Triple

T3870219
Position Surface form Disambiguated ID Type / Status
Subject SCSE E91965 entity
Predicate associatedAirportServes P51581 FINISHED
Object La Serena E503 NE FINISHED

How this triple was built (3 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: [SCSE, associatedAirportServes, La Serena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: La Serena
Context triple: [SCSE, associatedAirportServes, 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 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.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: associatedAirportServes
Context triple: [SCSE, associatedAirportServes, La Serena]
  • A. airportServesAs
    Indicates that an airport functions in a particular role or capacity (such as primary, secondary, or hub) for a specified area, organization, or service.
  • B. airportServed
    Indicates that a particular airport provides service to, or is used for air travel to and from, a given location or area.
  • C. servesAirport
    Indicates that a transportation service or route provides access to and operates for a particular airport.
  • D. associatedWithAirportName
    Indicates a relationship where an entity is linked or connected to a specific airport by its name.
  • E. airportUse
    Indicates that an airport is used or utilized by a particular entity, such as an airline, organization, or service.
  • F. None of above. chosen

Provenance (5 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_69aed9645f348190a9868e7cef56ab7e completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeec533828819080f52dae15fdbecd completed March 9, 2026, 3:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69b6371762c08190ab0b829777e62540 completed March 15, 2026, 4:35 a.m.
PD Predicate disambiguation batch_69aee754dddc8190936e1f9c40a770db completed March 9, 2026, 3:29 p.m.
PDg Predicate description generation batch_69aee80858a481909961a33fb50ff8d1 completed March 9, 2026, 3:32 p.m.
Created at: March 9, 2026, 3:20 p.m.