Triple

T13827562
Position Surface form Disambiguated ID Type / Status
Subject Valladolid E332291 entity
Predicate namedAfter P63 FINISHED
Object Valladolid, Spain E678792 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: Valladolid, Spain | Statement: [Valladolid, namedAfter, Valladolid, Spain]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Valladolid, Spain
Context triple: [Valladolid, namedAfter, Valladolid, Spain]
  • A. Valladolid, Spain chosen
    Valladolid, Spain is a historic city in northwestern Spain that served as a former capital of the Spanish Empire and is known for its rich cultural heritage and architecture.
  • B. Zamora, Spain
    Zamora, Spain is a historic city in the Castile and León region known for its remarkably well-preserved Romanesque architecture and medieval city walls.
  • C. Toledo, Spain
    Toledo, Spain is a historic Spanish city renowned for its medieval architecture, rich cultural heritage, and role as a setting in literature and art.
  • D. Alburquerque, Spain
    Alburquerque, Spain is a historic town in the Extremadura region near the Portuguese border, known for its medieval castle and strategic frontier location.
  • E. Villaverde, Spain
    Villaverde, Spain is an industrial district in Madrid known for its major automotive manufacturing facilities.
  • 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_69d81c5ae7c88190b0dd41bdafeb5999 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de0295d2d48190b08eba0d805bd72d completed April 14, 2026, 9:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7b8ea22c081909cc34f1030a8589b completed May 3, 2026, 9:06 p.m.
Created at: April 9, 2026, 10:13 p.m.