Triple

T21363243
Position Surface form Disambiguated ID Type / Status
Subject Oeiras E526837 entity
Predicate hasSciencePark P32590 FINISHED
Object Taguspark NE NERFINISHED

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: Taguspark | Statement: [Oeiras, hasSciencePark, Taguspark]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taguspark
Context triple: [Oeiras, hasSciencePark, Taguspark]
  • A. Taguspark chosen
    Taguspark is a major Portuguese science and technology park near Lisbon that hosts research institutions, universities, and innovative companies in fields such as engineering and information technology.
  • B. Parque Europa
    Parque Europa is a themed park in Torrejón de Ardoz, Spain, featuring replicas of famous European monuments and landscaped recreational areas.
  • C. Madrid Río park
    Madrid Río park is a large urban green space in Madrid that features recreational areas, cultural facilities, and landscaped promenades along the banks of the Manzanares River.
  • D. Parque España
    Parque España is a popular urban park in Mexico City’s Condesa neighborhood, known for its leafy paths, Art Deco features, and vibrant community atmosphere.
  • E. Parque Grande
    Parque Grande is a large historic urban park in Zaragoza, Spain, known for its landscaped gardens, monuments, and recreational areas.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b51d8a308190b09113b3b3f9bc15 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e8b06cbcb481909ec9014da3b0a18a completed April 22, 2026, 11:26 a.m.
Created at: April 16, 2026, 5:08 p.m.