Triple

T9055545
Position Surface form Disambiguated ID Type / Status
Subject Zapallar E216985 entity
Predicate regionalCapital P3877 FINISHED
Object Valparaíso E14881 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: Valparaíso | Statement: [Zapallar, regionalCapital, Valparaíso]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Valparaíso
Context triple: [Zapallar, regionalCapital, Valparaíso]
  • A. Valparaíso chosen
    Valparaíso is a major Pacific port city in central Chile, renowned for its steep hillsides, colorful houses, historic funiculars, and UNESCO-listed historic quarter.
  • B. Valparaíso
    Valparaíso is a rural municipality located in the Caquetá Department of southern Colombia, known for its Amazonian landscapes and agricultural economy.
  • C. 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.
  • D. Santiago
    Santiago is the capital and primary economic, political, and cultural center of Chile, located in the country’s central valley.
  • E. Santiago
    Santiago is the aging Cuban fisherman and stoic protagonist of Ernest Hemingway’s novella *The Old Man and the Sea*, known for his endurance, dignity, and struggle against a giant marlin.
  • 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_69ca83d4425481909a319dab847724ec completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cc7a7488188190b3dd6bc2f2377503 completed April 1, 2026, 1:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69d02fd2a1e88190bc8d8c2b634399dd completed April 3, 2026, 9:23 p.m.
Created at: March 30, 2026, 7:10 p.m.