Triple

T25334570
Position Surface form Disambiguated ID Type / Status
Subject Tovar, Mérida, Venezuela E635240 entity
Predicate administrativeDivisionOf P747 FINISHED
Object Mérida state
Mérida state is a mountainous Andean state in western Venezuela known for its cool climate, colonial towns, and proximity to the Sierra Nevada National Park.
E342789 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: Mérida state | Statement: [Tovar, Mérida, Venezuela, administrativeDivisionOf, Mérida state]
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: Mérida state
Triple: [Tovar, Mérida, Venezuela, administrativeDivisionOf, Mérida state]
Generated description
Mérida state is a mountainous Andean state in western Venezuela known for its cool climate, colonial towns, and proximity to the Sierra Nevada National Park.

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_69e75a99bd6481909476115b35b9a8e4 completed April 21, 2026, 11:08 a.m.
NER Named-entity recognition batch_69f497c936b081909de30d0355eca852 completed May 1, 2026, 12:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10ad4bee908190ae8ade61dee1e242 completed May 22, 2026, 7:23 p.m.
NEDg Description generation batch_6a10ae56b8a48190a448e1a4bd938a2b completed May 22, 2026, 7:28 p.m.
NED2 Entity disambiguation (via description) batch_6a10af2b626081908a1a67773654a991 completed May 22, 2026, 7:31 p.m.
Created at: April 21, 2026, 1:32 p.m.