Triple

T24039974
Position Surface form Disambiguated ID Type / Status
Subject Legislature of Entre Ríos E595343 entity
Predicate hasPart P35 FINISHED
Object Senate of Entre Ríos
The Senate of Entre Ríos is the upper chamber of the provincial legislature in Entre Ríos, Argentina, responsible for debating and passing regional laws and overseeing the executive branch.
E1616037 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: Senate of Entre Ríos | Statement: [Legislature of Entre Ríos, hasPart, Senate of Entre Ríos]
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: Senate of Entre Ríos
Triple: [Legislature of Entre Ríos, hasPart, Senate of Entre Ríos]
Generated description
The Senate of Entre Ríos is the upper chamber of the provincial legislature in Entre Ríos, Argentina, responsible for debating and passing regional laws and overseeing the executive branch.

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_69e288c06a908190899cad4531f32c9a completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1d8d7a51c8190b108a4f862843209 completed April 29, 2026, 10:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f965357f4819099e5f593f90d8f11 completed May 21, 2026, 11:33 p.m.
NEDg Description generation batch_6a0f973823ac819092f241755fe86bf2 completed May 21, 2026, 11:37 p.m.
NED2 Entity disambiguation (via description) batch_6a0f9817d9248190aa2f7cc8fc2916bf completed May 21, 2026, 11:41 p.m.
Created at: April 17, 2026, 9:57 p.m.