Triple

T14070461
Position Surface form Disambiguated ID Type / Status
Subject Lisbon–Porto main line E338592 entity
Predicate majorIntermediateCityServed P112697 FINISHED
Object Tomar E371690 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: Tomar | Statement: [Lisbon–Porto main line, majorIntermediateCityServed, Tomar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tomar
Context triple: [Lisbon–Porto main line, majorIntermediateCityServed, Tomar]
  • A. Tomar chosen
    Tomar is a historic Portuguese city in the Santarém District, best known for its Templar-founded Convent of Christ, a UNESCO World Heritage site.
  • B. Valdemoro
    Valdemoro is a municipality and growing suburban town in central Spain, located south of Madrid.
  • C. Olmedo
    Olmedo is a Spanish-language surname most notably associated with José Joaquín de Olmedo, an important Ecuadorian poet and statesman.
  • D. Olmedo
    Olmedo is a small town in the Gallura region of northern Sardinia, Italy, known for its rural character and traditional Sardinian culture.
  • E. 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.
  • 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_69d81c67ba6c819091935650dfb3b895 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de568d0404819087e0fe37c72162cb completed April 14, 2026, 3 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcb66cfe2c8190af8354316d4f4df9 completed May 7, 2026, 3:57 p.m.
Created at: April 9, 2026, 10:21 p.m.