Triple

T29698573
Position Surface form Disambiguated ID Type / Status
Subject Consejo de Ministros E751418 entity
Predicate hasMember P10 FINISHED
Object Minister of the Interior of Uruguay
The Minister of the Interior of Uruguay is the cabinet official responsible for internal security, public order, and oversight of the national police forces within the country.
E1881567 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: Minister of the Interior of Uruguay | Statement: [Consejo de Ministros, hasMember, Minister of the Interior of Uruguay]
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: Minister of the Interior of Uruguay
Triple: [Consejo de Ministros, hasMember, Minister of the Interior of Uruguay]
Generated description
The Minister of the Interior of Uruguay is the cabinet official responsible for internal security, public order, and oversight of the national police forces within the country.

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_69f0d6266f8481909e70bb41cda18587 completed April 28, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f672b201048190b5265e1eec532ec4 completed May 2, 2026, 9:54 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26aa704f048190bcdc734713ab5fcf completed June 8, 2026, 11:41 a.m.
NEDg Description generation batch_6a26b01a27148190aa0135f779819255 completed June 8, 2026, 12:05 p.m.
NED2 Entity disambiguation (via description) batch_6a26b4faf2c881909f77e6c4a8dc665b completed June 8, 2026, 12:26 p.m.
Created at: April 28, 2026, 7:22 p.m.