Triple

T1401839
Position Surface form Disambiguated ID Type / Status
Subject University of Salamanca E31601 entity
Predicate locatedIn P40 FINISHED
Object Salamanca E55297 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: Salamanca | Statement: [University of Salamanca, locatedIn, Salamanca]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Salamanca
Context triple: [University of Salamanca, locatedIn, Salamanca]
  • A. Salamanca
    Salamanca is a Chilean town and municipality in the Coquimbo Region, known for its agricultural production and location in the Choapa Valley.
  • B. Salamanca chosen
    Salamanca is a historic city in western Spain renowned for its ancient university, golden sandstone architecture, and well-preserved medieval old town.
  • C. Alcalá de Henares
    Alcalá de Henares is a historic Spanish city east of Madrid, renowned as the birthplace of Miguel de Cervantes and for its well-preserved university and medieval architecture.
  • D. Burgos
    Burgos is a historic city in northern Spain known for its medieval architecture and its prominent role during the Spanish Civil War.
  • E. Ávila
    Ávila is a historic walled city in central Spain, renowned for its remarkably well-preserved medieval fortifications and Romanesque and Gothic architecture.
  • 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_69a49918e1f88190ba610f9dc8114578 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c39da20c8190b9c82cc4ad747c66 completed March 1, 2026, 10:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69ada0ba8f608190a5f1fcc5ebcee9e5 completed March 8, 2026, 4:15 p.m.
Created at: March 1, 2026, 7:59 p.m.