Triple

T11718436
Position Surface form Disambiguated ID Type / Status
Subject Estadio Sausalito E278562 entity
Predicate locatedIn P40 FINISHED
Object Viña del Mar E74624 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: Viña del Mar | Statement: [Estadio Sausalito, locatedIn, Viña del Mar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Viña del Mar
Context triple: [Estadio Sausalito, locatedIn, Viña del Mar]
  • A. Viña del Mar chosen
    Viña del Mar is a major Chilean resort city famed for its beaches, gardens, and annual international song festival.
  • B. Valparaíso
    Valparaíso is a rural municipality located in the Caquetá Department of southern Colombia, known for its Amazonian landscapes and agricultural economy.
  • C. Valparaíso
    Valparaíso is a major Pacific port city in central Chile, renowned for its steep hillsides, colorful houses, historic funiculars, and UNESCO-listed historic quarter.
  • D. La Serena
    La Serena is a coastal city in northern Chile known for its colonial architecture, beaches, and role as a gateway to major astronomical observatories in the region.
  • E. Santiago
    Santiago is a charismatic bohemian performer and friend of Christian in *Moulin Rouge! The Musical*, contributing comic relief, passion, and artistic flair to the story.
  • 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_69d6aaff2ce88190b4a1e4b341ad5377 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a4c26e4c8190ae30d906b4fd4221 completed April 10, 2026, 7:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69f0197d03c08190a5515ffe3cc887ea completed April 28, 2026, 2:20 a.m.
Created at: April 8, 2026, 9:40 p.m.