Triple

T36765422
Position Surface form Disambiguated ID Type / Status
Subject La Vega Province E908323 entity
Predicate hasMunicipalDistrict P78738 FINISHED
Object Río Verde Arriba
Río Verde Arriba is a municipal district in the central Dominican Republic, situated within La Vega Province and characterized by its rural communities and agricultural activities.
E2204535 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: Río Verde Arriba | Statement: [La Vega Province, hasMunicipalDistrict, Río Verde Arriba]
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: Río Verde Arriba
Triple: [La Vega Province, hasMunicipalDistrict, Río Verde Arriba]
Generated description
Río Verde Arriba is a municipal district in the central Dominican Republic, situated within La Vega Province and characterized by its rural communities and agricultural activities.

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_69f76e786ba481909cdcf6cf6b39dd32 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f7c9806eac8190b1268e846f56df73 completed May 3, 2026, 10:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3e1610bdd081909af6c5352fab5068 completed June 26, 2026, 6:02 a.m.
NEDg Description generation batch_6a3e1725b6c88190881d0c7e055e5513 completed June 26, 2026, 6:07 a.m.
NED2 Entity disambiguation (via description) batch_6a3e1dfc96388190812391ec86d40bac completed June 26, 2026, 6:36 a.m.
Created at: May 3, 2026, 4:12 p.m.