in northern Madrid, Spain, known for its major transport hub and as the home area of Real Madrid’s Santiago Bernabéu Stadium.
  • C. Collado Villalba
    Collado Villalba is a commuter town and municipality in central Spain, located in the Sierra de Guadarrama northwest of Madrid.
  • D. Boadilla del Monte
    Boadilla del Monte is a suburban municipality in the Community of Madrid, Spain, known for its residential areas, green spaces, and proximity to the Spanish capital.
  • E. Fuenlabrada
    Fuenlabrada is a large suburban city in central Spain, located southwest of Madrid and known for its rapid growth, industrial activity, and sizable commuter population.
  • 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_69c687e9ad288190bae5bcac9c8ac855 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c68ace80108190804a835bc646b2b2 completed March 27, 2026, 1:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7127b5b908190af3818df47102079 completed March 27, 2026, 11:27 p.m.
Created at: March 27, 2026, 1:41 p.m.