Triple

T31637833
Position Surface form Disambiguated ID Type / Status
Subject Camaná E807361 entity
Predicate locatedInAdministrativeEntity P40 FINISHED
Object Camaná Province
Camaná Province is a coastal administrative division in southern Peru, known for its agricultural valleys, beaches, and the city of Camaná as its capital.
E1996889 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: Camaná Province | Statement: [Camaná, locatedInAdministrativeEntity, Camaná Province]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Camaná Province
Triple: [Camaná, locatedInAdministrativeEntity, Camaná Province]
Generated description
Camaná Province is a coastal administrative division in southern Peru, known for its agricultural valleys, beaches, and the city of Camaná as its capital.

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_69f348d892948190915f8facacb9568c completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6a9188ad88190a84a1aac280d3d3c completed May 3, 2026, 1:47 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2f3b62ea6481908d406e367ec896fd completed June 14, 2026, 11:38 p.m.
NEDg Description generation batch_6a2f3c207d988190835c5bda034bbc6c completed June 14, 2026, 11:41 p.m.
NED2 Entity disambiguation (via description) batch_6a2f3ef33fc08190bdedc81c93429535 completed June 14, 2026, 11:53 p.m.
Created at: April 30, 2026, 10:48 p.m.