Triple

T2627688
Position Surface form Disambiguated ID Type / Status
Subject TivoliVredenburg E59158 entity
Predicate hasPart P35 FINISHED
Object Ronda E217795 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: Ronda | Statement: [TivoliVredenburg, hasPart, Ronda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ronda
Context triple: [TivoliVredenburg, hasPart, Ronda]
  • A. Ronda chosen
    Ronda is a historic mountaintop city in Spain’s Málaga province, famed for its dramatic gorge-spanning bridges, whitewashed old town, and deep cultural ties to bullfighting and Spanish military tradition.
  • B. San Javier
    San Javier is a Chilean town known for its agricultural activity and wine production in the Maule Region.
  • C. Jerez de los Caballeros
    Jerez de los Caballeros is a historic town in the Extremadura region of southwestern Spain, known for its medieval architecture and association with several notable Spanish conquistadors.
  • D. Alhué
    Alhué is a rural commune and town in central Chile known for its agricultural activities and traditional countryside character within the Santiago Metropolitan Region.
  • E. Puntagorda
    Puntagorda is a small rural municipality on the island of La Palma in Spain’s Canary Islands, known for its agricultural landscapes, pine forests, and coastal cliffs.
  • 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_69ab4ac558388190962492cd2e1b0ce6 completed March 6, 2026, 9:44 p.m.
NER Named-entity recognition batch_69abd8b3f72c819085e88f1d74495593 completed March 7, 2026, 7:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69af90a06c4c81908cc4dca65f2190bd completed March 10, 2026, 3:31 a.m.
Created at: March 6, 2026, 9:50 p.m.