Triple

T1841676
Position Surface form Disambiguated ID Type / Status
Subject Tours E41188 entity
Predicate twinnedWith P1072 FINISHED
Object Segovia E151896 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: Segovia | Statement: [Tours, twinnedWith, Segovia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Segovia
Context triple: [Tours, twinnedWith, Segovia]
  • A. Segovia chosen
    Segovia is a historic Spanish city in the region of Castile and León, renowned for its Roman aqueduct, medieval architecture, and well-preserved old town.
  • B. Ávila
    Ávila is a historic walled city in central Spain, renowned for its remarkably well-preserved medieval fortifications and Romanesque and Gothic architecture.
  • C. Badajoz
    Badajoz is a historic city in western Spain near the Portuguese border, known for its medieval fortress and role as a strategic frontier stronghold.
  • 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. Valladolid
    Valladolid is a historic city in northwestern Spain that served as a major political and cultural center, including as a former capital of the Spanish monarchy.
  • 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_69a88647f9388190909bc36e795bdaec completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb03e7a7481909c5b902034390ef1 completed March 7, 2026, 4:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc6200bd08190bd1d9fd3046fca00 completed March 10, 2026, 7:20 a.m.
Created at: March 4, 2026, 7:33 p.m.