Triple

T13365877
Position Surface form Disambiguated ID Type / Status
Subject Honolulu E318935 entity
Predicate hasSisterCity P919 FINISHED
Object La Serena E503 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: La Serena | Statement: [Honolulu, hasSisterCity, La Serena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: La Serena
Context triple: [Honolulu, hasSisterCity, La Serena]
  • A. La Serena chosen
    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.
  • B. Vallenar
    Vallenar is a city in northern Chile known as an agricultural and mining center in the Atacama Desert.
  • C. Maipú
    Maipú is a renowned wine-producing region in Argentina’s Mendoza Province, noted for its high-quality Malbec and other varietals.
  • D. Maipú
    Maipú is a populous commune and suburb of Santiago, Chile, known for its residential areas, commercial activity, and historical significance in the Santiago Metropolitan Region.
  • E. Valparaíso
    Valparaíso is a rural municipality located in the Caquetá Department of southern Colombia, known for its Amazonian landscapes and agricultural economy.
  • 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_69d806b7bbac8190b85278c87fa7aff3 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69da628c71ac81908cfa36342077766e completed April 11, 2026, 3:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7c0d591648190beb4430c6b199eb8 completed May 3, 2026, 9:40 p.m.
Created at: April 9, 2026, 9:32 p.m.