Triple

T5886076
Position Surface form Disambiguated ID Type / Status
Subject Municipality of La Serena E130864 entity
Predicate locatedIn P40 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: [Municipality of La Serena, locatedIn, La Serena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: La Serena
Context triple: [Municipality of La Serena, locatedIn, 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 populous commune and suburb of Santiago, Chile, known for its residential areas, commercial activity, and historical significance in the Santiago Metropolitan Region.
  • D. Maipú
    Maipú is a renowned wine-producing region in Argentina’s Mendoza Province, noted for its high-quality Malbec and other varietals.
  • E. 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.
  • 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_69c0085628dc8190b334c1b44c067efc completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c03678dd2c819095926f80f25696b0 completed March 22, 2026, 6:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69c1412fcc84819084be2f6210a00fb0 completed March 23, 2026, 1:33 p.m.
Created at: March 22, 2026, 3:57 p.m.