Triple

T15567600
Position Surface form Disambiguated ID Type / Status
Subject Ourém E374155 entity
Predicate locatedNear P294 FINISHED
Object Leiria E245541 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: Leiria | Statement: [Ourém, locatedNear, Leiria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Leiria
Context triple: [Ourém, locatedNear, Leiria]
  • A. Leiria chosen
    Leiria is a historic city in central Portugal known for its medieval hilltop castle and role as a regional administrative and cultural center.
  • B. Alcobaça
    Alcobaça is a historic Portuguese city best known for its UNESCO-listed Cistercian monastery, one of the country’s most important medieval monuments.
  • C. Lousã
    Lousã is a town and municipality in central Portugal known for its surrounding mountains, schist villages, and outdoor activities such as hiking and mountain biking.
  • D. Lourinhã
    Lourinhã is a coastal municipality in western Portugal known for its rich dinosaur fossil discoveries and scenic Atlantic beaches.
  • E. Caldas da Rainha
    Caldas da Rainha is a historic spa and market city in western Portugal, renowned for its thermal baths, ceramics tradition, and proximity to the Atlantic coast.
  • 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_69d85ccd575081908909b71a3f3e3a61 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04dde90b081908284d9258d4462e3 completed April 16, 2026, 2:47 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0025e9b00c81908cb5f305c894363f completed May 10, 2026, 6:30 a.m.
Created at: April 10, 2026, 4:10 a.m.