Triple

T35036862
Position Surface form Disambiguated ID Type / Status
Subject José Ballivián Province E1010942 entity
Predicate hasMunicipality P847 FINISHED
Object Santa Rosa Municipality
Santa Rosa Municipality is an administrative subdivision in Bolivia that serves as one of the local government units within José Ballivián Province in the Beni Department.
E2122995 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: Santa Rosa Municipality | Statement: [José Ballivián Province, hasMunicipality, Santa Rosa Municipality]
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: Santa Rosa Municipality
Triple: [José Ballivián Province, hasMunicipality, Santa Rosa Municipality]
Generated description
Santa Rosa Municipality is an administrative subdivision in Bolivia that serves as one of the local government units within José Ballivián Province in the Beni Department.

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_69f76dcea02c81908542a223f6d5059f completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f7858cb6608190a0c2fa79c2d215a6 completed May 3, 2026, 5:27 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37bd2d08ec8190a70b4e3e4af75fd3 completed June 21, 2026, 10:30 a.m.
NEDg Description generation batch_6a37be0aaefc81909c335f1bfb98f9bc completed June 21, 2026, 10:33 a.m.
NED2 Entity disambiguation (via description) batch_6a37be76c740819082de7596c5ec3299 completed June 21, 2026, 10:35 a.m.
Created at: May 3, 2026, 4:01 p.m.